Association between clusters of antibodies against periodontal microorganisms and Alzheimer disease mortality: Evidence from a nationally representative survey in the USA.

BACKGROUND: Alzheimer disease (AD) has been linked with periodontal microorganisms such as Porphyromonas gingivalis in observational and mechanistic studies. IgG antibodies against periodontal microorganisms which are markers of past and current periodontal infection have been correlated with cognitive impairment. We examined associations between empirically derived groups of 19 IgG antibodies against periodontal microorganisms and AD mortality. METHODS: Individuals participating in the Third National Health and Nutrition Examination Survey (NHANES III) with complete data on IgG titers were followed up between 1988 and December 31, 2019. The outcome was AD mortality, and the main exposures were IgG antibodies against periodontal microorganisms classified into four mutually exclusive groups using cluster analysis. Survey-weighted Cox proportional hazard models were used to evaluate adjusted hazard ratios (aHR) and 95% confidence intervals (CI) for the relationship between clusters and AD mortality. RESULTS: With up to 21 years of follow-up, 160 AD-related deaths were documented. In the multivariable-adjusted model, AD mortality overall was not associated with the Red-Green (aHR 1.18; 95% CI, 0.46-3.07), Yellow-Orange (aHR 1.36; 95% CI, 0.58-3.19), Orange-Blue (aHR 0.63; 95%, CI, 0.33-1.21), and the Orange-Red (aHR 0.79; 95% CI, 0.37-1.70) when the upper tertiles were compared to the bottom tertiles. However, the subgroup of middle-aged individuals in the highest tertile of the Red-Green cluster, but not older individuals, had a 13% higher risk of AD mortality (aHR 1.13; 95% CI, 1.02-1.26) compared with those in the bottom tertile. CONCLUSION: Clusters of IgG antibodies against periodontal microorganisms did not predict AD mortality in this study.

Identifying a group of factors predicting cognitive impairment among older adults.

BACKGROUND: Cognitive impairment has multiple risk factors spanning several domains, but few studies have evaluated risk factor clusters. We aimed to identify naturally occurring clusters of risk factors of poor cognition among middle-aged and older adults and evaluate associations between measures of cognition and these risk factor clusters. METHODS: We used data from the National Health and Nutrition Examination Survey (NHANES) III (training dataset, n = 4074) and the NHANES 2011-2014 (validation dataset, n = 2510). Risk factors were selected based on the literature. We used both traditional logistic models and support vector machine methods to construct a composite score of risk factor clusters. We evaluated associations between the risk score and cognitive performance using the logistic model by estimating odds ratios (OR) and 95% confidence intervals (CI). RESULTS: Using the training dataset, we developed a composite risk score that predicted undiagnosed cognitive decline based on ten selected predictive risk factors including age, waist circumference, healthy eating index, race, education, income, physical activity, diabetes, hypercholesterolemia, and annual visit to dentist. The risk score was significantly associated with poor cognitive performance both in the training dataset (OR Tertile 3 verse tertile 1 = 8.15, 95% CI: 5.36-12.4) and validation dataset (OR Tertile 3 verse tertile 1 = 4.31, 95% CI: 2.62-7.08). The area under the receiver operating characteristics curve for the predictive model was 0.74 and 0.77 for crude model and model adjusted for age, sex, and race. CONCLUSION: The model based on selected risk factors may be used to identify high risk individuals with cognitive impairment.