Oral health‐related quality of life in <scp>US</scp> adults with type 2 diabetes

Vu, G; Little, Bertis B.; Esterhay, Robert J.; Jennings, J’Aime C.; Creel, Liza; Gettleman, Lawrence

doi:10.1111/jphd.12489

Cited by 5 publications

(14 citation statements)

References 18 publications

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“…Following previous studies ( 15 , 23 ), the composite score generated from OHIP-7 was used to construct two OHRQoL measures—the severity of perceived poor OHRQoL and the prevalence of poor OHRQoL. The severity of perceived poor OHRQoL was based on a summative score, ranging from 0 to 28, generated from seven questions in OHIP-7.…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies examined the impact of periodontal diseases on the OHRQoL between patients with and without diabetes and found mixed findings ( 11 – 13 ). Others identified risk factors associated with the poor OHRQoL of diabetic patients and found several risk factors, including, but not limited to, dry mouth sensation, the use of a removable prosthesis, untreated dental caries, periodontal disease, unmet denture needs, low income, and poor oral hygiene ( 14 , 15 ). Based on the national data with a sample of 2,945 community dwellers in the United States, the study found that diabetic patients were more likely to experience poorer OHRQoL than those without diabetes ( 15 ).…”

Section: Introductionmentioning

confidence: 99%

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Rural-urban disparities in Oral Health-related Quality of Life for middle-aged and older adults with diabetes in Taiwan

Chen

Lin

et al. 2023

Front. Public Health

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BackgroundPublic health faces a significant challenge in reducing rural–urban disparities in diabetes. Since dietary control is part of the medical regimen for diabetes management, how diabetic patients perceive the impact of oral health on their quality of life is critical. The present study aimed to compare the Oral Health-related Quality of Life (OHRQoL) between rural and urban diabetic patients.MethodsThe study design was cross-sectional. The study sample included 831 self-reported diabetic patients, extracted from the first wave of the new-cohort Taiwan Longitudinal Study on Aging survey (NC_TLSA) that comprised a nationally representative sample of community-dwelling adults aged 50 and above in Taiwan. The composite score generated from the Oral Health Impact Profile-7 (OHIP-7), which has seven questions, was used to construct two OHRQoL measures, the severity of perceived poor OHRQoL and the prevalence of poor OHRQoL. These two OHRQoL measures were treated as dichotomous variables. Multivariate logistic regression models were applied for analysis.ResultsRural diabetic patients had a higher likelihood of experiencing the severity of perceived poor OHRQoL than those in urban areas (OR = 2.40, 95% CI: 1.30–4.40). Although rural diabetic patients also had a higher prevalence of poor OHRQoL than urban diabetic patients, the difference was not significant (OR = 1.47, 95% CI: 0.95–2.28). Social determinants, such as education, are essential factors attributed to both OHRQoL measures.ConclusionOverall, rural diabetes community-dwelling patients had a poorer OHRQoL than those in urban areas. Given a bidirectional relationship between oral health and diabetes, improving oral health in rural areas may be a critical avenue to improve the quality of diabetes care in rural areas.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rural-urban disparities in Oral Health-related Quality of Life for middle-aged and older adults with diabetes in Taiwan

Chen

Lin

et al. 2023

Front. Public Health

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“…BMI, WC, TG, and systolic BP were higher in the obese group than in the non-obese group, confirming the recruitment of participants for the study who experienced adverse impacts on OHRQoL. Levels of FPG were not significantly different among all the studied groups, excluding the influence of DM on lower OHRQoL [ 16 ].…”

Section: Discussionmentioning

confidence: 69%

“…Our search of the literature found no studies that have directly explored OHRQoL in older persons with obesity. Most studies reported OHRQoL in adults or older patients with metabolic syndrome (MS), diabetes mellitus (DM), or chronic kidney disease (CKD) [ 16 , 17 , 18 ]. The OHRQoL of patients with MS, DM, or CKD whose status had an obese component tended to be negatively impacted [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…Most studies reported OHRQoL in adults or older patients with metabolic syndrome (MS), diabetes mellitus (DM), or chronic kidney disease (CKD) [ 16 , 17 , 18 ]. The OHRQoL of patients with MS, DM, or CKD whose status had an obese component tended to be negatively impacted [ 16 , 17 , 18 ]. However, Yamashita et al investigated OHRQoL in young adults with obesity and found no difference in the OHIP scores between obese and non-obese populations [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

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Increased Oral Dryness and Negative Oral Health-Related Quality of Life in Older People with Overweight or Obesity

2022

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This cross-sectional study was to evaluate the association between the oral health-related quality of life (OHRQoL) of older Thai people with obesity and oral health indicators. General and oral conditions were assessed. Oral dryness was determined using the Xerostomia Inventory-11 (XI-11) and clinical oral dryness score (CODS). OHRQoL was evaluated by the oral health impact profile (OHIP-14). Participants were aged 60–86 years; 73 (59.3%) were overweight or obese, and 50 (40.7%) were normal weight. Older patients with obesity had almost four times the rate of hypertension (OR = 3.59; 95%CI:1.34–9.60; p = 0.002), more missing teeth (p = 0.025), and higher CODS (p = 0.014) than those without obesity. The total XI-11 scores were positively associated with the total CODS, after adjusting for BMI (r = 0.267, p = 0.003). Those with obesity had almost three times the tendency for a negative OHRQoL compared with the non-obese (OR = 2.73; 95%CI:1.12–6.71; p = 0.04). After adjusting for all related factors, the chances of predicting an OHIP-14 score of four based on obesity and total XI-11 score were 4.42 (95%CI:1.57–12.47; p = 0.005) and 1.11 (95%CI:1.02–1.20; p = 0.013), respectively. Obesity had an increasingly undesirable negative impact on the OHRQoL of older Thai people and was influenced by BMI and oral dryness.

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Interpretable machine learning model for data driven classification of Oral Health Related Quality of Life in Patients with Type 2 Diabetes Mellitus

Srivastava,

Murali,

Jain

et al. 2024

Preprint

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Type 2 Diabetes Mellitus(T2DM) is a debilitating condition with a number of complications including those of the oral cavity which can further deteriorate patients general and oral health related quality of life (OHRQoL). Machine Learning (ML) can help assign an individuals propensity to develop poor OHRQoL, given a set of variables, and at the same time identify the most important features contributing to this outcome. Previously inferential statistical methods have attempted to explain this, albeit with limited success. The aim of this cross sectional study is to determine the impact on OHRQoL in T2DM patients, and identify features most likely to be associated with this outcome and to compare ML and DL analytical methods with inferential statistics. Twelve-hundred T2DM patients were subjected to OHRQoL and demographic data questionnaires and WHO Oral Health Assessment form. K-means Clustering was performed to label individuals as having or not having an impact on OHRQoL. Class imbalance was addressed by undersampling of the majority class using informed subset selection. Further, using the collected data as input features we developed ML algorithms (Naive Bayes(NB), Random Forest(RF), Logistic Regression(LR), Kernel Support Vector Machine(SVM) and Artificial Neural Network(ANN)), to accurately classify individuals with or without poor oral health related quality of life (OHRQoL) and utilized SHapley Additive exPlanations (SHAP) analysis for feature importance. The best performing model was SVM (AUC=0.983; Sensitivity=1) for classifying the patients into into poor OHRQoL. SHAP values were highest for Age, Prosthetic Need, Tobacco use and years since onset of diabetes. Features closely related to diabetes, that is, periodontal pockets and loss of attachment were not identified as relevant by inferential statistics, but were deemed as important features associated with poor OHRQoL by SHAP analysis.

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Oral health‐related quality of life in US adults with type 2 diabetes

Abstract: Keywordsoral health-related quality of life; oral health; quality of life; type 2 diabetes; United States.

Cited by 5 publications

References 18 publications

Rural-urban disparities in Oral Health-related Quality of Life for middle-aged and older adults with diabetes in Taiwan

Rural-urban disparities in Oral Health-related Quality of Life for middle-aged and older adults with diabetes in Taiwan

Increased Oral Dryness and Negative Oral Health-Related Quality of Life in Older People with Overweight or Obesity

Interpretable machine learning model for data driven classification of Oral Health Related Quality of Life in Patients with Type 2 Diabetes Mellitus

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