Papapanou, Panos N

ABSTRACT Aim To systematically review and meta‐analyze cohort studies evaluating associations of gingival inflammation, periodontitis, and tooth loss with cognitive decline and incident mild cognitive impairment or dementia. Methods PROSPERO‐registered searches of MEDLINE, Embase, Scopus, Web of Science, and the Cochrane Library identified cohort studies from inception through October 10, 2025, with a PubMed update on June 24, 2026. Random‐effects meta‐analyses pooled adjusted hazard ratios (HRs) for dementia/MCI; cognitive function outcomes were summarized qualitatively. Risk of bias (RoB) was assessed using the Newcastle–Ottawa Scale. Results Fifty‐nine studies met inclusion criteria; 28 contributed to dementia/MCI meta‐analyses. Severe periodontitis (HR: 1.16; 95% CI: 1.06–1.27; I 2 = 0%; Q ‐test p = 0.49), periodontitis of unspecified severity (HR: 1.45; 95% CI: 1.21–1.73; I 2 = 98%; Q ‐test p < 0.001), edentulism (HR: 1.26; 95% CI: 1.19–1.33), and low tooth count (HR: 1.27; 95% CI: 1.14–1.42; I 2 = 94%; Q ‐test p < 0.001) were associated with elevated dementia/MCI risk. RoB was low in 31 studies (53%), moderate in 25 (42%), and high in 3 (5%); inadequate confounder adjustment was common. Meta‐regression showed stronger estimates for ICD‐code periodontitis definitions (fold change (FC) = 1.44 95% CI: 1.04–2.01), older cohorts (FC per year = 1.02 95% CI: 1.00, 1.05), and weaker estimates with lower RoB (FC per NOS star = 0.81 95% CI: 0.73–0.92). Follow‐up duration, cohort age, and RoB were not associated with tooth‐count estimates. Conclusion Periodontitis and tooth loss are associated with modestly higher dementia/MCI risk and poorer cognitive function, but heterogeneity, residual confounding, and bias temper causal interpretation. Longitudinal studies with rigorous baseline cognition assessment and intervention studies are needed.

ABSTRACT Cohort studies are vulnerable to confounding and time‐related biases. Recent advances emphasize the importance of emulating a hypothetical target trial to minimize several of these biases. The target trial framework is a critical tool for designing cohort studies. It requires explicit specification of protocol components: eligibility criteria, treatment strategies, outcomes, timing of follow‐up (time zero), and the causal contrast to be estimated. Critical to this framework is defining “time zero” when treatment assignment takes place and follow‐up begins, which prevents prevalent user and immortal time bias and ensures proper temporal sequencing of confounders, exposure, and outcome. By guiding design, data collection, and analysis, the target trial framework helps align observational studies with the principles of randomized trials, improving the validity and interpretability of their findings. While it cannot eliminate unmeasured confounding, this approach promotes transparent, bias‐conscious cohort design, minimizes time‐related bias, and supports more robust causal inference with observational data.

ABSTRACT Cross‐sectional studies capture health states, exposures, and risk factors at a single time point, providing essential data for estimating disease prevalence and informing public health planning. These studies serve multiple epidemiological purposes: characterizing population health, monitoring temporal trends through repeated surveys, and evaluating interventions via interrupted time series designs. They also offer practical advantages for validating self‐reported measures and creating diagnostic models. Cross‐sectional designs are efficient and well‐suited to descriptive epidemiology, but they have limited utility for causal inference. The simultaneous measurement of exposures and outcomes creates temporal ambiguity that fundamentally constrains etiologic interpretation. However, causal inferences can be strengthened under specific conditions—when temporal sequence is unambiguous (e.g., genetic variants preceding outcomes) or when valid instrumental variables are available. This methodological tutorial equips readers with concepts and tools to critically appraise cross‐sectional studies across the application domains outlined and to design and analyze their own cross‐sectional studies that yield high‐quality epidemiologic descriptions.

ABSTRACT Aims To investigate whether the association between the nitrite‐generating capacity of the subgingival microbiome and early cardiometabolic risk biomarkers varies by dietary nitrate intake. Materials and Methods Cross‐sectional data from 668 participants (mean age 31 ± 9 years, 73% women) were analysed. Dietary nitrate intake was calculated from food frequency questionnaires. Subgingival 16S rRNA sequencing (Illumina, MiSeq) and PICRUSt2 estimated microbial genes. The Microbiome‐Induced Nitric Oxide Enrichment Score (MINES) was calculated as a ratio of microbial gene abundances representing enhanced net capacity for NO generation. Adjusted multivariable linear models regressed cardiometabolic risk biomarkers (HbA1c, glucose, insulin, insulin resistance (HOMA‐IR), blood pressure) on nitrate intake and MINES together with a MINES × nitrate intake interaction term. Results Mean nitrate intake was 190 ± 171 mg/day. Significant interactions of MINES and nitrate intake were observed for insulin and HOMA‐IR ( p < 0.05). Among participants with a low MINES, higher nitrate intake was associated with lower HOMA‐IR (1.2 [1.1–1.4] vs. 1.5 [1.3–1.6]; p = 0.002), but levels were similar in those with high MINES ( p = 0.84). Conclusions A biomarker of higher microbial NO‐generating capacity in subgingival plaque is associated with lower insulin and insulin resistance among individuals with lower dietary nitrate intake. Future trials evaluating the cardiometabolic benefits of nitrate‐rich diets should incorporate measures of the entire oral microbiome.

ABSTRACTObjectiveTo study the association between dietary patterns and subgingival microbiota.MethodsParticipants (n = 651) who were enrolled in the Oral Infections, Glucose Intolerance, and Insulin Resistance Study (ORIGINS) with subgingival plaque sampling (n = 890 plaques) and a dietary assessment were included. 16S rRNA gene amplicon sequences of subgingival plaque from sites with either probing depth <4 or ≥4 mm were processed separately and used to obtain α‐diversity metrics (Faith, Shannon, Simpson, Observed) and taxa ratios (Red Complex to Corynebacterium [RCLR], Treponema to Corynebacterium [TCLR], and Treponema to Neisseria [TNLR]). Food frequency questionnaires (FFQs) were processed to calculate Alternate Healthy Eating Index (AHEI) and A Priori Diet Quality Score (APDQS) scores. Mixed regression models examined the mean levels of microbial metrics across quartiles of diet quality. Means ± standard errors are reported along with p‐values.ResultsIn multivariable models assessing the association between diet scores and α‐diversity metrics, higher AHEI values were significantly associated with lower Faith (p‐value = 0.01) and Observed (p‐value = 0.04) diversity values; similar findings were observed for APDQS (p‐value = 0.01, p‐value = 0.04). In multivariable models assessing the association between diet scores (AHEI and APDQS) and taxa ratios (RCLR, TCLR and TNLR), as the AHEI quartile increased, all taxa ratios decreased significantly as follows: −1.06 ± 0.093 in Q1 to −1.34 ± 0.099 in Q4 (RCLR), −0.43 ± 0.077 in Q1 to −0.64 ± 0.083 in Q4 (TCLR) and −0.09 ± 0.083 in Q1 to −0.38 ± 0.089 in Q4 (TNLR), respectively. In contrast, as the APDQS quartiles increased, only TNLR decreased significantly from −0.08 ± 0.085 in Q1 to −0.34 ± 0.091 in Q4.ConclusionDiets rich in fruits, vegetables, whole grains and other nutritionally rich plant foods are associated with lower oral microbial diversity and favourable ratios of pathogenic to commensal microbiota.

Background: Dietary nitrate consumption is associated with cardiometabolic health in many but not all studies. The enterosalivary nitrate-nitrite-nitric oxide (NO 3 - NO 2 - NO) pathway requires the oral microbiome for NO production (a signaling molecule beneficial to cardiometabolic health) following nitrate intake. We hypothesize that the oral microbiome modifies the cardiometabolic benefits of nitrate intake. Methods: We analyzed cross-sectional data from 657 ORGINS participants. Dietary nitrate intakes and alternative healthy eating index (AHEI) scores were calculated from food frequency questionnaire responses and a US food database. Subgingival 16S rRNA microbial genes (Illumina, MiSeq) were sequenced, and PICRUSt2 estimated metagenomic content. The Microbiome Induced Nitric oxide Enrichment Score (MINES) was calculated as a microbial gene abundance ratio representing enhanced net capacity for NO generation. Cardiometabolic biomarker (systolic and diastolic blood pressure, HbA1C, glucose, insulin, and HOMA-IR) Z-scores were averaged to form a cardiometabolic Z-score (CMZ). Adjusted multivariable linear models regressed CMZ and individual Z-scores on nitrate intake tertiles. Results: Participants were mean age 31±9, 72% female, 31% White, 14% Black, and 28% Hispanic. Mean nitrate intake was 189±171 mg/day. Higher nitrate intake was significantly associated with lower CMZ, glucose, insulin, and HOMA-IR. Mean CMZ(SE) across dietary nitrate tertiles were 0.53(0.30), 0.35(0.31), and -0.11(0.30), p for trend=0.04; findings were attenuated after adjustment for AHEI (p for trend=0.12). In subgroup analysis by MINES level, higher nitrate intake was only associated with lower cardiometabolic biomarker levels among participants with a <median MINES even after AHEI adjustment (Table). Conclusion: Higher dietary nitrate intake was associated with lower cardiometabolic risk biomarker levels only among those with reduced potential for oral microbiome-dependent NO generation.

AbstractAimWe investigated whether periodontal measures are cross‐sectionally associated with prediabetes and cardiometabolic biomarkers among non‐diabetic younger adults.Materials and MethodsOne thousand seventy‐one participants (mean age = 32.2 years [SE = 0.3]; 73% female) from the Oral Infections, Glucose Intolerance and Insulin Resistance Study were enrolled. Full‐mouth clinical attachment loss (fm‐CAL), probing depth (fm‐PD) and bleeding on probing were ascertained. Interproximal CAL (i‐CAL) and probing depths (i‐PD) served as our primary exposures. Glucose, HbA1c, insulin and insulin resistance (HOMA‐IR) outcomes were assessed from fasting blood. Prediabetes was defined per American Diabetes Association guidelines. Prediabetes prevalence ratios (PR [95% CI]) and mean [SE] cardiometabolic biomarkers were regressed on periodontal variables via multivariable robust variance Poisson regression or multivariable linear regression.ResultsPrevalence of prediabetes was 12.5%. Fully adjusted prediabetes PR in Tertiles 3 versus 1 of mean i‐CAL was 2.42 (1.77, 3.08). Fully adjusted fasting glucose estimates across i‐CAL tertiles were 83.29 [0.43], 84.31 [0.37], 86.48 [0.46]; p for trend <.01. Greater percent of sites with i‐PD ≥3 mm showed elevated natural‐log‐HOMA‐IR after adjustment (0%–12% of sites = 0.33 [0.03], 13%–26% of sites = 0.39 [0.03], ≥27% of sites = 0.42 [0.03]; p for trend = .04).Conclusionsi‐CAL (vs. fm‐CAL) was associated with elevated fasting glucose and prediabetes, whereas i‐PD (vs. fm‐PD) was associated with insulin resistance. Future studies are needed to examine periodontal disease and incident prediabetes.