Genetically Supported Drug Targets and Dental Traits: A Mendelian Randomization Study.

Current interventions for oral/dental diseases heavily rely on operative/surgical procedures, while the discovery of novel drug targets may enable access to noninvasive pharmacotherapy. Therefore, this study aims to leverage large-scale data and Mendelian randomization (MR) techniques, utilizing genetic variants as instruments, to identify potential therapeutic targets for oral and dental diseases supported by genetic evidence. By intersecting 4,302 druggable genes with expression quantitative trait loci from 31,684 blood samples, we identified 2,580 druggable targets as exposures. Single nucleotide polymorphisms associated with dental disease/symptom traits were collected from FinnGen R9, the Gene-Lifestyle Interactions in Dental Endpoints consortium, and the UK Biobank to serve as outcomes for both discovery and replication purposes. Through MR analysis, we identified 43 druggable targets for various dental disease/symptom traits. To evaluate the viability of these targets, we replicated the analysis using circulating protein quantitative trait loci as exposures. Additionally, we conducted sensitivity, colocalization, Gene Ontology/Kyoto Encyclopedia of Genes and Genomes annotation, protein-protein interaction analyses, and validated dental trait-associated druggable gene expression in animal models. Among these targets, IL12RB1 (odds ratio [OR], 1.01; 95% confidence interval [CI], 1.01-1.01) and TNF (OR, 0.98; 95% CI, 0.97-0.99) exhibited therapeutic promise for oral ulcers, whereas CXCL10 (OR, 0.84; 95% CI, 0.76-0.91) was for periodontitis. Through a rigorous quality control and validation pipeline, our study yields compelling evidence for these druggable targets, which may enhance the clinical prognosis by developing novel drugs or repurposing existing ones.

Difference in hematocrit and plasma albumin levels as an early biomarker of severity and prognosis in patients with severe fever and thrombocytopenia syndrome.

Severe fever with thrombocytopenia syndrome (SFTS) is a widespread infectious disease with high mortality. Hence, identifying valuable biomarkers for detecting the early changes in SFTS is crucial. In this study, we investigated the relationship between the difference in hematocrit (HCT) and serum albumin (ALB) levels (HCT-ALB) and the prognosis of patients with SFTS virus infection. After excluding the patients who did not meet the SFTS diagnostic criteria, those with SFTS from the First Affiliated Hospital of Wannan Medical College were divided into a fatal and Nonfatal group based on their disease prognosis. A dynamic analysis of the daily laboratory data was conducted for 14 days following SFTS onset. A receiver operating characteristic (ROC) curve was used to evaluate the predictive value of HCT-ALB. Another sample of patients with SFTS admitted to the First Affiliated Hospital of Nanjing Medical University was utilized to verify the study conclusions. A total of 158 patients with SFTS were included. Among them, 126 patients were categorized in the Nonfatal group and 32 in the fatal group, leading to a mortality rate of 20.25% (32/158). Univariate analysis of the laboratory test findings and ROC curve analysis showed that alanine aminotransferase (ALT), aspartate aminotransferase (AST), HCT-ALB, and lactate dehydrogenase (LDH) had a relatively better ability to discriminate the disease condition of the patients with SFTS. Moreover, HCT-ALB served as a predictor of SFTS prognosis. Additionally, an area under the ROC curve (AUC) of 0.777 and a critical HCT-ALB value of 4.75 on day 7 were associated with a sensitivity of 83.3% and a specificity of 73.9%. On day 8 (AUC = 0.882), the critical value of HCT-ALB was 9.25, while the sensitivity was 100% and specificity was 76.5%. Further verification based on the data of 91 patients with SFTS admitted to the First Affiliated Hospital of Nanjing Medical University demonstrated a mortality rate of 51% (24/47) among those with HCT-ALB values >4.75 on day 7 of the disease course, highlighting the potential of the HCT-ALB value of >4.75 for predicting SFTS prognosis. High HCT-ALB values are closely related to the mortality of patients with SFTS. HCT-ALB is a sensitive and independent predictor of early disease in patients with SFTS.

Big Data in Epidemiology: Brave New World?

Epidemiology is experiencing a significant shift toward the utilization of big data for health monitoring and decision-making. This article discusses the recent example of the World Health Organization (WHO) global oral health status report and regional summaries, which faced criticisms due to its reliance on big data from the Global Burden of Disease (GBD) study. We address the arguments for and against the use of big data in epidemiology and provide an assessment of the value and limitations of big data epidemiology. Moreover, we provide recommendations as to how the oral health community should reconcile traditional epidemiologic approaches with big data and advanced data analytics. This Perspective article highlights the challenges of the current epidemiologic landscape, the potential of big data, and the need for a balanced approach to data utilization in epidemiology.

Developing partnerships for academic data science consulting and collaboration units.

Data science consulting and collaboration units (DSUs) are core infrastructure for research at universities. Activities span data management, study design, data analysis, data visualization, predictive modelling, preparing reports, manuscript writing and advising on statistical methods and may include an experiential or teaching component. Partnerships are needed for a thriving DSU as an active part of the larger university network. Guidance for identifying, developing and managing successful partnerships for DSUs can be summarized in six rules: (1) align with institutional strategic plans, (2) cultivate partnerships that fit your mission, (3) ensure sustainability and prepare for growth, (4) define clear expectations in a partnership agreement, (5) communicate and (6) expect the unexpected. While these rules are not exhaustive, they are derived from experiences in a diverse set of DSUs, which vary by administrative home, mission, staffing and funding model. As examples in this paper illustrate, these rules can be adapted to different organizational models for DSUs. Clear expectations in partnership agreements are essential for high quality and consistent collaborations and address core activities, duration, staffing, cost and evaluation. A DSU is an organizational asset that should involve thoughtful investment if the institution is to gain real value.

The power and pitfalls of underpowered studies.

This article reflects on the potential value and many pitfalls of underpowered studies to help authors and readers consider whether and how they contribute meaningfully to the published literature. A basic introduction to power and sample size calculations is provided. Several problems that can arise in analysis and publication of underpowered studies are described. In addition, features of underpowered studies that may provide value are proposed, including when the hypothesis test of interest is a limited part of the story, the data is rich enough to showcase interesting features of the population of interest, when the rarity or ubiquity of events is an important finding, and when the study is preregistered to reduce the impact of publication bias. Several reporting guidelines for underpowered studies are also suggested.

Clinical and Epidemiological Profile of Patients Presenting With Acute Abdomen to the Emergency Department of a Tertiary Care Hospital.

BACKGROUND:  Acute abdomen is a common and urgent clinical condition requiring prompt diagnosis and treatment. This study determines the clinical and epidemiological profile of patients presenting with acute abdomen at a tertiary care hospital. OBJECTIVE: To describe the demographic characteristics, provisional diagnoses, treatment modalities, and pain management effectiveness in patients with acute abdomen. METHODS:  This prospective observational study was conducted in the Emergency Medicine department at Dr. D. Y. Patil Medical College, Hospital & Research Center, Pimpri, Pune, over a specified period. The study included patients presenting with acute abdomen, aged above 14 years, excluding those with traumatic acute abdomen and pregnant patients. A sample size of 146 was calculated based on the proportion of ureteric colic cases, with a 95% confidence interval and a 6% margin of error; however, a total of 176 patients were included in the study. Data collection involved recording demographic details, clinical features, provisional diagnoses, and pain scores, as well as performing required blood investigations and ultrasonography. Pain scores were assessed before and after treatment. Patients will be given non-steroidal anti-inflammatory drugs (NSAIDs) or opioid analgesia, depending on the clinical severity. Emergency medicine residents, in consultation with the on-call consultant, determined the disposition of patients, deciding if they required surgical or conservative management. RESULTS: The study found that the majority of patients, n = 130 (73.86%), were aged 26-50 years, with cases n = 103 (58.52%) being males and cases n = 73 (41.48%) females. Acute appendicitis was the most common diagnosis, n = 41 (24.43%), followed by urolithiasis n = 33 (18.75%). Surgical interventions were required for n = 78 (45.08%) of patients, highlighting the urgent nature of these conditions. Pain management was effective, with significant reductions in pain scores post-treatment (mean visual analog score (VAS) decreased from 6.22 to 2.33, and mean numerical rating score (NRS) from 6.05 to 2.10; p < 0.001). CONCLUSION:  The study underscores the high prevalence of gastrointestinal and renal conditions in patients with acute abdomen, particularly in middle-aged adults. The high rate of surgical interventions reflects the urgent nature of these conditions. Significant reductions in pain scores demonstrated effective pain management. Comprehensive care strategies are essential for optimizing patient outcomes. Future research with larger sample sizes and multi-center participation is recommended to validate these findings and enhance management protocols for acute abdomen.

Improving rigor and reproducibility in western blot experiments with the blotRig analysis.

Western blot is a popular biomolecular analysis method for measuring the relative quantities of independent proteins in complex biological samples. However, variability in quantitative western blot data analysis poses a challenge in designing reproducible experiments. The lack of rigorous quantitative approaches in current western blot statistical methodology may result in irreproducible inferences. Here we describe best practices for the design and analysis of western blot experiments, with examples and demonstrations of how different analytical approaches can lead to widely varying outcomes. To facilitate best practices, we have developed the blotRig tool for designing and analyzing western blot experiments to improve their rigor and reproducibility. The blotRig application includes functions for counterbalancing experimental design by lane position, batch management across gels, and analytics with covariates and random effects.

Are the Relationships of Physical Activity and Television Viewing Time With Mortality Robust to Confounding? A Study, Utilizing E-Values, From the Melbourne Collaborative Cohort Study.

BACKGROUND: Physical activity and sedentary behavior are associated with health outcomes. However, evidence may be affected by confounding bias. This study aimed to examine the relationships of physical activity and television (TV) viewing time with all-cause, cardiovascular, and cancer mortality in a cohort of Australian adults, and determine the robustness of these relationships to residual and unmeasured confounding. METHODS: Data from 27,317 Melbourne Collaborative Cohort Study participants (mean age = 66) were used. Physical activity was assessed using the International Physical Activity Questionnaire-Short Form and categorized as insufficient, sufficient, or more than sufficient. TV viewing time was categorized as low, moderate, or high. Multivariable Cox regression models were used to evaluate associations of interest. E-values were calculated to assess the strength of unmeasured confounders required to negate the observed results. RESULTS: For highest versus lowest physical activity category, the hazard ratio was 0.67 (95% confidence interval, 0.56-0.81) for all-cause mortality; E-values ranged between 1.79 and 2.44. Results were similar for cardiovascular mortality; however, hazard ratios were lower (0.72; 95% confidence interval, 0.51-1.01) and E-values much smaller (1.00-2.12) for cancer mortality. For highest versus lowest TV viewing time category, the hazard ratio was 1.08 (1.01-1.15) for all-cause mortality; E-values ranged between 1.00 and 1.37. Results were similar for cardiovascular and cancer mortality. CONCLUSIONS: Physical activity and TV viewing time were associated with mortality. The robustness to unmeasured/residual confounding was moderate for physical activity (all-cause and cardiovascular mortality), but weaker for physical activity (cancer mortality) and TV viewing time in this study of Australian adults.

Examining the Evidence on the Statistics Prerequisite for Admission to Doctor of Nursing Practice Programs: Retrospective Cohort Study.

BACKGROUND: Doctor of Nursing Practice (DNP) programs in the United States confer the highest practice degree in nursing. The proportion of racial and ethnic minority DNP students, including those of Asian descent, keeps increasing in the United States. Statistics is commonly required for DNP programs. However, there is insufficient evidence regarding the number of years within which statistics should be taken and the minimum grade required for admission to the program. OBJECTIVE: This study aimed to examine the associations of statistics prerequisite durations and grades for admission with the course performances within the DNP program. We also explored whether a postadmission statistics overview course can prepare students for a DNP statistics course as well as a required statistics prerequisite course. METHODS: A retrospective cohort study was conducted with a sample of 31 DNP students at a large university in the Mid-Atlantic region. Statistical analysis of data collected over 5 years, between 2018 and 2022, was performed to examine the associations, using Spearman rank correlation analysis and Mann-Whitney U test (U). RESULTS: The performance of students in a DNP statistics course was not associated with prerequisite duration. There was no significant association between the duration and the DNP statistics course letter grades (ρ=0.12; P=.66), neither with exam 1 (ρ=0.03; P=.91) nor with exam 2 scores (ρ=0.01; P=.97). Prerequisite grades were positively associated with exam 1 grades (ρ=0.59; P=.02), but not exam 2 (ρ=0.35; P=.19) or course grades (ρ=0.40; P=.12). In addition, no difference was found in the performance of students whether meeting the prerequisite requirements or taking a 1-month, self-paced overview course (exam 1: U=159, P=.13; exam 2: U=102, P=.50; course letter grade: U=117, P=.92). CONCLUSIONS: No evidence was found to support the need for limits on when prerequisites are completed or grade requirements. Opting for a statistics overview course after admission can serve as a viable alternative to the statistics prerequisite, effectively preparing students for advanced quantitative data analysis in a DNP program.

Socioeconomic status across the early life course predicts gene expression signatures of disease and senescence.

BACKGROUND: Socioeconomic status (SES) is associated with many chronic diseases, indicators of senescence and mortality. However, the changing salience of SES in the prediction of adult health is not well understood. Using mRNA-seq abundance data from wave V of the National Longitudinal Study of Adolescent to Adult Health (Add Health), we examine the extent to which SES across the early life course is related to gene expression-based signatures for chronic diseases, senescence and inflammation in the late 30s. METHODS: We use Bayesian methods to identify the most likely model of life course epidemiology (critical, sensitive and accumulation models) that characterises the changing importance of parental SES and SES during young (ages 27-30) and mid-adulthood (ages 36-39) in the prediction of the signatures. RESULTS: For most signatures, SES is an important predictor in all periods, although parental SES or SES during young adulthood are often the most predictive. For three signatures (components of diabetes, inflammation and ageing), critical period models involving the exclusive salience of SES in young adulthood (for diabetes) or parental SES (for inflammation and ageing) are most probable. The observed associations are likely mediated by body mass index. CONCLUSION: Models of life course patterns of SES may inform efforts to identify age-specific mechanisms by which SES is associated with health at different points in life and they also suggest an enhanced approach to prediction models that recognise the changing salience of risk factors.

Decline in Explanatory Power of Trauma Mortality Models With Age: Varying Contribution of Glasgow Coma Scale, Injury Severity Score, Comorbidities, and Frailty.

INTRODUCTION: Adjusting for confounding variables is critical for objective comparison of outcomes. The explanatory power of variables used in adjusted models for injury and their relative utility across age groups has not been well-defined. This study aimed to assess the explanatory power of covariates commonly adjusted in injury research and their relative performance across age groups. METHODS: Inpatients 18-100 y (2017-2022) were selected from 90 hospital trauma registries. Patients were grouped into sequential 5-y age blocks. Mortality was defined as the proportion of patients "expired + hospice". Dominance analysis was used to determine the average contribution (McFadden's R2) for covariates commonly included in multivariable logistic regressions. RESULTS: Three hundred seventeen-thousand one hundred thirty-six patients were included (51.1% male, mean age: 63, mean injury severity score [ISS]: 9.8, mean Glasgow Coma Scale: 14.3, 93.5% blunt). Total explanatory power (McFadden's R2) for mortality was highest in youngest age group (52.7% in 18-24 group) and decreased with age, with the lowest R2 (19.6%) in 95-100 group. Regardless of age, the Glasgow Coma Scale was the most important covariate (R2 ranging from 9.0% to 20.4%). At age 18-24 y, ISS was a more dominant contributor than Elixhauser Score, but beyond 55 y, Elixhauser Score became more dominant than ISS. CONCLUSIONS: The explanatory power of adjustment models including common covariates is limited and varies significantly across age groups, decreasing linearly with age. Adjusting for outcomes using these covariates may limit objective comparisons especially for older adults. Additional research is needed to identify covariates that enhance the explanatory power of adjustment models to allow for more objective comparisons across all ages.

Factors Influencing Interest and Engagement in Biomedical Research Among Community Medicine Residents in India: A Descriptive Cross-Sectional Study.

Introduction Medical science must be based on sound and scientific evidence and requires continuous research. Engaging in research allows students and faculty to explore new frontiers, question existing paradigms, and discover innovative solutions to medical challenges. As a specialty, community medicine plays a pivotal role in addressing public health issues. However, the engagement of community medicine residents in biomedical research remains suboptimal, which may impede the generation of evidence-based practices tailored to the Indian context. This study was conducted to find the interest and engagement of community medicine residents, and factors influencing their interest in biomedical research. Methods An online survey was conducted among community medicine residents of Uttar Pradesh, from February to April 2024, using Google Forms having a semi-structured, pretested questionnaire. Results One hundred and ninety-six residents participated in the study, where females (52.6%; 103/196) outnumbered males (47.4%; 93/196). The majority of participants were third-year residents (40.8%). Most participants seemed interested in biomedical research (83.2%) and thought that Basic Course in Biomedical Research (BCBR) helps conduct research projects (75%). Around half had previous experience in research projects, with cross-sectional studies being the most common (75.9%) study design. Enhancing research skills and a desire to contribute to medical knowledge emerged as primary motivators. On the other hand, the lack of time due to being overburdened with academic and educational activities was seen as the most common barrier to conducting research. Conclusions The majority of participants were found interested in research activities. The opportunity to improve research skills, desire to serve the medical fraternity, and a positive impact on resumes were the leading motivating factors for conducting research. Difficulty in sparing time, little knowledge, and poor support from mentors were found as important barriers.

Common mistakes in biostatistics.

Biostatistics plays a pivotal role in developing, interpreting and drawing conclusions from clinical, biological and epidemiological data. However, the improper application of statistical methods can lead to erroneous conclusions and misinterpretations. This paper provides a comprehensive examination of the most frequent mistakes encountered in the biostatistical analysis process. We identified and elucidated 10 common errors in biostatistical analysis. These include using the wrong metric to describe data, misinterpreting P-values, misinterpreting the 95% confidence interval, misinterpreting the hazard ratio as an index of prognostic accuracy, ignoring the sample size calculation, misinterpreting analysis by strata in randomized clinical trials, confusing correlation and causation, misunderstanding confounders and mediators, inadequately codifying variables during the data collection, and bias arising when group membership is attributed on the basis of future exposure in retrospective studies. We discuss the implications of these errors and propose some practical strategies to mitigate their impact. By raising awareness of these pitfalls, this paper aims to enhance the rigor and reproducibility of biostatistical analyses, thereby fostering more robust and reliable biomedical research findings.

Introducing prediction intervals for sample means.

Researchers and practitioners are typically familiar with descriptive statistics and statistical inference. However, outside of regression techniques, little attention may be given to questions around prediction. In the current paper, we introduce prediction intervals using fundamental concepts that are learned in descriptive and inferential statistical training (i.e., sampling error, standard deviation). We walk through an example using simple hand calculations and reference a simple R package that can be used to calculate prediction intervals.

Methods for building a staff workforce of quantitative scientists in academic health care.

Collaborative quantitative scientists, including biostatisticians, epidemiologists, bio-informaticists, and data-related professionals, play vital roles in research, from study design to data analysis and dissemination. It is imperative that academic health care centers (AHCs) establish an environment that provides opportunities for the quantitative scientists who are hired as staff to develop and advance their careers. With the rapid growth of clinical and translational research, AHCs are charged with establishing organizational methods, training tools, best practices, and guidelines to accelerate and support hiring, training, and retaining this staff workforce. This paper describes three essential elements for building and maintaining a successful unit of collaborative staff quantitative scientists in academic health care centers: (1) organizational infrastructure and management, (2) recruitment, and (3) career development and retention. Specific strategies are provided as examples of how AHCs can excel in these areas.

Understanding the impact of radiotherapy fractionation on overall survival in a large head and neck squamous cell carcinoma dataset: a comprehensive approach combining mechanistic and machine learning models.

Introduction: Treating head and neck squamous cell carcinomas (HNSCC), especially human papillomavirus negative (HPV-) and locally advanced cases, remains difficult. Our previous analyses of radiotherapy-only HNSCC clinical trials data using mechanistically-motivated models of tumor repopulation and killing by radiotherapy predicted that hyperfractionation with twice-daily fractions, or hypofractionation involving increased doses/fraction and reduced treatment durations, both improve tumor control and reduce late normal tissue toxicity, compared with standard protocols using 35×2 Gy. Here we further investigated the validity of these conclusions by analyzing a large modern dataset on 3,346 HNSCC radiotherapy patients from the University Health Network in Toronto, Canada, where 42.5% of patients were also treated with chemotherapy. Methods: We used a two-step approach that combines mechanistic modeling concepts with state-of-the-art machine learning, beginning with Random Survival Forests (RSF) for an exploratory analysis and followed by Causal Survival Forests (CSF) for a focused causal analysis. The mechanistic concept of biologically effective dose (BED) was implemented for the standard dose-independent (DI) tumor repopulation model, our alternative dose-dependent (DD) repopulation model, and a simple model with no repopulation (BEDsimp). These BED variants were included in the RSF model, along with age, stage, HPV status and other relevant variables, to predict patient overall survival (OS) and cause-specific mortality (deaths from the index cancer, other cancers or other causes). Results: Model interpretation using Shapley Additive Explanations (SHAP) values and correlation matrices showed that high values of BEDDD or BEDDI, but not BEDsimp, were associated with decreased patient mortality. Targeted causal inference analyses were then performed using CSF to estimate the causal effect of each BED variant on OS. They revealed that high BEDDD (>61.8 Gy) or BEDDI (>57.6 Gy), but not BEDsimp, increased patient restricted mean survival time (RMST) by 0.5-1.0 years and increased survival probability (SP) by 5-15% several years after treatment. In addition to population-level averages, CSF generated individual-level causal effect estimates for each patient, facilitating personalized medicine. Discussion: These findings are generally consistent with those of our previous mechanistic modeling, implying the potential benefits of altered radiotherapy fractionation schemes (e.g. 25×2.4 Gy, 20×2.75 Gy, 18×3.0 Gy) which increase BEDDD and BEDDI and counteract tumor repopulation more effectively than standard fractionation. Such regimens may represent potentially useful hypofractionated options for treating HNSCC.

Supraventricular tachycardia diagnosis in asthma patients is associated with adverse health outcomes.

INTRODUCTION: Supraventricular tachycardia (SVT) can occur during treatment of an acute asthma exacerbation. There are, however, no data on the long-term outcomes of children who are diagnosed with both asthma and SVT. This study aims to analyze the impact of SVT in asthmatic children on mortality and/or cardiac arrest, hypothesizing asthmatic subjects with SVT have increased mortality and/or cardiac arrest compared to asthmatic subject with no-SVT. METHODS: This was a retrospective cohort study, utilizing the TriNetX© electronic health record (EHR) database that included asthmatic subjects 2-18 years of age. The study population was divided into two groups (subjects with SVT diagnosis and no-SVT diagnosis). Data related to demographics, diagnostic, procedural, and medication codes were collected. The primary outcome was any death and/or cardiac arrest in a patient after the first asthma diagnosis date. RESULTS: This study included 91,066 asthmatic subjects (244 [0.27%] with SVT and 90,822 [99.73%] with no-SVT). Multivariable logistic regression analysis demonstrated that after controlling for demographic and clinical features, the odds of all-cause death and/or cardiac arrest after the first reported asthma exacerbation was significantly higher in asthmatic children with SVT compared to no-SVT (odds ratio [OR]: 4.30, confidence interval [CI]: 2.50-7.39, p < .001). CONCLUSIONS: Our large nationwide EHR study suggests that asthmatic pediatric patients with documented SVT diagnosis at any point in their EHR may be at increased risk of adverse health outcomes compared to no-SVT. Further studies are needed to determine the factors contributing to the increased risk of mortality and/or cardiac arrest in children with asthma and SVT.

Complete Loss of Natural Teeth and Loneliness: A Fixed-Effect Analysis.

Psychosocial properties of oral health have been reported. The present study aimed to investigate the causal effect of complete loss of natural teeth on loneliness by using fixed-effects analysis to control for confounding factors, including unmeasured time-invariant factors. Data from older adults participating in at least 2 consecutive waves of the English Longitudinal Study of Ageing in waves 3 (2006/2007), 5 (2010/2011), and 7 (2014/2015) were analyzed (N = 18,682 observations from 7,298 individuals). The association between complete loss of natural teeth and loneliness score (ranging from 3 to 9) was examined using fixed-effect linear regression analysis adjusting for time-varying confounders, including sociodemographic and health characteristics. The prevalence of complete tooth loss was 12.7%, 12.8%, and 10.6% in waves 3, 5, and 7, respectively. Individuals who transitioned to complete tooth loss during any 2 consecutive waves had an increase in loneliness score by 0.27 (95% confidence interval [CI] 0.03, 0.52), which was greater than those who maintained natural teeth (-0.03; 95% CI -0.05, -0.01). Fixed-effects analysis adjusting for time-varying confounders revealed a significant association between complete loss of natural teeth and an increase in loneliness score by 0.31 (95% CI 0.17, 0.46). Complete loss of natural teeth among older adults in England was associated with loneliness, even after accounting for measured time-varying and (un)measured time-invariant confounders. Retaining natural teeth may reduce the risk of loneliness.