Demmer, Ryan T

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.

Background We examined the relationship between infection‐related hospitalization (IRH) and heart failure (HF) in a large clinical health system and assessed whether IRH improved prediction beyond HF risk equations. Methods We studied 80 880 adults (14 150 with IRH and 66 730 age‐ and sex‐matched comparators) aged ≥18 years without HF in the Rochester Epidemiology Project from 1/1/2005–12/31/2019. IRH was identified using International Classification of Diseases ( ICD ) codes. HF incidence was defined as ≥1 HF‐related ICD code. Multivariable Cox models assessed the association between IRH and incident HF. Among 29 219 adults aged 30 to 79 years without baseline cardiovascular disease in the Rochester Epidemiology Project, Harrell's concordance statistic assessed whether IRH improved discrimination of the Predicting Risk of Cardiovascular Disease EVENTs–Heart Failure (PREVENT‐HF) equations. Results Participants were 53.3% women, 85.6% White individuals, and 95.1% non‐Hispanic individuals, with a mean age of 56.0±20.0 years. Median follow‐up time was 6.2 years. Among the IRH group, 2729 developed incident HF (2.75/100 person‐years) versus 4854 (1.00/100 person‐years) in the comparator. The multivariable adjusted hazard ratio for incident HF among participants with versus without an IRH was 1.85 (95% CI, 1.76, 1.94). In a separate cohort, incorporating IRH into PREVENT‐HF increased the concordance statistic from 0.776 to 0.779 (change, 0.003 [95% CI, 0.001–0.004]). The PREVENT‐HF risk score underestimated the 10‐year risk of HF by up to 9% among participants with an IRH. Conclusions IRH was associated with incident HF but provided negligible change to 10‐year HF risk discrimination. However, among individuals with an IRH, incorporating this information meaningfully improved 10‐year HF risk prediction. Severe infections may serve as a risk‐enhancing factor warranting enhanced preventive measures, particularly in populations where IRH is more common.

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.

Abstract Background Although periodontitis and oral microbiota are linked to cardiometabolic diseases (CMD), it is unclear if they similarly predict CMD mortality. We compared the predictive ability of salivary microbiota and periodontal disease measures for CMD mortality in the National Health and Nutrition Examination Survey (NHANES). Methods We included 5,037 adults aged ≥30 years (mean age [± standard deviation (SD)]: 48[± 14]; 50% male) from the 2009–2010 and 2011–2012 NHANES cycles. We used 16S rRNA sequencing data from saliva to operationalize microbial composition and diversity. We calculated the relative abundance log‐ratio of Treponema (linked with periodontal disease) to Corynebacterium (linked with periodontal health) to compute the Microbial Indicator of Periodontitis (MIP). Interproximal periodontal probing depth and clinical attachment loss were measured from periodontal examinations. Mortality was ascertained through 2019. Survey‐weighted Cox models regressed mortality rates on MIP, microbial diversity, and periodontal measures to estimate hazard ratios and 95% confidence intervals (HR [95% CI]). Results Over 8.8 median follow‐up years, there were 81 CMD and 267 all‐cause deaths. After multivariable adjustment, MIP was associated with increased CMD mortality risk (HR per 1‐SD: 2.10 [1.30–3.38]). Neither microbial diversity nor periodontitis measures were associated with CMD mortality. MIP was associated with periodontitis in multivariable modeling (risk ratio per 1‐SD: 1.29 [1.22–1.39]). Conclusions In a nationally representative cohort, greater baseline salivary Treponema to Corynebacterium ratio predicted increased CMD mortality risk, while microbial diversity metrics and periodontal parameters were not significantly associated with CMD mortality. Longitudinal studies that further contextualize the oral microbiota are warranted. Plain Language Summary Bacteria in the mouth that cause gum disease are linked to cardiometabolic diseases (e.g., diabetes, cardiovascular disease, kidney disease). However, it is not well understood if bacteria of the mouth can predict the risk of death due to cardiometabolic diseases. We used data from a nationwide survey of US adults to explore whether bacteria from saliva, collected from a single time point, are associated with the future risk of cardiometabolic disease death. We found that people with higher levels of gum disease bacteria were more likely to die from cardiometabolic diseases. Future studies are needed to better understand the role of gum disease bacteria in the development of cardiometabolic diseases and the risk of death.

Background Heart failure (HF) is a growing public health burden. Systemic inflammation is commonly observed in patients with HF and believed to be related to disease pathogenesis. Infection is a potential acute trigger of chronic inflammation. Our objective was to examine the relationship between infection‐related hospitalization (IRH) and HF. Methods and Results We studied beneficiaries with at least 15 months of continuous enrollment in the US‐based MarketScan databases during 2013 to 2019. We included n=152 008 patients with an inpatient or outpatient HF International Classification of Diseases, Ninth Revision/Tenth Revision ( ICD‐9/10 ) code in the primary position. Among patients with HF, IRH was identified using select ICD‐9/10 codes. We used a case‐crossover design to compare the frequency of IRH occurring within 3 months of the index HF event (case period) versus the frequency of IRH occurring 12 to 15 months before the index HF event (control period). Logistic models regressed the log‐odds of having an IRH during the case versus control periods; odds ratios (ORs; 95% CIs) are presented. Among 152 008 beneficiaries, 53% were male with a mean±SD age of 56(±11) years. The odds of having an IRH during the case (versus control) period was elevated for both the 3‐month case period (OR, 4.39 [95% CI, 4.18–4.60]), and 1‐month case period (OR, 7.39 [95% CI, 6.88–7.94]), after adjusting for the total number of hospitalizations. This relationship persisted across different types of infections. Conclusions IRH was associated with incident HF after both 1 and 3 months and may represent a modifiable risk factor for HF.

Abstract Despite the availability of effective vaccines and a recent decrease in annual deaths, COVID-19 remains a leading cause of death. Serological studies provide insights into host immunobiology of adaptive immune response to infection, which holds promise for identifying high-risk individuals for adverse COVID-19 outcomes. We investigated correlates of anti-nucleocapsid antibody responses following SARS-CoV-2 infection in a US population-based meta-cohort of adults participating in longstanding National Institutes of Health–funded cohort studies. Anti-nucleocapsid antibodies were measured from dried blood spots collected between February 2021 and February 2023. Among 1419 Collaborative Cohort of Cohorts for COVID-19 Research participants with prior SARS-CoV-2 infection, the mean age (standard deviation) was 65.8 (12.1), 61% were women, and 42.8% self-reported membership in a race/ethnicity minority group. The proportion of participants reactive to nucleocapsid peaked at 69% by 4 months after infection and waned to only 44% ≥12 months after infection. Higher anti-nucleocapsid antibody response was associated with older age, Hispanic or American Indian Alaskan Native (vs White) race/ethnicity, lower income, lower education, former smoking, and higher anti-spike antibody levels. Asian race (vs White) and vaccination (even after infection) were associated with lower nucleocapsid reactivity. Neither vaccine manufacturer nor common cardiometabolic comorbidities were not associated with anti-nucleocapsid response. These findings inform the underlying immunobiology of adaptive immune response to infection, as well as the potential utility of anti-nucleocapsid antibody response for clinical practice and COVID-19 serosurveillance.

Background The immune response to infections may become dysregulated and promote myocardial damage contributing to heart failure (HF). We examined the relationship between infection‐related hospitalization (IRH) and HF, HF with preserved ejection fraction, and HF with reduced ejection fraction. Methods and Results We studied 14 468 adults aged 45 to 64 years in the ARIC (Atherosclerosis Risk in Communities) Study who were HF free at visit 1 (1987–1989). IRH was identified using select International Classification of Diseases ( ICD ) codes in hospital discharge records and was treated as a time‐varying exposure. HF incidence was defined as the first occurrence of either a hospitalization that included an ICD, Ninth Revision ( ICD‐9 ) discharge code of 428 (428.0–428.9) among the primary or secondary diagnoses or a death certificate with an ICD‐9 code of 428 or an ICD, Tenth Revision ( ICD‐10 ) code of I50 among any of the listed diagnoses or underlying causes of death. We used multivariable‐adjusted Cox proportional hazards models to assess the association between IRH and incident HF, HF with reduced ejection fraction, and HF with preserved ejection fraction. Median follow‐up time was 27 years, 55% were women, 26% were Black, mean age at baseline was 54±6 years, 46% had an IRH, and 3565 had incident HF. Hazard ratio (HR) for incident HF events among participants who had an IRH compared with those who did not was 2.35 (95% CI, 2.19–2.52). This relationship was consistent across different types of infections. Additionally, IRH was associated with both HF with reduced ejection fraction and HF with preserved ejection fraction: 1.77 (95% CI, 1.35–2.32) and 2.97 (95% CI, 2.36–3.75), respectively. Conclusions IRH was associated with incident HF, HF with reduced ejection fraction, and HF with preserved ejection fraction. IRH might represent a modifiable risk factor for HF pathophysiology.