Research ArticleClinical Studies
Open Access

The Impact of Metabolic Comorbidities (Diabetes, Hypertension) on the Severity and Progression of Oropharyngeal Infections

FLORIAN CIPRIAN VENTER, TIMEA CLAUDIA GHITEA, ADRIAN NICOLAE VENTER, AMIN-FLORIN EL-KHAROUBI, MOUSA EL-KHAROUBI, EVELIN CLAUDIA GHITEA, MARC CRISTIAN GHITEA and AMINA VENTER
In Vivo January 2026, 40 (1) 663-676; DOI: https://doi.org/10.21873/invivo.14228

Abstract

Background/Aim: Metabolic comorbidities, such as diabetes mellitus (DM) and hypertension (HTN), can influence the severity and progression of oropharyngeal infections. This study investigated the impact of these conditions on inflammatory and metabolic markers in patients with severe infections.

Patients and Methods: This retrospective study included patients diagnosed with peritonsillar phlegmon, laterocervical/submandibular abscess, and peritonsillar abscess. Metabolic [glycemia, body mass index (BMI)] and inflammatory [erythrocyte sedimentation rate (ESR), C-reactive protein (CRP)] parameters were analyzed based on the presence of comorbidities. Linear regression was employed to identify significant associations between variables.

Results: Severe oropharyngeal infections were significantly associated with elevated glycemia (R2=0.961, p<0.001) and CRP levels (R2=0.983, p<0.001), indicating an exacerbated inflammatory and metabolic response. Hypertension was correlated with glycemia (p=0.036) but not with CRP (p=0.684). The presence of metabolic comorbidities did not significantly influence glycemia within the study group.

Conclusion: Patients with severe oropharyngeal infections exhibit significantly elevated glycemia and CRP, emphasizing the need for rigorous monitoring of these parameters. Hypertension was associated with glycemic alterations, suggesting a metabolic interplay. These findings underscore the importance of a personalized approach in managing oropharyngeal infections in patients with metabolic comorbidities.

Keywords:

Introduction

Oropharyngeal infections represent a common health concern, with a wide spectrum of severity ranging from mild to severe complications requiring emergency medical intervention (1). Several factors influence the progression of these infections, including immunological status, associated comorbidities, and individual inflammatory responses (2). In this context, metabolic comorbidities such as diabetes mellitus (DM) and hypertension (HTN) play a crucial role in determining the severity and prognosis of these infections (3).

Diabetes mellitus is recognized as a predisposing factor for severe infections due to its impact on immune function and increased susceptibility to bacterial infections (4). Chronic hyperglycemia impairs chemotaxis, phagocytosis, and the ability of immune cells to control the spread of infection (5). Additionally, DM is associated with a systemic inflammatory state, which may contribute to a more severe progression of oropharyngeal infections (6).

Hypertension frequently coexists with DM and is also a risk factor for chronic inflammation and endothelial dysfunction (7). Studies suggest that hypertensive patients may exhibit an altered immune response, potentially leading to a more aggressive course of infections (8). Furthermore, HTN is often associated with other comorbidities, such as obesity and cardiovascular disease, which may negatively impact the body’s response to severe infections (9).

Metabolic syndrome, characterized by the simultaneous presence of abdominal obesity, insulin resistance, dyslipidemia, and hypertension, is a significant risk factor for chronic inflammation and immune dysfunction (10). Patients with metabolic syndrome exhibit a persistent proinflammatory state, which may contribute to an increased susceptibility to severe infections, including oropharyngeal infections (11). The metabolic disturbances associated with this syndrome impair the immune response, reducing the body’s ability to control pathogen spread and prolonging both local and systemic inflammation (12-16).

This study aimed to evaluate the impact of DM and HTN on the severity and progression of oropharyngeal infections by analyzing correlations between these comorbidities and inflammatory parameters, length of hospitalization, and the need for complex treatments. The objective was to identify significant differences in infection severity and therapeutic strategies applied to patients with DM and HTN compared to those without these conditions. The main hypothesis of the study was that patients with DM and HTN experience more severe infections, require more intensive treatments, and have a higher risk of prolonged hospitalization. The findings of this study may contribute to optimizing prevention and management strategies for patients at high risk of infectious complications.

Patients and Methods

Study design and patient selection. This retrospective observational analysis was conducted among a cohort involving patients hospitalized due to peritonsillar and other cervical infections within a specialized medical facility. Data collected from patient charts included clinical, biological, alongside imaging variables relevant to assessing oral status and infection-related severity.

Inclusion and exclusion criteria. A total of 108 patients were categorized into three groups, based on their diagnosed infection type: Group I: Patients diagnosed with peritonsillar phlegmon, a diffuse infection involving soft tissue around the tonsillar region, characterized by substantial inflammation with heightened risk of complications; Group II: Patients presenting with laterocervical/submandibular abscess, a deep-seated infection affecting cervical or submandibular spaces, posing potential risk of spreading to critical anatomical structures; Group III: Patients developing a peritonsillar abscess, an accumulation of pus between the tonsillar capsule and surrounding soft tissues, being one of the most severe consequences of untreated tonsillar infections.

Inclusion criteria: Patients diagnosed with clinically and imaging-supported peritonsillar or cervical infections. Patients having complete medical histories including prior dental treatments; Patients aged 18 years or older.

Exclusion criteria: Patients suffering from infections not related to dental etiology; cases with inadequate medical files or missing dental background; patients severely immunocompromised or those with terminal oncological diseases.

Data collection and parameters analyzed. For each participant, the following details were documented and examined. Demographics: Age, biological sex, and residential area; Dental health: Presence of compromised dentition, dental replacements, or prosthetic applications; Comorbidities: Diabetes mellitus, hypertension, chronic cardiac disease, hepatic disorders; Inflammatory biomarkers: Leukocyte count, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR); Surgical requirements: Incision for drainage or other operative methods; Hospital stay duration: Total hospitalization days; Administered therapies: Antibiotics, corticosteroids, and pain management.

Statistical analysis. Collected data were processed using advanced statistical software (SPSS, version 20, IBM, Armonk, NY, USA). Inter-group comparisons were conducted through Student’s t-test for continuous parameters and Chi-square analysis for categorical factors. Associations between dental condition and infection severity were analyzed via logistic regression models, adjusting for confounding influences. A p-value of less than 0.05 was set as the statistical significance threshold (17).

Ethical considerations. This study received official approval from the Ethics Board of the medical institution (Approval No. 9410/08.04.2021). All patient-related data were fully anonymized to ensure privacy protection and compliance with ethical research guidelines.

Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.

Results

Demographic characteristics. The distribution of patients included in the study indicates a mean age of 36.96 years, with a standard deviation of 17.52 years, highlighting a notable variability within the patient group. This finding suggests that both younger and older individuals are affected by severe oropharyngeal infections, reflecting the broad age range of impacted patients (Figure 1A).

Figure 1.
Figure 1.

Demographic distribution of patients according to age, sex, and environment.

Regarding sex distribution, the majority of patients were male (57.4%), while female patients accounted for 42.6% of the study population. This difference may indicate a higher predisposition among men to develop severe oropharyngeal infections or a potential delay in seeking medical care, leading to a greater representation of male patients in severe cases.

Analysis of geographical distribution revealed that 51.9% of patients originated from rural areas, while 48.1% were from urban environments. This relatively balanced proportion suggests that severe oropharyngeal infections are not confined to a specific setting. However, the slightly higher prevalence in rural areas may be associated with reduced access to preventive healthcare or limited availability of routine dental treatments (Figure 1B).

These demographic data offer a comprehensive overview of the study population, highlighting potential risk factors related to age, sex, and living environment, which may contribute to the severity and progression of oropharyngeal infections.

Comparison of blood glucose, BMI, ESR, CRP, and leukocyte values between patients with and without comorbidities. The analysis of clinical and metabolic parameters shows significant differences across comorbidities, suggesting their impact on inflammation severity and systemic response to oropharyngeal infections.

Blood glucose and BMI. Patients with diabetes had significantly higher blood glucose (129.63 mg/dl) than non-diabetics (107.19 mg/dl), as expected. Elevated glucose levels were also observed in patients with HTN and congestive heart failure (CHF), linking metabolic imbalance to cardiovascular diseases. A patient afflicted by stroke showed extreme hyperglycemia (284 mg/dl), indicating severe metabolic decompensation. BMI was similar across groups (27.27–28.86 kg/m2), with most patients being overweight.

Inflammatory markers (ESR and CRP). ESR was higher in patients with DM (45.0 mm/h) and CHF (47.3 mm/h), reflecting chronic inflammation, while patients with HTN had lower ESR (34.7 mm/h), suggesting a different inflammatory response. CRP was elevated in DM (117.15 mg/dl) and HTN (123.40 mg/dl), supporting the link between metabolic and cardiovascular diseases and systemic inflammation. Patients with CHF and liver disease also showed high CRP, while a stroke patient had an extreme CRP level (300.20 mg/dl), indicating severe inflammation, likely due to systemic infection or vascular complications.

Liver disease and inflammation. Patients with liver disease had higher ESR (49.9 mm/h), reflecting chronic inflammation, though CRP levels showed no significant increase, possibly due to disease variability.

Overall, these findings confirm a strong correlation between metabolic comorbidities and systemic inflammation. Patients with DM, HTN, and CHF exhibited higher inflammatory markers, reinforcing their susceptibility to severe infections and the need for close monitoring and aggressive treatment (Table I).

View this table:
Table I.

Metabolic and inflammatory parameters according to comorbidities in patients with oropharyngeal infections.

The findings show a strong link between metabolic comorbidities and systemic inflammation. Patients with DM, HTN, and CHF had higher inflammatory markers, supporting their role in infection severity and the need for closer monitoring.

Graphs illustrate blood glucose, BMI, ESR, and CRP distribution across comorbidities, with 95% confidence intervals highlighting variability. Patients with DM had significantly higher glucose, ESR, and CRP, indicating heightened inflammation and infection risk.

Patients with HTN showed elevated glucose and CRP, suggesting persistent inflammation, while slightly increased BMI may reflect a link to obesity. Patients with CHF had increased glucose, ESR, and CRP, reinforcing the association between heart failure and chronic inflammation.

In acute myocardial infarction (AMI), CRP levels varied, indicating a distinct inflammatory response, while elevated glucose highlighted metabolic imbalances. Stroke patients had the highest glucose and CRP, suggesting a strong proinflammatory state affecting recovery. Patients with liver disease showed increased ESR and CRP, indicating chronic inflammation linked to liver dysfunction. Overall, metabolic and cardiovascular conditions amplify inflammation, worsening oropharyngeal infection severity and progression (Figure 2).

Figure 2.
Figure 2.

95% confidence intervals for metabolic and inflammatory parameters in patients with diabetes mellitus (DM) (A), hypertension (HTN) (B), chronic heart failure (CHF) (C), acute myocardial infarction (AMI) (D), stroke (SCI) (E), and liver diseases (F).

Comparison of blood glucose, BMI, ESR, CRP, and leukocyte values between patients with and without comorbidities across research groups. The analysis of metabolic and inflammatory parameters shows significant variations across patient groups and comorbidities, highlighting their impact on inflammation and infection severity. The distribution of metabolic and inflammatory parameters according to comorbidities and patient groups is presented in Table II.

View this table:
Table II.

Distribution of metabolic and inflammatory parameters according to comorbidities and patient groups.

In diabetes (DM), Group III had the highest blood glucose (143.50 mg/dl), while BMI was higher in Group II (31.63 kg/m2), reinforcing the diabetes-obesity link. CRP was highest in Group II (144.11 mg/dl), indicating strong systemic inflammation. For HTN, blood glucose increased in Group III (139.11 mg/dl), while CRP was also highest (135.68 mg/dl), suggesting progressive metabolic impairment and inflammation. Patients with CHF in Group III showed elevated blood glucose (139.00 mg/dl) and CRP (143.46 mg/dl), reflecting poor metabolic control and chronic inflammation. In AMI, Group III had significantly higher blood glucose (152.33 mg/dl), ESR (60.3 mm/h), and CRP (105.97 mg/dl), indicating severe post-infarction inflammation. Patients afflicted by stroke (SCI) in Group III exhibited extreme blood glucose (284.00 mg/dl) and CRP (300.20 mg/dl), reflecting severe systemic inflammation and metabolic decompensation. For liver disease, Group III had high blood glucose (144.25 mg/dl) and CRP (123.94 mg/dl), suggesting chronic inflammation linked to liver dysfunction. These, underscore the strong link between metabolic comorbidities and systemic inflammation, emphasizing the need for close monitoring and proactive management.

Figure 3 shows average blood glucose levels across comorbidities and patient groups. Patients with DM had significantly higher glucose, especially in Group III, suggesting poor control or severe disease. Patients with HTN, CHF, or AMI also showed elevated levels, linking these conditions to metabolic imbalances. Patients with stroke in Group III had the highest values, indicating severe decompensation, while patients with liver disease also exhibited increased glucose, likely due to impaired metabolism. The figure underscores the correlation between metabolic comorbidities and hyperglycemia, highlighting the need for strict glucose monitoring in cardiovascular, liver, and neurological diseases.

Figure 3.
Figure 3.

Comparison of mean blood glucose levels in patients with metabolic and cardiovascular comorbidities.

Figure 4 shows the mean BMI across different comorbidities and patient groups. Patients with DM in Group II had the highest BMI, reinforcing the diabetes-obesity link. BMI remained stable in patients with HTN or CHF but is higher in Group III, indicating obesity’s impact on cardiac function. Patients with AMI showed slightly lower BMI, possibly due to disease-related weight loss. Patients with stroke or liver disease in Group III had notably high BMI, linking obesity to these conditions. The figure highlights BMI as a key risk factor in metabolic and cardiovascular diseases, emphasizing the need for weight management

Figure 4.
Figure 4.

Mean BMI across different comorbidities and patient groups.

Figure 5 shows mean BMI across comorbidities. Patients with DM in Group II had the highest BMI, reinforcing the diabetes-obesity link. BMI remained stable in patients with HTN or CHF but rose in Group III, suggesting obesity’s impact on cardiac function. Patients with AMI showed slightly lower BMI, possibly due to disease-related weight loss. Patients with stroke or liver disease in Group III had high BMI, linking obesity to these conditions. The figure highlights BMI as a key risk factor in metabolic and cardiovascular diseases, emphasizing the need for weight management.

Figure 5.
Figure 5.

Mean erythrocyte sedimentation rate (ESR) across different comorbidities and patient groups.

Figure 6 shows mean CRP levels (mg/dl) across comorbidities, highlighting systemic inflammation and infection severity. In patients with DM, CRP rose from Group I to III, indicating worsening inflammation. Patients with HTN or CHF showed high CRP in Group III, linking them to chronic inflammation and vascular damage. Patients with AMI in Group III had significantly elevated CRP, reflecting acute inflammation post-infarction. Patients with stroke showed the highest CRP levels, exceeding 300 mg/dl, suggesting severe systemic inflammation. Patients with liver disease in Group III also exhibited high CRP, associated with chronic liver damage. The figure underscores the link between metabolic comorbidities and systemic inflammation, emphasizing its role in disease progression and infection risk.

Figure 6.
Figure 6.

Mean C-reactive protein (CRP) levels across different comorbidities and patient groups.

Correlations. Pearson correlation analysis revealed significant associations between blood glucose, CRP, and metabolic comorbidities, presented in Table III and Figure 7. A notable finding is the positive correlation between blood glucose and HTN (r=0.202, p=0.036), suggesting that hypertensive patients tend to have higher blood glucose levels, potentially indicating an increased risk of metabolic dysfunction or undiagnosed diabetes.

View this table:
Table III.

Pearson correlations between metabolic and inflammatory parameters and associated comorbidities.

Figure 7.
Figure 7.

Distribution of blood glucose and inflammation C-reactive protein (CRP) according to comorbidities – Analysis of significant correlations between glucose and hypertension (HTN) (A), stroke (SCI) (B), and CPR and SCI (C).

Stroke was associated with the highest blood glucose values, showing a strong and significant correlation (r=0.471, p<0.001). This suggests that patients with stroke often experience poor glycemic control, either due to pre-existing diabetes or stress-induced hyperglycemia, a common occurrence in acute cerebrovascular events.

Regarding systemic inflammation, CRP was significantly correlated with stroke (r=0.231, p=0.016), indicating elevated inflammatory status in patients with stroke. This supports the role of inflammation in stroke pathogenesis and progression, reinforcing CRP as a potential prognostic marker. These results emphasize the need for strict monitoring of blood glucose and inflammation in patients with HTN and stroke to prevent associated metabolic and cardiovascular complications.

Linear regression. To evaluate the relationship between metabolic parameters and comorbidities, regression analyses were conducted for blood glucose, CRP, and stroke based on previously identified significant correlations (Table IV). The results indicate that stroke is a strong predictor of both hyperglycemia and systemic inflammation, with high coefficients of determination (R2) suggesting a significant impact of stroke on these biomarkers.

View this table:
Table IV.

Regression results between metabolic parameters and stroke (SCI).

For blood glucose, the model showed R2=0.961, meaning that 96.1% of blood glucose variability is explained by the presence of stroke. The regression coefficient for stroke was 84.4 mg/dl (p<0.001), indicating that patients with stroke have an average blood glucose level 84.4 mg/dl higher than those without this condition. This confirms a strong association between stroke and hyperglycemia, likely due to metabolic stress or pre-existing diabetes.

Regarding CRP, the model yielded R2=0.983, meaning that 98.3% of CRP variability is attributed to stroke. The coefficient for stroke was 190.4 mg/dl (p<0.001), demonstrating that patients with stroke have significantly higher CRP levels, averaging an increase of 190.4 mg/dl. This finding supports a strong link between stroke and systemic inflammation, confirming that SCI with stroke exhibit an exaggerated inflammatory response.

Regression models for blood glucose vs. HTN (p=0.909) and CRP vs. HTN (p=0.684) were not statistically significant, indicating that HTN does not independently influence glycemia or inflammation (CRP) in this patient cohort.

These results establish stroke as a major predictor of both hyperglycemia and systemic inflammation, reinforcing the need for strict monitoring of blood glucose and inflammatory markers in patients with stroke to prevent metabolic and inflammatory complications.

Discussion

Our results revealed a significant association between elevated blood glucose levels and the presence of stroke, with an R2 coefficient of 0.961, indicating strong predictive power. This finding aligns with existing literature, which demonstrates that hyperglycemia is frequently linked to an increased risk of stroke and poor prognosis (18, 19). Previous studies have shown that hyperglycemia worsens cerebral ischemia through mechanisms such as endothelial dysfunction, oxidative stress, and inflammation (20). Additionally, stress-induced hyperglycemia, even in non-diabetic patients, is considered a negative predictor of post-stroke recovery, reinforcing the need for strict blood glucose monitoring in patients with stroke (21).

Our analysis also identified a strong correlation between CRP levels and stroke (R2=0.983, p<0.001), suggesting heightened systemic inflammation in patients with stroke. The literature supports CRP as a key inflammatory marker associated with stroke severity, commonly used as a predictor of mortality and recurrence risk (22). Recent studies suggest that elevated CRP levels before stroke may indicate an underlying proinflammatory state, which contributes to endothelial dysfunction and thrombosis (23). Furthermore, CRP has been recognized as an independent biomarker of ischemic lesion extension, reinforcing our findings and highlighting the importance of monitoring inflammation in patients with stroke (24-26).

Additionally, our study found a significant correlation between HTN and higher blood glucose levels (p=0.036), suggesting a potential link between the two conditions. The literature supports this association, emphasizing that hypertension and hyperglycemia frequently coexist in metabolic syndrome, sharing common pathophysiological mechanisms such as insulin resistance and endothelial dysfunction (27-30). Moreover, patients with HTN have an increased risk of developing type 2 diabetes, justifying our findings and reinforcing the need for routine glycemic screening in hypertensive patients (31).

In contrast, the regression analysis between CRP and HTN did not yield statistically significant association (p =0.684), indicating that systemic inflammation, as measured by CRP, is not directly influenced by HTN in this cohort. However, previous studies present conflicting results—some indicate that hypertensive patients have elevated CRP levels, suggesting chronic vascular inflammation, while others have not found a clear association (32-35). These discrepancies may stem from methodological differences or confounding factors, such as obesity and dyslipidemia, which can influence CRP levels independently of hypertension.

A comparison of our findings with the literature confirms that stroke is a significant predictor of both elevated blood glucose and systemic inflammation. Additionally, HTN was correlated with hyperglycemia, reinforcing existing evidence of their interrelation (36). However, no significant association was observed between CRP and HTN, suggesting that systemic inflammation is more closely linked to stroke than to hypertension in this context. These results highlight the importance of monitoring blood glucose and inflammation in patients with stroke and HTN to prevent severe complications (37).

Limitations and future perspectives. First, its retrospective design limits the ability to establish clear causal relationships between metabolic parameters and the severity of oropharyngeal infections. Additionally, the sample size may influence the robustness of statistical outcomes, and unequal patient distribution among study groups could introduce bias in interpretation.

Another limitation is the lack of consideration for other potential confounding factors, such as previous treatments, nutritional status, or genetic predisposition, which may influence the inflammatory response and infection progression. Moreover, CRP and other inflammatory markers were measured at a single time point, preventing an assessment of inflammation dynamics over time.

For future research, prospective studies with long-term patient monitoring are needed to better determine the impact of metabolic comorbidities on the course of oropharyngeal infections. Including additional biomarkers of inflammation and oxidative stress could provide a more comprehensive understanding of the underlying pathological mechanisms. Furthermore, multicenter studies with larger patient samples would enhance the generalizability of the findings and help in identifying optimized treatment strategies for patients at high risk of infectious complications.

Conclusion

The study identified a significant association between diabetes, hypertension, and the severity of oropharyngeal infections. Patients with stroke exhibited markedly higher blood glucose and CRP levels, indicating a heightened inflammatory response.

Regression analysis confirmed that stroke is a key predictor of both hyperglycemia and systemic inflammation, emphasizing the need for strict monitoring in these patients. In contrast, blood glucose levels in the study group were not significantly affected, suggesting a greater impact of individual comorbidities.

These findings highlight the importance of a personalized approach in managing oropharyngeal infections, focusing on blood glucose control and inflammation monitoring. Future research should explore underlying mechanisms and optimal therapeutic strategies to improve patient outcomes.

Acknowledgements

The Authors would like to thank the University of Oradea, for supporting the publication fee for this paper, through an internal project.

Footnotes

  • Authors’ Contributions

    Conceptualization: F.C.V. and A.V.; methodology: T.C.G.; software: A.N.V.; validation: A.F.E., M.E. and E.C.G.; formal analysis: M.C.G.; investigation: T.C.G.; resources: T.C.G.; data curation: T.C.G.; writing—original draft preparation: T.C.G.; writing—review and editing: T.C.G.; visualization: T.C.G.; supervision: T.C.G.; project administration: T.C.G.; funding acquisition: T.C.G. All Authors have read and agreed to the published version of the manuscript.

  • Conflicts of Interest

    The Authors declare no conflicts of interest in relation to this study.

  • Received February 13, 2025.
  • Revision received March 13, 2025.
  • Accepted March 14, 2025.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).

References

  1. ↵
    1. World Health Organization
    : Infection prevention and control of epidemic-and pandemic-prone acute respiratory infections in health care. World Health Organization, 2014.
  2. ↵
    1. Jesenak M,
    2. Brndiarova M,
    3. Urbancikova I,
    4. Rennerova Z,
    5. Vojtkova J,
    6. Bobcakova A,
    7. Ostro R,
    8. Banovcin P
    : Immune parameters and COVID-19 infection - associations with clinical severity and disease prognosis. Front Cell Infect Microbiol 10: 364, 2020. DOI: 10.3389/fcimb.2020.00364
    OpenUrlCrossRef
  3. ↵
    1. Gajjala PR,
    2. Sanati M,
    3. Jankowski J
    : Cellular and molecular mechanisms of chronic kidney disease with diabetes mellitus and cardiovascular diseases as its comorbidities. Front Immunol 6: 340, 2015. DOI: 10.3389/fimmu.2015.00340
    OpenUrlCrossRefPubMed
  4. ↵
    1. Casqueiro J,
    2. Casqueiro J,
    3. Alves C
    : Infections in patients with diabetes mellitus: A review of pathogenesis. Indian J Endocrinol Metab 16 Suppl 1(Suppl1): S27-S36, 2012. DOI: 10.4103/2230-8210.94253
    OpenUrlCrossRefPubMed
  5. ↵
    1. Lee H,
    2. Kim MJ,
    3. Lee IK,
    4. Hong CW,
    5. Jeon JH
    : Impact of hyperglycemia on immune cell function: a comprehensive review. Diabetol Int 15(4): 745-760, 2024. DOI: 10.1007/s13340-024-00741-6
    OpenUrlCrossRefPubMed
  6. ↵
    1. Murrah VA
    : Diabetes mellitus and associated oral manifestations: a review. J Oral Pathol Med 14(4): 271-281, 1985. DOI: 10.1111/j.1600-0714.1985.tb00494.x
    OpenUrlCrossRef
  7. ↵
    1. García RD,
    2. Asensio JA,
    3. Perdicaro DJ,
    4. de Los Ángeles Peral M
    : The role of inflammation as a preponderant risk factor in cardiovascular diseases. Curr Vasc Pharmacol 20(3): 244-259, 2022. DOI: 10.2174/1570161120666220201160038
    OpenUrlCrossRefPubMed
  8. ↵
    1. Su S,
    2. Chen R,
    3. Zhang S,
    4. Shu H,
    5. Luo J
    : Immune system changes in those with hypertension when infected with SARS-CoV-2. Cell Immunol 378: 104562, 2022. DOI: 10.1016/j.cellimm.2022.104562
    OpenUrlCrossRefPubMed
  9. ↵
    1. Pugliese G,
    2. Liccardi A,
    3. Graziadio C,
    4. Barrea L,
    5. Muscogiuri G,
    6. Colao A
    : Obesity and infectious diseases: pathophysiology and epidemiology of a double pandemic condition. Int J Obesity 46(3): 449-465, 2022. DOI: 10.1038/s41366-021-01035-6
    OpenUrlCrossRef
  10. ↵
    1. Said S,
    2. Mukherjee D,
    3. Whayne TF
    : Interrelationships with metabolic syndrome, obesity and cardiovascular risk. Curr Vasc Pharmacol 14(5): 415-425, 2016. DOI: 10.2174/1570161114666160722121615
    OpenUrlCrossRefPubMed
  11. ↵
    1. Martinac M,
    2. Pehar D,
    3. Karlović D,
    4. Babić D,
    5. Marčinko D,
    6. Jakovljević M
    : Metabolic syndrome, activity of the hypothalamic-pituitary-adrenal axis and inflammatory mediators in depressive disorder. Acta Clin Croat 53(1): 55-70, 2014.
    OpenUrlPubMed
  12. ↵
    1. Marruganti C,
    2. Suvan JE,
    3. D’Aiuto F
    : Periodontitis and metabolic diseases (diabetes and obesity): Tackling multimorbidity. Periodontol 2000, 2023. DOI: 10.1111/prd.12536
    OpenUrlCrossRef
    1. Ghitea TC
    : Correlation of periodontal bacteria with chronic inflammation present in patients with metabolic syndrome. Biomedicines 9(11): 1709, 2021. DOI: 10.3390/biomedicines9111709
    OpenUrlCrossRefPubMed
    1. Potra Cicalău GI,
    2. Marcu OA,
    3. Ghitea TC,
    4. Ciavoi G,
    5. Iurcov RC,
    6. Beiusanu C,
    7. Trifan DF,
    8. Vicaș LG,
    9. Ganea M
    : Study of periodontal bacteria in diabetic wistar rats: assessing the anti-inflammatory effects of carvacrol and magnolol hydrogels. Biomedicines 12(7): 1445, 2024. DOI: 10.3390/biomedicines12071445
    OpenUrlCrossRefPubMed
    1. Potra Cicalău GI,
    2. Vicaș LG,
    3. Ciavoi G,
    4. Ghitea TC,
    5. Csaba N,
    6. Cristea RA,
    7. Miere Groza F,
    8. Ganea M
    : A natural approach to the prevention and treatment of gingivitis and periodontitis: a review of pomegranate’s bioactive properties. Life (Basel) 14(10): 1298, 2024. DOI: 10.3390/life14101298
    OpenUrlCrossRefPubMed
  16. ↵
    1. Rat LA,
    2. Moldovan AF,
    3. Trifan DF,
    4. Matiș L,
    5. Murvai GF,
    6. Maris L,
    7. Ghitea TC,
    8. Maghiar MA
    : Can the correlation of periodontopathies with gastrointestinal diseases be used as indicators in severe colorectal diseases? Biomedicines 11(2): 402, 2023. DOI: 10.3390/biomedicines11020402
    OpenUrlCrossRefPubMed
  17. ↵
    1. George D,
    2. Mallery P
    : IBM SPSS statistics 27 step by step: A simple guide and reference. Routledge, 2021.
  18. ↵
    1. Kruyt ND,
    2. Biessels GJ,
    3. DeVries JH,
    4. Roos YB
    : Hyperglycemia in acute ischemic stroke: pathophysiology and clinical management. Nat Rev Neurol 6(3): 145-155, 2010. DOI: 10.1038/nrneurol.2009.231
    OpenUrlCrossRefPubMed
  19. ↵
    1. Luitse MJ,
    2. Velthuis BK,
    3. Kappelle LJ,
    4. van der Graaf Y,
    5. Biessels GJ, DUST Study Group
    : Chronic hyperglycemia is related to poor functional outcome after acute ischemic stroke. Int J Stroke 12(2): 180-186, 2017. DOI: 10.1177/1747493016676619
    OpenUrlCrossRefPubMed
  20. ↵
    1. Arcambal A,
    2. Taïlé J,
    3. Rondeau P,
    4. Viranaïcken W,
    5. Meilhac O,
    6. Gonthier MP
    : Hyperglycemia modulates redox, inflammatory and vasoactive markers through specific signaling pathways in cerebral endothelial cells: Insights on insulin protective action. Free Rad Biol Med 130: 59-70, 2019. DOI: 10.1016/j.freeradbiomed.2018.10.430
    OpenUrlCrossRefPubMed
  21. ↵
    1. Datta A,
    2. Akundi S,
    3. Wagh K,
    4. Bhurle G,
    5. Sarmah D,
    6. Sharma A,
    7. Shah S,
    8. Borah A,
    9. Saraf S,
    10. Bhattacharya P
    : Stroke and associated comorbidities in Southeast Asian countries. Neuroprotection: 1-19, 2025. DOI: 10.1002/nep3.71
    OpenUrlCrossRef
  22. ↵
    1. Iovanovici DC,
    2. Negru AG,
    3. Cseppento CDN,
    4. Bochis CF,
    5. Dogaru BG,
    6. Jurcau A,
    7. Emilia BE,
    8. Pașcalau NA,
    9. Ghitea TC,
    10. Bungau AF,
    11. Bungau SG
    : Usefulness of the Edmonton Frail Scale in assessing the impact of heart failure on frailty. In Vivo 37(4): 1857-1866, 2023. DOI: 10.21873/invivo.13277
    OpenUrlAbstract/FREE Full Text
  23. ↵
    1. Esenwa CC,
    2. Elkind MS
    : Inflammatory risk factors, biomarkers and associated therapy in ischaemic stroke. Nat Rev Neurol 12(10): 594-604, 2016. DOI: 10.1038/nrneurol.2016.125
    OpenUrlCrossRefPubMed
  24. ↵
    1. García González L
    : Exploration of peripheral electrical stimulation adapted as a modulation tool for reciprocal inhibition through the activation of afferent fibers during gait. Universitat Politècnica de Catalunya, 2023. Available at: http://hdl.handle.net/2117/387886 [Last accessed on March 14, 2025]
    1. Prasun P
    : Mitochondrial dysfunction in metabolic syndrome. Biochim Biophys Acta Mol Basis Dis 1866(10): 165838, 2020. DOI: 10.1016/j.bbadis.2020.165838
    OpenUrlCrossRefPubMed
  26. ↵
    1. Ellulu MS,
    2. Patimah I,
    3. Khaza’ai H,
    4. Rahmat A,
    5. Abed Y
    : Obesity and inflammation: the linking mechanism and the complications. Arch Med Sci 13(4): 851-863, 2017. DOI: 10.5114/aoms.2016.58928
    OpenUrlCrossRefPubMed
  27. ↵
    1. Krentz AJ,
    2. Viljoen A,
    3. Sinclair A
    : Insulin resistance: a risk marker for disease and disability in the older person. Diabet Med 30(5): 535-548, 2013. DOI: 10.1111/dme.12063
    OpenUrlCrossRefPubMed
    1. Ghitea TC,
    2. El-Kharoubi A,
    3. Ganea M,
    4. Bimbo-Szuhai E,
    5. Nemeth TS,
    6. Ciavoi G,
    7. Foghis M,
    8. Dobjanschi L,
    9. Pallag A,
    10. Micle O
    : The antimicrobial activity of Origanum vulgare L. correlated with the gastrointestinal perturbation in patients with metabolic syndrome. Molecules 26(2): 283, 2021. DOI: 10.3390/molecules26020283
    OpenUrlCrossRefPubMed
    1. Stroia CM,
    2. Ghitea TC,
    3. Vrânceanu M,
    4. Mureșan M,
    5. Bimbo-Szuhai E,
    6. Pallag CR,
    7. Pallag A
    : Relationship between vitamin D3 deficiency, metabolic syndrome and VDR, GC, and CYP2R1 gene polymorphisms. Nutrients 16(9): 1272, 2024. DOI: 10.3390/nu16091272
    OpenUrlCrossRefPubMed
  30. ↵
    1. Ghitea TC,
    2. Aleya L,
    3. Tit DM,
    4. Behl T,
    5. Stoicescu M,
    6. Sava C,
    7. Iovan C,
    8. El-kharoubi A,
    9. Uivarosan D,
    10. Pallag A,
    11. Bungau S
    : Influence of diet and sport on the risk of sleep apnea in patients with metabolic syndrome associated with hypothyroidism — a 4-year survey. Environ Sci Pollut Res 29(16): 23158-23168, 2022. DOI: 10.1007/s11356-021-17589-x
    OpenUrlCrossRef
  31. ↵
    1. Echouffo-Tcheugui JB,
    2. Ali MK,
    3. Griffin SJ,
    4. Narayan KMV
    : Screening for type 2 diabetes and dysglycemia. Epidemiol Rev 33(1): 63-87, 2011. DOI: 10.1093/epirev/mxq020
    OpenUrlCrossRefPubMed
  32. ↵
    1. Grundy SM
    : Inflammation, hypertension, and the metabolic syndrome. JAMA 290(22): 3000, 2003. DOI: 10.1001/jama.290.22.3000
    OpenUrlCrossRefPubMed
    1. Hage FG
    : C-reactive protein and hypertension. J Hum Hypertens 28(7): 410-415, 2014. DOI: 10.1038/jhh.2013.111
    OpenUrlCrossRefPubMed
    1. Willerson JT,
    2. Ridker PM
    : Inflammation as a cardiovascular risk factor. Circulation 109(21 suppl 1): II2-10, 2004. DOI: 10.1161/01.CIR.0000129535.04194.38
    OpenUrlCrossRefPubMed
  35. ↵
    1. Pietri P,
    2. Vyssoulis G,
    3. Vlachopoulos C,
    4. Zervoudaki A,
    5. Gialernios T,
    6. Aznaouridis K,
    7. Stefanadis C
    : Relationship between low-grade inflammation and arterial stiffness in patients with essential hypertension. J Hypertension 24(11): 2231-2238, 2006. DOI: 10.1097/01.hjh.0000249701.49854.21
    OpenUrlCrossRefPubMed
  36. ↵
    1. Mancusi C,
    2. Izzo R,
    3. di Gioia G,
    4. Losi MA,
    5. Barbato E,
    6. Morisco C
    : Insulin resistance the hinge between hypertension and type 2 diabetes. High Blood Press Cardiovasc Prev 27(6): 515-526, 2020. DOI: 10.1007/s40292-020-00408-8
    OpenUrlCrossRefPubMed
  37. ↵
    1. Jadhav AN,
    2. Tarte PR
    : Acute cardiovascular complications in patients with diabetes and hypertension: management consideration for minor oral surgery. J Korean Assoc Oral Maxillofac Surg 45(4): 207-214, 2019. DOI: 10.5125/jkaoms.2019.45.4.207
    OpenUrlCrossRefPubMed
Back to top

In this issue

Print
Download PDF
Article Alerts
Email Article
Citation Tools
Reprints and Permissions
The Impact of Metabolic Comorbidities (Diabetes, Hypertension) on the Severity and Progression of Oropharyngeal Infections
FLORIAN CIPRIAN VENTER, TIMEA CLAUDIA GHITEA, ADRIAN NICOLAE VENTER, AMIN-FLORIN EL-KHAROUBI, MOUSA EL-KHAROUBI, EVELIN CLAUDIA GHITEA, MARC CRISTIAN GHITEA, AMINA VENTER
In Vivo Jan 2026, 40 (1) 663-676; DOI: 10.21873/invivo.14228
Twitter logo Facebook logo Mendeley logo

Jump to section

Keywords