obesity epidemiology case study

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Obesity Epidemiology

Associate Professor of Nutrition and Epidemiology

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During the past twenty years there has been a dramatic increase in obesity in the United States. An estimated 30% of adults in the US are obese; in 1980, only 15% were. The issue is gaining greater attention with the CDC and with the public health world in general. This book offers practical information about the methodology of epidemiologic studies of obesity. The book is structured in four main sections. The first section considers issues surrounding the definition of obesity, measurement techniques, and the designs of epidemiologic studies. The second section addresses the consequences of obesity, looking at epidemiologic studies that focus on cardio-vascular disease, diabetes, and cancer. The third section looks at determinants obesity, reviewing a wide range of risk factors for obesity including diet, physical activity and sedentary behaviors, sleep disorders, psychosocial factors, physical environment, biochemical and genetic predictors, and intrauterine exposures. The final section addresses the analytical issues and challenges for epidemiologic studies of obesity.

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The genetic epidemiology of obesity: a case study

Affiliation.

• 1 Cancer Research UK Health Behaviour Research Centre, Department of Epidemiology and Public Health, University College London, London, UK. [email protected]
• PMID: 21153623
• DOI: 10.1007/978-1-60327-416-6_17

Obesity (OMIM #601665) is a disease where excessive stores of body fat impact negatively on health. The first law of thermodynamics dictates that energy cannot be created or destroyed so if energy is taken into the body, but not transformed to ATP for metabolic work or dissipated as heat, it will be stored as fat. Therefore, the ultimate cause of obesity is a long-term positive energy imbalance [energy intake (EI) exceeds energy expenditure (EE)]. Despite this simple explanation, there is no single reason why EI may exceed EE meaning that the proximate causes of obesity are multi-factorial in origin involving a complex interplay of genetic, behavioural, and environmental influences on metabolism, diet, and activity.

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Data and case studies

Resources Policy Dossiers Obesity & COVID-19 Data and case studies

• Sugar-Sweetened Beverage Tax
• Digital Marketing
• School-based interventions
• Community-level interventions
• Pregnancy & Obesity
• Childhood Obesity Treatment
• Front-of-pack nutrition labelling
• Obesity & COVID-19
• Physical Activity
• Food Systems
• Weight Stigma

World Obesity have collated some of the recent data and case studies available looking pertaining to obesity and the current outbreak of COVID-19. 

Researchers at Johns Hopkins University in the US examined 265 patients to determine if younger patients hospitalised with COVID-19 were more likely to be living with overweight and obesity. They found a correlation, which they hypothesise may be due to physiologic changes from obesity. Other comorbidities these patients may have had were not reported. Read the full study here .

Chinese researchers identified 66 patients with COVID-19 and fatty liver disease and compared the outcomes for those with and without obesity. They found obesity was a significant risk factor for severe illness in this population after accounting for other factors (age, gender, smoking, diabetes, high blood pressure, and dyslipidaemia). Read the full study here . 

The global rise in the prevalence of obesity and type 2 diabetes can be partially explained by a rise in diets high in fats, sugars and refined carbohydrates. Diets high in saturated fatty acids cause inflammation and immune disfunction, which may explain why minority groups (who experience disproportionate rates of diseases linked to nutrition, such as obesity and diabetes) are also hospitalised with COVID-19 at higher rates. Read the full study here .

MicroRNAs (abbreviated miRNAs) are produced in human cells to regulate gene expression. Some research has suggested that these may also defend against viruses. These researchers identified 848 miRNAs that are may be effective against SARS and 873 that could target COVID-19 using genome sequences of each of these viruses. Previous studies have suggested that the elderly and those with underlying conditions (including obesity) may produce less of these miRNAs, possibly explaining why these groups are at increased risk of severe illness from COVID-19. However, trials in human and animal subjects are needed to verify these theoretical results. Read the full study here .  

Given the importance of determining the risk factors for morbidity and mortality related to COVID-19, this retrospective study analysed the frequency and outcomes of COVID-19 patients in critical care who are living with overweight or obesity. “Of the 3,615 individuals who tested positive for COVID-19, 775 (21%) had a body mass index (BMI) 30-34, and 595 (16% of the total cohort) had a BMI >35.” While patients were separated into elderly (over 60) and younger (under 60) groups, it was not reported if the study controlled for other variables that may affect the course of COVID-19. Read the full study here .

This piece describes two patients with obesity that experienced damage to their airways while being intubated due to severe illness from COVID-19. The authors recommend videolaryngoscopy for intubation to protect both patients and healthcare workers. Read the full study here .

These researchers chose to specifically examine how many COVID-19 patients living with obesity or overweight were placed on ventilators. Based in Lille, France, the study included 124 patients, 68.8% of whom ultimately required ventilation. They established a dose-response relationship- increasing body max index (BMI) increased the risk of needing ventilation. This study found that BMI seemed to be associated with ventilator treatments independently of age, diabetes or high blood pressure. However, further research must be conducted before this relationship is proven. Read the full study here .

Researchers obtained medical records of 16,749 people hospitalised for COVID-19 to determine what were some of the factors that made patients more likely to experience severe cases of the illness. Slightly over half of patients had at least one underlying condition (including obesity) and these patients were more likely to die from COVID-19. The study found that obesity is linked to mortality, independently of age, gender and other associated conditions. Read the full study here .

Using a very large sample size of 17,425,455, this cohort study aimed to identify risk factors associated with mortality due to COVID-19 across the general population. Among the comorbidities, most of them were associated with increased risk, including obesity. Furthermore, deprivation was also identified as a major risk factor. Specifically, for patients with overweight and obesity, as their body mass index increased, so did their risk of dying from COVID-19. Read the full study here .

This study included 48 critically ill patients with COVID-19 treated with invasive ventilation in Spain. Of this population, 48% were living with obesity, 44% with hypertension, and 38% with chronic lung disease. Symptoms and patient outcomes were also described. Read the full study here .

This study examined the correlation between severe disease and body mass index (BMI) among 357 patients in France. People diagnosed with severe COVID-19 were 1.35 times more likely to also be living with obesity and people in critical care with COVID-19 were 1.89 times more likely to be living with obesity than the general public. This study adjusted for age and gender of patients but no other cofounding factors. Read the full study here .

Previous research has demonstrated that children tend to gain weight during when school is not in session, so experts have been concerned about the impact of lockdowns due to coronavirus on childhood obesity rates. This study observed lifestyle behaviours in 41 children living with obesity at baseline and then three weeks into quarantine. Scientists found that children reported eating more meals, as well as more potato chips, red meat, and sugar-sweetened beverages. They slept more, exercised less and spent much more time looking at screens. As a result, researchers recommend that lifestyle interventions be delivered through telemedicine while the lockdown lasts. Read the full study here .

A recent study from France examined 1317 COVID-19 patients living with diabetes. Of these, more than 10% passed away and almost 33% needed to be placed on a ventilator within a week of admission to the hospital. Obesity was found to be an independent risk factor for poor outcomes when other cofounding factors were accounted for. Read the full study here .

This study found that, of 5700 patients admitted to 12 selected New York hospitals with COVID-19, 56.6% had hypertension (high blood pressure), 41.7% were living with obesity and 33.8% had diabetes. It also reported data on patient outcomes. Read the full study here .  

Wuhan city, the capital of Hubei province in China, was for a long time the epicentre of the COVID-19 outbreak. This study presents information of patients admitted to two Wuhan hospitals with laboratory-confirmed COVID-19. 191 patients were included in order to determine what risk factors lead to fatalities, describe Covid-19 symptoms over time, determine how long patients are infectious after they recover and record what treatments were tried. It should be noted that almost half of patients had underlying health conditions such as hypertension or heart disease, although obesity was not measured. Read the full study here . 

This study examined 24 adults to determine which populations in the Seattle area were hospitalised with severe illness from COVID-19, what underlying conditions they had, the results of medical imaging tests and whether they recovered. Patients had an average body mass index of 33.2 (give or take 7.2 units) and over half (58%) of patients were diagnosed with diabetes. Scientists concluded that “patients with coexisting conditions and older age are at risk for severe disease and poor outcomes after ICU [intensive care unit] admission.” Read the full study here .

Looking at 383 patients in Shenzen, China, this study was the first to directly examine the correlation between obesity and severe illness from coronavirus. For this study, a person with a body mass index (BMI) between 24.0 - 27.9 was considered overweight and a person with a BMI greater than 28 was considered to be living with obesity. While people living with obesity generally experienced the same length of illness, they were significantly more likely to develop severe pneumonia, even when accounting for other risk factors. Read the full study here .

Based on a sample of 4,103 New York City residents, this paper evaluates what characteristics make people more likely to be admitted to the hospital and critical care.  Overall, it was observed that 39.8% of people living with obesity were hospitalised, compared to 14.5% without. Scientists found “particularly strong associations of older age, obesity, heart failure and chronic kidney disease with hospitalization risk, with much less influence of race, smoking status, chronic pulmonary disease and other forms of heart disease.” Read the full study here .

In order to ensure the proper monitoring of COVID-19-related hospitalisations across the United States, the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) was developed. This report “presents age-stratified COVID-19-associated hospitalisation rates for patients admitted during March 1-28, 2020, and clinical data on patients admitted during March 1-30, 2020.” Among the 1,482 patients diagnosed and hospitalised with COVID-19, 90% had at least one comorbidity and 42% were living with obesity, with African Americans and the elderly disproportionately affected. Read the full study here .

This report examined demographic information of patients hospitalised with COVID-19 in China. Of these, older patients, diabetics and those living with obesity were significantly more likely to be considered “severely ill.” The study also looked at symptoms during admission at admission and treatment options. Read the full study here .

In this study, researchers used data from 103 consecutive patients hospitalized in the USA. There were two major findings- a correlation between critical care admissions due to COVID-19 and a body mass index greater than 35 in general, and a correlation between needing invasive mechanical ventilation and having both heart disease and obesity. These findings were adjusted for age, sex, and race. Read the full study here .

This article examined how SARS- CoV-2 impacts pregnancy using 46 patients in the USA. Almost all patients who developed severe disease were living with overweight and obesity. After diagnosis, 16% of patients were admitted to the hospital and 2% were placed in intensive care. Researchers believe this, along with the need to induce labour prematurely in some patients to improve breathing, may suggest that pregnant women should be classified as a vulnerable group. Read the full study here .

School and recreational space closures due to COVID-19 have reduced physical activity among children. Researchers used modeling software to simulate the following scenarios: 

• No school closures (control) 
• Schools closed for two months 
• Schools closed for two months and 10% reduction in physical activity over the summer break  
• Schools closed for four months (April through May and September through October) and 10% reduction in physical activity over the summer break 
• Schools closed for six months (April through May and September through December) and 10% reduction in physical activity over the summer break 

Overall, the pandemic is projected to increase mean standardised body mass index (BMI) between 0.056 (two-month closure) and 0.198 (six-month closure) units. It may also increase the percentage of children living with obesity in the USA by up to 2.373 percentage points. Read the full study here .

This study was conducted to examine the characteristics and course of disease in 50 New York children (under 21 years of age) hospitalised with COVID-19. Of the study population, 11 patients had obesity and 8 had overweight.  Obesity was found to be a significant risk factor for both severe disease and mechanical ventilation while immunosuppression was not.  Read the full study here .

Researchers at the University of Chicago Medical Center found that patients hospitalized with COVID-19 were more likely to die if they were also living with obesity, even when accounting for age, sex, and underlying conditions. 238 patients were included within the study. These researchers did not find a significant connection with admission to critical care units or mechanical ventilation in patients with obesity. Limitations included the makeup of the study population, as the sample size was small and the vast majority were African American, so the results may not be representative of all people. Read the full study here.  

This meta-analysis and systematic review found nine separate articles regarding the link between COVID-19, obesity and more severe diseases. Between all studies, 1817 patients were examined. Researchers found an odds ratio of 1.89 for poor outcomes in patients with obesity, especially among younger patients, which indicates that obesity increases the risk of severe diseases. Read the full study here . 

A meta-analysis concluded that people living with obesity were more likely to have worse outcomes if they also contracted COVID-19. Researchers identified nine articles (six of which were retrospective case-control studies, four of which were retrospective cohort studies, and one of which used both methods) and extracted data from each. Limitations included heterogeneity in study design (particularly regarding the definition of obesity), lack of comorbidity reporting, and low quantity of studies used. Read the full study here .

As almost 75% of American adults over the age of 20 are living with overweight or obesity, this disease should be considered a public health priority, especially given the increased likelihood of poor outcomes in COVID-19 patients with obesity. The paper outlines several mechanisms explaining why obesity may lead to more severe disease, including having more of the receptor the virus uses to enter cells, reduced lung function, chronic inflammation, endothelial disfunction, changes in blood clotting, and physiological changes related to common comorbidities of obesity. Finally, several compelling studies linking obesity to increased risk of complications are included. Read the full study here .

Evidence shows that the impact of COVID-19 tends to be more serious in specific vulnerable groups, including people living with obesity. Furthermore, the pandemic also seems to have a number of indirect repercussions including on eating behaviour patterns among people with obesity. The objective of this study was “to examine the impact of the COVID-19 pandemic on patronage to unhealthy eating establishments in populations with obesity.”   

These researchers combined GPS data with known obesity rates to determine how many people with obesity entered unhealthy restaurants during the COVID-19 pandemic (December 2019- April 2020). Prior to lockdowns, more people in areas with high obesity rates entered fast food restaurants; in March, fewer people did across all areas; however, the numbers of patrons steadily increased during April, at a faster rate in areas with higher obesity rates. While informative, a number of limitations were observed, including the fact that not all consumers exactly match the demographics of the area they live in and that more variables may contribute to restaurant traffic than accounted for here. Read the full study here . 

Various studies over the past few months have linked obesity to a more serious course of illness from COVID-19. It is therefore essential that we improve our understanding of the possible reasons for the link and what it means for those living with obesity. This systematic review looks at the influence of obesity on COVID-19 outcomes and proposes biological mechanisms as to why a more severe courseof illness can occur. It also discusses the implications of COVID-19 for those living with obesity. Read the full study here .

Both COVID-19 and childhood obesity are pandemics raging across America today. Obesity is an independent risk factor for the severity of COVID-19, suggesting that children with obesity could see a more severe course of illness due to COVID-19. The stay-at-home mandates and physical distancing preventative measures have resulted in a lack of access to nutritious foods, physical activity, routines and social interactions, all of which could negatively impact children -especially those living with obesity. Read the full study here .

Obesity has been suggested as a risk factor for poor outcome in those with COVID-19. Studies show that patients with obesity are more likely to require mechanical ventilation. In fact, multiorgan failure in patients with COVID-19 and obesity could be dueto the chronic metabolic inflammation and predisposition to the “enhanced release of cytokines-pathophysiology accompanying severe obesity”. However, the association between body mass index (BMI) and COVID-19 outcomes has yet to be fully explored. This study intends to address that gap. Read the full study here .

Emerging evidence suggests that the severity of COVID-19 in a patient is associated with overweight and obesity. Patients with obesity are at risk for a number of other non-communicable diseases, including cardiovascular dysfunction and hypertension and diabetes. In individuals living with overweight and obesity, macronutrient excess in adipose tissue stimulates adipocytes “to release tumour necrosis factor α(TNF-α), interleukin-6 (IL-6) and other pro-inflammatory mediators and to reduce production of the anti-inflammatory adiponectin, thus predisposing to a proinflammatory state and oxidative stress”. Obesity also impairs immune responses; it has a negative impact on pathogen defences within the body. Therefore, the acceleration of viral inflammatory responses in COVID-19 and more unfavourable prognoses are associated with individuals living with obesity. Read the full study here .

Obesity has been identified as a comorbidity for severe outcomes in patients with COVID-19. In this study, comorbidities associated with increased risk of COVID-19 were determined in a population-based analysis of Mexicans with at least one comorbidity. Data was obtained from the COVID-19 database of the publicly available Mexican Ministry of Health “Dirección General de Epidemiología”. Variables of the patients’ heath were all noted, such as age, gender, smoking status, history of COVID-19 contact, comorbidities, etc. Patients with missing information were excluded in the analysis. To determine the independent effect of each comorbidityon COVID-19 and separate the effect of two or more, “analysis was limited to patients reporting only one comorbidity." Read the full study here .

Obesity has arisen as a major complication for the COVID-19 pandemic, which has been caused by the novel SARS-CoV-2 virus. The former is a major health concern due to its side-effects on human health and association with morbidity and mortality. Evidence points out that obesity can worsen patient prognosis due to COVID-19 infection. There may be a “pathophysiological link that could explain the fact that obese patients are prone to present with SARS-CoV-2 complications”. The authors present mechanistic obesity-related issues that aggravate the SARS-CoV-2 infection in patients living with obesity and the possible molecular links between obesity and SARS-CoV-2 infection. Read the full study here .

The highly infectious serious acute respiratory syndrome COVID-19 has caused high morbidity and mortality all over the world. It has been suggested that SARS-CoV-2, the pathogen of COVID-19, uses angiotensin-converting enzyme 2 (ACE2) as a cell receptor. This receptor is found in the lungs but also many other organs, including the adipose tissue, heart, and oral epithelium. Previous studies have identified obesity as a critical factor in the prognoses of COVID-19 patients, and that, in patients with COVID-19, non-survivors had a higher body mass index (BMI) than survivors. This study intended to “investigate the association between obesity and poor outcomes of COVID-19 patients." Read the full study here .

Approximately 45% of individuals worldwide have overweight or obesity. Obesity is characterized by its pro-inflammatory condition. The excess visceral and omental adiposity seen in individuals with obesity are linked with an increase in pro-inflammatory cytokines that affect systemic cellular processes. Importantly, they “change the nature and frequency of immune cells infiltration”. When a high percentage of a population have obesity, more virulent viral strains tend to develop, and the reach of a virus is wider. Furthermore, the state of obesity is correlated to the presence of comorbidities that are dangerous to human health, such as type 2 diabetes and hypertension. This systematic review includes articles from a myriad of databases in order to address how living with obesity impacts one’s reaction to the SARS-CoV-2 virus and course of COVID-19. Read the full study here .

The psychological impact of COVID-19 lockdown and quarantine on children has been documented to cause “anxiety, worrying, irritability, depressive symptoms, and even post-traumatic stress disorder symptoms”. In particular, children living with severe obesity may struggle with anxieties about the possibility of obesogenic issues that can arise during the course of illness due to COVID-19. In this study, 75 families (one child interviewed per family) were interviewed on anxiety that their child with severe obesity may have, and on what specific type anxieties they are. 24 of 75 children reported having COVID-19 related anxieties. Read the full study here . 

In this multi-centre study focused on retrospective observational data from eight hospitals throughout Greece, the data on 90 critically ill patients positive for COVID-19 is analysed. Those hospitalised due to COVID-19 reflect critically ill patients whodeveloped extremely severe acute respiratory syndrome (SARS) in elderly patients with COVID-19-related pneumonia and/or underlying chronic diseases. Many underlying chronic diseases have been identified as risk factors for developing more severe COVID-19. These include type-2 diabetes, cardiovascular diseases, and hypertension. Obesity has also been associated with disease severity. In this study the relation of comorbidities such as obesity and type-2 diabetes and COVID-19 disease severity is explored. Read the full study here .

According to the World Health Organisation, physical inactivity is the fourth leading cause of death, and increases the risk of a person contracting a “metabolic disease, including obesity and type 2 diabetes (T2D).” This article points out that those seeking treatment for obesity or T2D may find difficulty in doing so during the COVID-19 pandemic due to lockdowns. As it has been found that sedentary behaviour increases one's risk for many chronic diseases, the authors wished to explore hypothetical immunopathologyof COVID-19 in patients living with obesity and how the immune defences against COVID-19 may be related to the “immuno-metabolic dysregulations'' characterised by it. Furthermore, they explore the possibility of exercise as a counteractive measure due to its anti-inflammatory properties. Read the full study here .

Obesity has been linked to a less-efficient immune response in the human body as well as poorer outcomes for respiratory diseases. In this article, researchers hypothesised that a higher Body Mass Index is a risk factor for a more severe course of illness for COVID-19. They followed all patients hospitalised from 11 January to 16 February 2020 until March 26 2020 at the Third People’s Hospital of Shenzhen (China), which was dedicated to COVID-19 treatment. Read the full study here .

As reported by the World Health Organization, the global prevalence of obesity is still on the rise both across high-income as well as low-and middle-income countries. Obesity has been associated with an increase in mortality for patients fighting COVID-19. The authors suggest that the inflammatory profile associated with patients with obesity is conducive to a more severe course of illness in patients with COVID-19. Read the full study here .

Researchers studying COVID-19, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), have concluded that obesity, diabetes, hypertension or cardiovascular disease is correlated to an increased severity of illness due to COVID-19. Obesity has been associated with SARS-CoV-2 due to the “cytokine storm” of the latter; a number of the pro-inflammatory cytokines released in the “storm” which are detrimental to organ function are also found contributing to the chronic low-grade inflammation in patients with obesity. The authors wished to study a Middle Eastern population and assess the outcome of COVID-19 in relation to obesity. They observed clinical data from patients in the Al Kuwait Hospital in Dubai, UAE, to study the correlation between obesity and poor clinical outcomes of COVID-19. Read the full study here .

In many previous studies, underlying conditions such as obesity, hypertension and diabetes have been found to be correlated with an increased rate of hospitalisation and death due to SARS-CoV-2. Obesity is a non-communicable disease marked by an imbalanced energy state due to hypertrophy and hyperplasia of adipose tissue. Increased secretion of various cytokines and hormones, such as interleukin-6, tumour necrosis factor alpha and leptin, establishes a low-grade inflammatory state in patients with obesity. These pro-inflammatory cytokines predispose individuals “to increased risk for infection and adverse outcomes”. The metabolic disorders that are associated with obesity are numerous, including diabetes, hypertension and cardiovascular diseases. Most are associated with an increased risk of severe COVID-19. Due to this link, obesity is “an important factor in determining the morbidity and mortality risk in SARS CoV 2 patients” as well as the need for mechanical ventilation. Read the full study here .

Pulmonary consolidation is the most common complication of COVID-19. A high percentageof COVID-19 related pulmonary consolidationis due to extensive pulmonary fibrosis (PF). Viral infections have been shown to be a risk factor for PF, and both viral infections and aging were“strongly associated cofactors” for PF in this study. Infection with SARS-CoV-2, the virus responsible for COVID-19,suppresses the angiotensin-converting enzyme 1 (ACE2), which is a receptor exploited by the virus for cell entry; this receptor is “a negative regulator of” PF, which therefore links the virus to the progression of PF. Read the full study here .

Elevated body mass index has been marked as a risk factor for COVID-19 severity, hospital admissions and mortality. Diabetes and hypertension have also been associated with severe and fatal cases of COVID-19. Mendelian randomisation (MR) analyses the causal effect of an exposure risk factor on an outcome using genetic variants as instruments of estimation. In this study, the causal relationship between obesity traits (such as elevated BMI and metabolic disorders) and quantitative cardiometabolic biomarkers and COVID-19 susceptibility was examined by MR. Data was obtained from the UK Biobank. 1,211 individuals who had tested positive for COVID-19 and 387,079 individuals who were negativeor untestedwere analysed. Read the full study here .

Obesity and diabetes have both been identified in epidemiological reports as comorbidities “frequently associated with severe forms of COVID-19”. Both have also been identified as an independent risk factor for the severity of COVID-19 in a patient. The presence of these diseases is associated with each other; therefore, they could “confer a particularly high risk of severe COVID-19”. In previous analysis of the CORONAvirus-SARS-CoV-2 and Diabetes Outcomes (CORONADO) Study, it was shown “that body mass index (BMI) was positively and independently associated with severe COVID-19-related outcomes ... in patients with diabetes hospitalised for COVID-19”. In this analysis of the CORONADO data, the course of COVID-19 and its relationship to obesity in patients with type 2 diabetes hospitalised for this disease is explored. The influence of age on BMI and COVID-19 prognosis is also addressed due to the heightened impact of COVID-19 on the elderly population. Read the full study here .

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• Published: 27 January 2020

Epidemiology and Population Health

Evidence from big data in obesity research: international case studies

• Emma Wilkins 1 ,
• Ariadni Aravani 1 ,
• Amy Downing 1 ,
• Adam Drewnowski 2 ,
• Claire Griffiths 3 ,
• Stephen Zwolinsky 3 ,
• Mark Birkin 4 ,
• Seraphim Alvanides 5 , 6 &
• Michelle A. Morris   ORCID: orcid.org/0000-0002-9325-619X 1  

International Journal of Obesity volume  44 ,  pages 1028–1040 ( 2020 ) Cite this article

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Background/objective

Obesity is thought to be the product of over 100 different factors, interacting as a complex system over multiple levels. Understanding the drivers of obesity requires considerable data, which are challenging, costly and time-consuming to collect through traditional means. Use of ‘big data’ presents a potential solution to this challenge. Big data is defined by Delphi consensus as: always digital , has a large sample size, and a large volume or variety or velocity of variables that require additional computing power (Vogel et al. Int J Obes. 2019). ‘Additional computing power’ introduces the concept of big data analytics. The aim of this paper is to showcase international research case studies presented during a seminar series held by the Economic and Social Research Council (ESRC) Strategic Network for Obesity in the UK. These are intended to provide an in-depth view of how big data can be used in obesity research, and the specific benefits, limitations and challenges encountered.

Methods and results

Three case studies are presented. The first investigated the influence of the built environment on physical activity. It used spatial data on green spaces and exercise facilities alongside individual-level data on physical activity and swipe card entry to leisure centres, collected as part of a local authority exercise class initiative. The second used a variety of linked electronic health datasets to investigate associations between obesity surgery and the risk of developing cancer. The third used data on tax parcel values alongside data from the Seattle Obesity Study to investigate sociodemographic determinants of obesity in Seattle.

Conclusions

The case studies demonstrated how big data could be used to augment traditional data to capture a broader range of variables in the obesity system. They also showed that big data can present improvements over traditional data in relation to size, coverage, temporality, and objectivity of measures. However, the case studies also encountered challenges or limitations; particularly in relation to hidden/unforeseen biases and lack of contextual information. Overall, despite challenges, big data presents a relatively untapped resource that shows promise in helping to understand drivers of obesity.

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Acknowledgements

The ESRC Strategic Network for Obesity was funded via ESRC grant number ES/N00941X/1. The authors would like to thank all of the network investigators ( https://www.cdrc.ac.uk/research/obesity/investigators/ ) and members ( https://www.cdrc.ac.uk/research/obesity/network-members/ ) for their participation in network meetings and discussion which contributed to the development of this paper.

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Wilkins, E., Aravani, A., Downing, A. et al. Evidence from big data in obesity research: international case studies. Int J Obes 44 , 1028–1040 (2020). https://doi.org/10.1038/s41366-020-0532-8

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Received : 23 May 2019

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Accepted : 07 January 2020

Published : 27 January 2020

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DOI : https://doi.org/10.1038/s41366-020-0532-8

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Metabolic and biochemical effects of pyrroloquinoline quinone (pqq) on inflammation and mitochondrial disfunction: potential health benefits in obesity and future perspectives.

1. Introduction

2. pqq: structure and general characteristics, 3. pqq and adipose tissue, 4. pqq and mitochondrial dysfunction, 5. pqq and inflammation, 6. pqq and skeletal muscle health, 7. conclusions, author contributions, conflicts of interest.

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Charrier, D.; Cerullo, G.; Carpenito, R.; Vindigni, V.; Bassetto, F.; Simoni, L.; Moro, T.; Paoli, A. Metabolic and Biochemical Effects of Pyrroloquinoline Quinone (PQQ) on Inflammation and Mitochondrial Disfunction: Potential Health Benefits in Obesity and Future Perspectives. Antioxidants 2024 , 13 , 1027. https://doi.org/10.3390/antiox13091027

Charrier D, Cerullo G, Carpenito R, Vindigni V, Bassetto F, Simoni L, Moro T, Paoli A. Metabolic and Biochemical Effects of Pyrroloquinoline Quinone (PQQ) on Inflammation and Mitochondrial Disfunction: Potential Health Benefits in Obesity and Future Perspectives. Antioxidants . 2024; 13(9):1027. https://doi.org/10.3390/antiox13091027

Charrier, Davide, Giuseppe Cerullo, Roberta Carpenito, Vincenzo Vindigni, Franco Bassetto, Luca Simoni, Tatiana Moro, and Antonio Paoli. 2024. "Metabolic and Biochemical Effects of Pyrroloquinoline Quinone (PQQ) on Inflammation and Mitochondrial Disfunction: Potential Health Benefits in Obesity and Future Perspectives" Antioxidants 13, no. 9: 1027. https://doi.org/10.3390/antiox13091027

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A and B, Included are 56 387 residents across 1157 neighborhoods. C, The sample was limited to the 20 863 patients with diagnosed hypertension. Cut points for antihypertensive use and percentages of blood pressure control and Black patients were comparable with observed quartiles within Cuyahoga County, Ohio. White lines demarcate Cleveland city limits, and black lines denote major highways. White shaded areas have sparse or no residential population (ie, airports, industrial districts). ADI indicates area deprivation index.

eTable 1. Demographic, Neighborhood, and Clinical Variables Between the Analyzed Cohort and Cohorts Excluded for Missing Data or Limited Sample Sizes of Self-Reported Race Categories

eTable 2. Performance Characteristics of Sex-Stratified Conditional Autoregressive Poisson Regressions

eTable 3. Sensitivity Analysis of Hypertension Prevalence and Odds Ratios (ORs) of Hypertension Diagnosis Derived From Multivariable Logistic Regression With Interaction Among Sex, Race, and ADI Quintile Derived From the Wisconsin Neighborhood Atlas

eTable 4. Sensitivity Analysis of Hypertension Prevalence Ratios Derived From Sex-Stratified Conditional Autoregressive (CAR) Poisson Rate Models Incorporating ADI Quintile Associated With Patients’ Census Block Group of Residence

eFigure. ADI Deciles for US Census Block Groups in Cuyahoga County, Ohio, Derived From Sociome and Neighborhood Atlas

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Blazel MM , Perzynski AT , Gunsalus PR, et al. Neighborhood-Level Disparities in Hypertension Prevalence and Treatment Among Middle-Aged Adults. JAMA Netw Open. 2024;7(8):e2429764. doi:10.1001/jamanetworkopen.2024.29764

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Neighborhood-Level Disparities in Hypertension Prevalence and Treatment Among Middle-Aged Adults

• 1 Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio
• 2 Center for Healthcare Research and Policy, Case Western Reserve University/MetroHealth Medical Center, Cleveland, Ohio
• 3 Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio
• 4 Cleveland Clinic Value-Based Operations, Cleveland Clinic, Cleveland, Ohio
• 5 Center for Value-Based Care Research, Cleveland Clinic, Cleveland, Ohio

Question   Are there disparities in hypertension burden and treatment across neighborhoods by socioeconomic disadvantage and racial and ethnic composition?

Findings   In this cross-sectional study of geocoded electronic health record data for 56 387 middle-aged adults, a disproportionate burden of hypertension prevalence and treatment was found in socioeconomically disadvantaged and predominantly Black neighborhoods.

Meaning   These findings suggest the presence of neighborhood-level disparities in hypertension and treatment, indicating a need to investigate how to address these disparities at a structural level.

Importance   Hypertension in middle-aged adults (35-50 years) is associated with poorer health outcomes in late life. Understanding how hypertension varies by race and ethnicity across levels of neighborhood disadvantage may allow for better characterization of persistent disparities.

Objective   To evaluate spatial patterns of hypertension diagnosis and treatment by neighborhood socioeconomic position and racial and ethnic composition.

Design, Setting, and Participants   In this cross-sectional study of middle-aged adults in Cuyahoga County, Ohio, who encountered primary care in 2019, geocoded electronic health record data were linked to the area deprivation index (ADI), a neighborhood disadvantage measure, at the US Census Block Group level (ie, neighborhood). Neighborhoods were stratified by ADI quintiles, with the highest quintile indicating the most disadvantage. Data were analyzed between August 7, 2023, and June 1, 2024.

Exposure   Essential hypertension.

Main Outcomes and Measures   The primary outcome was a clinician diagnosis of essential hypertension. Spatial analysis was used to characterize neighborhood-level patterns of hypertension prevalence and treatment. Interaction analysis was used to compare hypertension prevalence by racial and ethnic group within similar ADI quintiles.

Results   A total of 56 387 adults (median [IQR] age, 43.1 [39.1-46.9] years; 59.8% female) across 1157 neighborhoods, which comprised 3.4% Asian, 31.1% Black, 5.5% Hispanic, and 60.0% White patients, were analyzed. A gradient of hypertension prevalence across ADI quintiles was observed, with the highest vs lowest ADI quintile neighborhoods having a higher hypertension rate (50.7% vs 25.5%) and a lower treatment rate (61.3% vs 64.5%). Of the 315 neighborhoods with predominantly Black (>75%) patient populations, 200 (63%) had a hypertension rate greater than 35% combined with a treatment rate of less than 70%; only 31 of 263 neighborhoods (11.8%) comprising 5% or less Black patient populations met this same criterion. Compared with a spatial model without covariates, inclusion of ADI and percentage of Black patients accounted for 91% of variation in hypertension diagnosis prevalence among men and 98% among women. Men had a higher prevalence of hypertension than women across race and ADI quintiles, but the association of ADI and hypertension risk was stronger in women. Sex prevalence differences were smallest between Black men and women, particularly in the highest ADI quintile (1689 [60.0%] and 2592 [56.0%], respectively).

Conclusions and Relevance   These findings show an association between neighborhood deprivation and hypertension prevalence, with disparities observed particularly among Black patients, emphasizing a need for structural interventions to improve community health.

More than 116 million US adults have hypertension, which is the top modifiable individual-level risk factor for cardiovascular disease. 1 - 3 A decrease in systolic blood pressure by 10 mm Hg is estimated to reduce the risk of a cardiovascular event by 20% to 30%. 3 Middle age (35-50 years) is a critical time for intervention, as midlife hypertension has implications for poor cardiovascular health in subsequent decades 4 and is associated with cognitive decline and dementia risk. 5 , 6 The disproportionate burden of uncontrolled hypertension in non-Hispanic Black adults is a key contributor to existing disparities in stroke, cardiovascular disease, and mortality. 7 , 8 Due to historical redlining, Black individuals have been systematically housed in neighborhoods that experienced disinvestment. 9 Furthermore, where a person lives, including local resources and social environment, has been associated with hypertension risk. 10 , 11

Critically, place-based interventions have shown positive outcomes and are necessary to target existing health inequities. 12 - 14 Many reports of hypertension prevalence use national databases, such as the National Health and Nutrition Examination Survey (NHANES), that provide nationally representative estimates of disease at the population level. 15 - 17 Few studies have reported small area–level hypertension rates, 4 , 18 and none have evaluated to what extent neighborhoods connect the association between race and ethnicity and hypertension among midlife adults. In this report, we evaluate whether spatial patterns of hypertension diagnosis and treatment are associated with neighborhood socioeconomic position and racial and ethnic composition.

In this cross-sectional study, we analyzed electronic health record (EHR) data of adults aged 35 to 50 years who resided in Cuyahoga County, Ohio, and had 1 or more primary care appointments within the Cleveland Clinic Health System or MetroHealth System in 2019. The first primary care appointment attended in 2019 for each patient was classified as their index visit. The study was approved by the Cleveland Clinic Institutional Review Board (No. 22-896). Informed consent was waived due to institutional review board determination of minimal risk and that the research could not practicably be performed otherwise. Our report follows the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

We derived area deprivation index (ADI) values in Ohio from 2015 to 2019 American Community Survey 5-year data at the US Census Block Group level using the R package sociome. 19 The ADI includes measures of income, education, housing, and occupation on a scale of 40 to 160, where a higher score indicates greater disadvantage. We used a local representation of the ADI due to technical limitations of the University of Wisconsin Neighborhood Atlas, described elsewhere 20 , 21 (eFigure in Supplement 1 ).

Our primary outcome was a clinician diagnosis of essential hypertension on or prior to the index visit. We defined essential hypertension as at least 1 International Statistical Classification of Diseases and Related Problems, Tenth Revision code in the Clinical Classification Software 98 diagnostic group. Our secondary outcome was hypertension treatment, which we defined as an antihypertensive medication prescribed on or up to 365 days prior to the index visit among patients with a hypertension diagnosis. In neighborhood-level analyses, we used the variable treatment rate, or the percentage of patients per neighborhood with hypertension who were prescribed an antihypertensive medication.

We grouped patients into neighborhoods based on their address at the time of their primary care visit. We then created ADI quintiles derived from all Ohio census block groups (ie, neighborhoods). We calculated the percentage of Black patients residing in each neighborhood and performed analyses using categories comparable with observed quartiles of our study sample within Cuyahoga County (≤5%, 5.1%-25.0%, 25.1%-75.0%, and >75.0%). This variable was included in models as the percentage of Black patients.

We obtained patient age, sex, and race and ethnicity from the EHR. Patients self-reported race as American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, White, or multiracial and self-reported ethnicity as Hispanic or not Hispanic. Due to sample size limitations, we analyzed the following combined racial and ethnic categories: Asian, Hispanic, non-Hispanic Black (hereafter Black), and non-Hispanic White (hereafter White). We excluded patients who identified as American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, and multiracial as well as patients with missing race and ethnicity, sex, or geographic identifiers.

To characterize patient health, we obtained body mass index and common comorbidities documented on or before the patients’ index visit. We considered patients to have a comorbidity if they had at least 1 International Statistical Classification of Diseases, Tenth Revision code for each of the following diseases: type 2 diabetes, lipid or metabolic disorders, coronary artery disease, chronic kidney disease, cerebrovascular disease, depression disorders, anxiety disorders, tobacco use, alcohol use, and substance abuse.

We descriptively compared patient demographics, comorbidities, and antihypertensive prescribing across ADI quintiles using frequencies and percentages for categorical variables and medians and IQRs for continuous variables. We estimated the prevalence of essential hypertension in middle-aged adults by ADI quintile and race and ethnicity and stratified results by sex because hypertension prevalence rates differ across men and women. 22 As a sensitivity analysis, we used the ADI from Kind and Buckingham 23 via the 2015 Wisconsin Neighborhood Atlas. The statistical analysis for this report was conducted between August 7, 2023, and June 1, 2024.

We conducted a spatial analysis for a deeper understanding of neighborhood-level patterns of hypertension prevalence and treatment. We developed map visualizations to compare hypertension prevalence with the percentage of Black patients across neighborhoods, ADI, and antihypertensive medication prescribing rates among those with hypertension. We estimated area-level correlation measures using Pearson correlation coefficients and Moran I statistics to identify the strength of the association among our variables of interest.

To characterize neighborhood-level hypertension rates while accounting for potential spatial correlation between neighboring block groups, we used sex-stratified bayesian conditional autoregressive (CAR) Poisson rate models. We developed 3 models: (1) a null model with no covariates (ie, random and spatial effects only), (2) a model accounting for ADI quintile to characterize hypertension prevalence across socioeconomic position, and (3) a model accounting for the interaction of ADI quintile and the percentage of Black patients per neighborhood to understand the overall degree of neighborhood-level variability in hypertension accounted for by these 2 factors.

We developed an interaction model to compare hypertension prevalence by racial and ethnic group within similar ADI quintiles. We conducted a multivariable logistic regression with a 3-way interaction among sex, race and ethnicity, and ADI quintile. Interaction terms were selected a priori to investigate the heterogeneity of hypertension across racial and ethnic groups in each ADI quintile, stratified by sex. 22 Odds ratios (ORs) from this model are displayed with the hypertension prevalence of each subgroup analyzed.

All analyses were performed using R, version 4.3.1 statistical software (R Foundation for Statistical Computing) within the Posit Workbench–integrated development environment, version 2023.09.0 + 463 (Posit Software, PBC). Bayesian estimates are reported as posterior mean (95% credible interval [CrI]), and frequentist estimates are reported as maximum likelihood estimate (95% CI).

A total of 60 546 patients met the inclusion criteria. We removed 3130 unique patients due to missing race or ethnicity (n = 3120), missing sex (n = 4), or addresses that could not be geocoded to a US Census Block Group (n = 7). We removed 1029 patients in race categories with small sample sizes (113 American Indian or Alaska Native, 27 Native Hawaiian or Pacific Islander, and 889 multiracial) (eTable 1 in Supplement 1 ). Our final analytic sample included 56 387 adults who resided in 1157 Cuyahoga County neighborhoods (block groups). The number of neighborhoods per quintile was as follows: quintile 1, 259; quintile 2, 176; quintile 3, 159; quintile 4, 187; and quintile 5, 376.

Among the 56 387 patients analyzed, the median (IQR) age was 43.1 (39.1-46.9) years, 59.8% were female, and 40.2% were male. Overall, 21.6% of patients lived in the highest (least resources) ADI quintile, and 31.2% lived in the lowest (most resources) ADI quintile. The cohort racial and ethnic background included, 1944 Asian (3.4%), 17 557 Black (31.1%), 3089 Hispanic (5.5%), and 33 797 White (60.0%) patients. The racial background of the population differed across quintiles; in the lowest ADI quintile, 1229 patients (7.0%) were Black and 15 146 (86.1%) were White compared with 7436 Black patients (61.2%) and 3006 White patients (24.6%) in the highest ADI quintile. We found a socioeconomic gradient for most comorbidities, including cerebrovascular disease, obesity, and coronary artery disease. Patients residing in neighborhoods in the highest ADI quintile had a higher prevalence of hypertension (50.7% vs 25.5%) and lower treatment rates (61.3% vs 64.5%) ( Table 1 ).

We observed a gradient in hypertension prevalence across ADI quintiles for almost all racial and ethnic groups ( Table 2 ). Across ADI quintiles, men consistently had higher rates of hypertension than women, though prevalence differences were smallest between Black men and women, particularly in the highest ADI quintile (1689 of 2833 [65.0%] and 2592 of 4630 [56.0%], respectively). For all quintiles combined, Black men and women had the highest prevalence of hypertension compared with all other racial and ethnic groups (men, 3644 of 6446 [56.5%]; women, 5715 of 11 111 [51.4%]).

We found a high degree of spatial clustering for hypertension rates (Moran I  = 0.58; P  < .001) and a small but significant degree of spatial clustering for antihypertensive prescribing (Moran I  = 0.05; P  = .002). Higher neighborhood-level prevalence of hypertension was correlated with a higher ADI quintile ( r  = 0.73; P  < .001) ( Figure , A) and a higher percentage of Black patients ( r  = 0.64; P  < .001) ( Figure , B). Neighborhoods with a greater percentage of Black patients tended to have a higher ADI score ( r  = 0.62; P  < .001). We assessed how groups of neighborhoods compared with national hypertension prevalence (33% based on a cutoff of 140/90 mm Hg) and treatment prevalence (73%) estimated using NHANES data by Aggarwal et al. 24 Among the 315 neighborhoods with predominantly Black (>75%) patient populations, 200 neighborhoods (63%) had hypertension rates of greater than 35% combined with antihypertensive prescription rates of less than 70% ( Figure , C). Of those 200 neighborhoods, 80% were in the highest ADI quintile. In comparison, only 31 of the 263 neighborhoods (11.8%) in which Black patients comprised 5% or less of the population had hypertension rates of greater than 35% combined with treatment rates of less than 70%.

In the CAR Poisson rate model incorporating ADI quintile (model 1), men living in neighborhoods in the highest ADI quintile had a 58% increased prevalence of hypertension compared with the lowest ADI quintile (posterior mean, 1.58; 95% CrI, 1.46-1.70) ( Table 3 ). In the women’s CAR model incorporating ADI quintile only, neighborhoods in the highest quintile had twice the prevalence of hypertension compared with the lowest ADI quintile (posterior mean, 2.08; 95% CrI, 1.91-2.25). Compared with a null CAR model (no covariates), ADI quintile accounted for 85% of neighborhood-level variation in men and 78% in women. The CAR model incorporating an interaction between ADI quintile and percentage of Black patients per neighborhood accounted for 91% of spatial variation in hypertension prevalence in men and 98% in women compared with the null model (performance characteristics shown in eTable 2 in Supplement 1 ).

In our interaction model, which included a 3-way interaction among sex, race and ethnicity, and ADI quintile, the odds of hypertension in the highest vs lowest ADI quintile were higher for White men (OR, 1.77; 95% CI, 1.57-2.00; P  < .001) and White women (OR, 2.88; 95% CI, 2.58-3.21; P  < .001) compared with Black men (OR. 1.46; 95% CI, 1.20-1.77; P  < .001) and Black women (OR, 1.68; 95% CI, 1.44-1.96; P  < .001) ( Table 2 ). Hispanic women had significantly increased odds of hypertension with increasing neighborhood disadvantage (quintile 5 vs quintile 1: OR, 2.49; 95% CI, 1.56-4.15; P  < .001), while higher ADI quintiles were comparatively not associated with higher odds of hypertension within Hispanic men. Asian women had relatively smaller (compared with other women) but significant increases in hypertension odds across most ADI quintiles (quintile 2: OR, 1.58 [95% CI, 1.03-2.39; P  = .03]; quintile 3: OR, 1.89 [95% CI, 1.10-3.16; P  = .02]; quintile 4: OR, 2.04 [95% CI, 1.21-3.36; P  = .006]). Among Asian men, we found increased odds of hypertension only for patients in the highest ADI quintile neighborhoods compared with those in the lowest ADI quintile neighborhoods (OR, 2.01; 95% CI, 1.11-3.56; P  = .02).

We conducted a sensitivity analysis of our spatial analysis and interaction analysis using ADI quintiles from the 2015 Wisconsin Neighborhood Atlas. 23 We excluded 99 patients who resided in a block group with suppressed ADI due to a high group quarter population. Results of the spatial analysis were similar overall (eTable 3 in Supplement 1 ). In the interaction analysis, ORs for hypertension were lower across Wisconsin Neighborhood Atlas–derived ADI quintiles for Hispanic women as in our primary analysis; results were otherwise comparable (eTable 4 in Supplement 1 ).

In this cross-sectional study, we found corresponding increases in hypertension prevalence as neighborhood disadvantage and the percentage of Black patients residing in a neighborhood increased. We identified a higher burden of midlife hypertension in Black adults compared with other racial and ethnic groups that persisted across levels of socioeconomic disadvantage. We also found that living in socioeconomically disadvantaged neighborhoods was associated with higher hypertension rates among people of all racial and ethnic backgrounds.

A growing body of evidence suggests that midlife hypertension increases the risk for heart failure, coronary heart disease, cognitive decline, and all-cause mortality. 5 , 6 , 25 , 26 In alignment with prior epidemiologic research, we found that men had a greater prevalence of hypertension than women. 27 - 29 However, the association of worsening neighborhood socioeconomic status and hypertension risk was more pronounced among Black, Hispanic, and White women. These findings are concordant with the existing literature, including a longitudinal cohort that showed the steepest annual growth in systolic blood pressure for women living in more socioeconomically vulnerable areas. 30 , 31 Thus, given the long-term consequences for health and mortality, midlife is a key time for optimization of cardiovascular risk factors. To our knowledge, our study is the first to describe the composition of neighborhood-level disparities in hypertension prevalence and treatment using the intersection of racial and ethnic composition and socioeconomic position.

In the spatial analyses, interactions between ADI and the percentage of Black patients per neighborhood accounted for nearly all the spatial variation in hypertension rates, beyond that accounted for by ADI alone in our models. This finding aligns with the history of racial residential segregation—including 20th century redlining practices that systematically excluded Black individuals from housing opportunities 9 —in Cuyahoga County, Ohio, which remains one of the most segregated areas in the US. In this study, we conceptualized race and ethnicity as socially constructed variables representing exposure to racism at interpersonal and structural levels. 9 , 32 - 34 In a robust regional population similar to our cohort, measures of structural racism were associated with a higher burden of hypertension and other chronic conditions. 35 Our stratified analysis revealed that White patients who lived in the highest ADI quintile were also diagnosed with hypertension at high rates. This finding suggests that neighborhood disinvestment and economic decline may be associated with health measures of all who live there, regardless of their racial and ethnic background.

Importantly, we found significant treatment disparities among neighborhoods that geographically corresponded to patterns of historical racial residential segregation and neighborhood disadvantage. 36 There were lower antihypertensive medication treatment rates within socioeconomically disadvantaged, predominately Black neighborhoods than in more resourced neighborhoods, suggesting that national estimates may mask nuanced variation in treatment across small areas. Despite reported national disparities in hypertension prevalence, previous estimates have also found that antihypertensive treatment rates for Black individuals in the US are comparable with those for White individuals. 8 , 24 , 27 , 37 Yet, at the neighborhood level, treatment varies across neighborhood resource level and racial and ethnic composition, suggesting that localized disparities persist.

Ongoing systemic racism has been independently associated with an increased risk of high blood pressure for both Black and Hispanic individuals. 38 In a prior cross-sectional analysis using NHANES and US Census data, Black adults had higher odds of hypertension regardless of individual or neighborhood poverty level, while only White adults living in low-income neighborhoods had higher odds of hypertension. 17 , 35 In Cuyahoga County, we report that Black men and women in midlife have hypertension prevalence rates of 57% and 51%, respectively, which are comparable with the highest national estimates within Black adults, depending on the definitions used. 7 , 24 , 39 In our analysis, nearly two-thirds of predominately (>75% population) Black neighborhoods in Cuyahoga County simultaneously exceeded the national average hypertension prevalence and were below average in antihypertensive treatment rates. Furthermore, 80% of these neighborhoods were characterized by a lack of socioeconomic resources. Hence, our study shows an intersectionality of race and place in the context of disparities in midlife hypertension, which is a critical factor in determining health and longevity later in the life course.

Our findings extend prior research on the association between neighborhoods and hypertension outcomes. 40 , 41 Successful place-based efforts have included using barber shops and salons to screen patients for hypertension who might otherwise not access primary care. 42 Place-based interventions that are more ecologically focused include setting up farmers’ markets and attracting grocery stores to food deserts to support access to healthy food. 11

Larger-scale approaches have included improving housing to ensure that it is free from lead (a known risk factor for hypertension). 43 Access to safe housing may reduce stress, another risk factor for hypertension. 44 , 45 Health system interventions, such as screening for and addressing health-related social needs that are inequitably distributed across neighborhoods, may improve blood pressure control, as well as large-scale hypertension-focused quality improvement programs. Most health system efforts are focused on the individual patient care setting 46 and are effective overall, but they do not address the disparities in blood pressure control across diverse patient populations. 47 , 48 The utility of our approach is not only that we used the ADI—a measure of income, education, housing, and employment resources, which are related to place-based interventions—but also that we demonstrate in the Figure how spatial analysis could be used to identify specific neighborhoods in which to place these interventions.

Our study has several limitations. Our population is limited to patients who interact with the health care system and obtain primary care services. Thus, the hypertension estimates provided are not the true neighborhood-level prevalence of hypertension, as only individuals who can access care are represented. It is also possible that patients may have had undiagnosed hypertension, thus making the prevalence estimates more conservative than the true estimates. We were also unable to determine whether the patients who had been prescribed an antihypertensive medication were taking the medication to treat hypertension, since antihypertensive medications are a broad category and are used to treat other conditions. Other indications for antihypertensive medication may have resulted in an overestimate of the number of patients being treated pharmacologically. Additionally, while race and ethnicity in the EHR are intended to be self-reported, we cannot exclude the possibility that this information was documented by another party without adequate verification. Finally, we focused on a single county for our analysis, with recognized limitations in generalizability of findings. However, the redlining and downstream structural racism that created the residential segregation in Cuyahoga County are widespread in the US. 9 , 49 Since racial segregation among neighborhoods is found in many large US cities, further research should investigate place-based disparities to promote equitable hypertension care in other locales.

The findings of this cross-sectional study suggest stark racial and neighborhood disparities in hypertension prevalence and antihypertensive treatment among adults in midlife, with a significant burden of undertreated hypertension in socioeconomically disadvantaged and racially segregated communities. Using spatial analysis techniques to identify neighborhoods in need, future research might investigate structural interventions to address place-based hypertension disparities.

Accepted for Publication: June 27, 2024.

Published: August 23, 2024. doi:10.1001/jamanetworkopen.2024.29764

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Blazel MM et al. JAMA Network Open .

Corresponding Author: Jarrod E. Dalton, PhD, Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Ave, JJN-3, Cleveland, OH 44195 ( [email protected] ).

Author Contributions: Dr Dalton had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Pfoh and Dalton contributed equally as co–senior authors.

Concept and design: Blazel, Perzynski, Mourany, Jones, Pfoh, Dalton.

Acquisition, analysis, or interpretation of data: Blazel, Perzynski, Gunsalus, Mourany, Gunzler, Dalton.

Drafting of the manuscript: Blazel, Pfoh, Dalton.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Blazel, Perzynski, Gunsalus, Mourany, Gunzler, Dalton.

Obtained funding: Perzynski, Dalton.

Administrative, technical, or material support: Perzynski, Jones.

Supervision: Perzynski, Jones, Pfoh, Dalton.

Conflict of Interest Disclosures: Dr Perzynski reported equity ownership in Global Health Metrics and book royalties from Springer Nature and Taylor & Francis outside the submitted work. Dr Gunzler reported receiving personal fees from BioSensics and Taylor & Francis outside the submitted work. Dr Pfoh reported receiving a Clinical Translational Science Award from the National Center for Advancing Translational Sciences to Case Western Reserve University and Cleveland Clinic. Dr Dalton reported receiving grants from the National Heart, Lung, and Blood Institute outside the submitted work. No other disclosures were reported.

Funding/Support: This work was supported by grant R01AG080486 from the National Institute on Aging (Drs Dalton and Perzynski).

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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Obesity and periodontitis: a comprehensive review of their interconnected pathophysiology and clinical implications

Claudia reytor-gonzález.

1 Facultad de Ciencias de la Salud Eugenio Espejo, Centro de Investigación en Salud Pública y Epidemiología Clínica (CISPEC), Universidad UTE, Quito, Ecuador

Juan Marcos Parise-Vasco

Natali gonzález.

2 Facultad de Odontología, Universidad UTE, Santo Domingo, Ecuador

Alison Simancas-Racines

3 Carrera de Medicina Veterinaria, Facultad de Ciencias Agropecuarias y Recursos Naturales, Universidad Técnica de Cotopaxi, Latacunga, Ecuador

Raynier Zambrano-Villacres

4 Universidad Espíritu Santo, Samborondón, Ecuador

Ana Karina Zambrano

5 Facultad de Ciencias de la Salud Eugenio Espejo, Centro de Investigación Genética y Genómica, Universidad UTE, Quito, Ecuador

Daniel Simancas-Racines

Manuel Gonzalez, Universidad Tecnológica Ecotec, Ecuador

Obesity and periodontitis are significant health problems with a complex bidirectional relationship. Excess body fat is linked to systemic diseases and can lead to persistent inflammation, potentially harming periodontal health. Periodontitis, a chronic inflammatory condition affecting the supporting structures of teeth, poses substantial health risks. Both conditions share pathological processes such as inflammation and oxidative stress, which aggravate health status and make treatment more challenging. Understanding this interaction is crucial for developing effective management strategies for both diseases. This study explores the multifaceted aspects of obesity and periodontitis and their reciprocal relationship.

Introduction

Obesity and periodontitis are serious public health issues that increase the burden of general health and chronic illnesses ( 1–3 ). Obesity, characterized by the abnormal accumulation of body fat, is linked to comorbidities such as insulin resistance, cardiovascular diseases, and certain cancers ( 1 , 4 ). It induces a low-grade chronic inflammatory state, releasing proinflammatory mediators that may link it to periodontitis ( 5 , 6 ).

Periodontitis is a chronic inflammatory disease caused by microbial-host interactions. It destroys tissue by affecting the supporting structures of teeth ( 7 , 8 ) and impacts overall wellbeing ( 9 ).

The bidirectional relationship between obesity and periodontitis is complex and multifaceted. Adipose tissue functions as an endocrine organ, releasing cytokines, and proinflammatory hormones that contribute to systemic inflammation and oxidative stress—common pathophysiological mechanisms shared by both conditions ( 7 ). Epidemiological studies support the notion that obesity is a significant risk factor for the development and exacerbation of periodontitis ( 10–12 ). Likewise, several studies suggest that periodontitis may increase obesity-related disorders such as intestinal dysbiosis ( 13 ) and insulin resistance ( 14 , 15 ).

Understanding the connection between obesity and periodontitis is crucial, as both conditions are highly prevalent worldwide. Examining their relationship not only has implications for oral health but may also reveal the mechanisms underlying a variety of systemic diseases, providing opportunities for preventive, and therapeutic interventions that could significantly improve the population’s overall health.

This narrative review explores the multifactorial aspects of obesity and periodontitis and their bidirectional relationship. It examines the interplay between these conditions, from inflammatory responses and oxidative stress to changes in periodontal microbiota and their impact during pregnancy or after bariatric surgery. Furthermore, the article delves into the implications of both non-surgical and surgical periodontal therapies in patients with obesity, emphasizing the need for comprehensive approaches to prevention and treatment.

Understanding the intricate connections between obesity and periodontitis is crucial for developing effective strategies to manage these interrelated conditions. As research continues to uncover the complexities of this relationship, healthcare practitioners can enhance their knowledge to provide more targeted interventions, ultimately improving the overall health outcomes of individuals affected by obesity and periodontitis.

For this narrative review, we considered publications from 1977 to 2023. The search was conducted through PubMed and Cochrane Library, using a combination of related search terms, including “periodontitis,” “obesity,” “oxidative stress,” “inflammatory response,” and “periodontal treatment.” Three research team members (CR-G, JMP-V, and DS-R) reviewed the articles by titles and abstracts, selecting them for full review only if all authors agreed on their relevance. Additionally, the research team examined the references from the identified articles to incorporate additional relevant publications. Ultimately, we reviewed 33 observational studies, seven cohort studies, three case–control studies, 20 systematic reviews, eight clinical trials, 63 reviews, and 13 studies with other designs, such as animal studies or conference reports. The chosen articles underwent a comprehensive content analysis to determine evidence of the relationship between periodontitis and obesity.

Obesity is a severe medical condition worldwide ( 1 ) characterized by excessive or abnormal accumulation of body fat, which increases the risk of several chronic diseases ( 3 ). It is primarily classified by body mass index (BMI), calculated as weight in kilograms divided by the square of height in meters (kg/m 2 ), with obesity defined as a BMI of 30 or higher ( 16 ).

In the past three decades, the prevalence of obesity has increased at an alarming rate, with a 27.5% increase in adults and a 47.1% increase in children ( 4 ). The exact cause of obesity remains elusive; however, it appears to involve a complex interaction of biological, psychosocial, and behavioral factors, including genetic composition, metabolic disorders, physical inactivity, socioeconomic status, a high-calorie diet, and cultural influences ( 4 , 17 ).

Obesity is associated with numerous comorbidities affecting almost all body systems, such as insulin resistance, type 2 diabetes mellitus, hepatic steatosis, cardiovascular disease, hypertension, cerebrovascular accidents, lipid metabolism disorders, gallbladder problems, osteoarthritis, sleep apnea, and other respiratory problems ( 1 , 4 , 18 , 19 ). It is also linked to certain types of cancer, including breast, ovarian, endometrial, prostate, liver, gallbladder, kidney, colon, and thyroid cancers ( 1 , 4 , 20–23 ).

A key aspect of obesity is its role in inducing a state of low-grade chronic inflammation ( 24 ) and its association with inflammatory markers related to systemic disease ( 5 , 6 ). In addition to storing energy, adipose tissue functions as an active endocrine organ, secreting various chemical mediators ( 25 ). These factors include leptin, cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukins, adiponectin, complement components, plasminogen activator inhibitor-1, proteins of the renin-angiotensin system, and resistin ( 25–27 ). Some of these substances, like cytokines, play a critical role in systemic inflammation ( 5 ) and may serve as a link between obesity and other inflammatory conditions such as periodontitis ( 28 ).

Periodontitis

Periodontitis is a chronic, non-communicable inflammatory disease that results from the interaction between pathogenic microorganisms and the host’s immune system ( 7 ). This condition destroys the tissues surrounding and supporting the tooth, including the gums, alveolar bone, and periodontal ligament ( 8 ), as a consequence of the release of proinflammatory mediators ( 29 ). The most common signs of this disease include gingival inflammation, loss of alveolar bone, dental mobility, increased probing depth, and gingival bleeding ( 2 , 30 ).

The global oral health status report estimated that severe periodontal diseases affect approximately 19% of the global adult population, accounting for over 1 billion cases worldwide ( 9 ). This has made the disease a significant public health issue that causes disability, negatively impacts chewing and aesthetics, and reduces quality of life ( 2 , 31 ).

According to the National Health and Nutrition Examination Survey of the United States, 42% of adults had periodontitis by 2014 ( 32 , 33 ), indicating that although the disease can appear from the age of 15, its prevalence increases with age, with older adults being the most vulnerable group where more aggressive forms are presented ( 9 , 30 ).

Various factors can disturb the natural balance in the mouth, leading to a shift in the biofilm beneath the gums towards proinflammatory dysbiosis. This imbalance involves excessive growth of microorganisms such as Porphyromonas gingivalis , Tannerella forsythia , and Treponema denticola , triggering chronic inflammation ( 34–36 ).

These bacteria colonize host tissues and evade defense mechanisms. Porphyromonas gingivalis fimbriae binds to other bacteria, such as Treponema denticola , and human proteins, such as glyceraldehyde-3-phosphate dehydrogenase, to facilitate adherence and invasion of host cells ( 37 ). The macromolecules that comprise the biofilms produced by these bacteria maintain proximity between bacterial and host cells, promoting health and disease ( 38 ).

They have also created several ways to obtain iron from the host environment, which is essential for their growth and contributes to biofilm dysbiosis ( 39 ). In addition, flagella-assisted motility allows these pathogens to seek nutrients and colonize favorable niches. At the same time, their metabolic activity and rapid growth enhance their ability to resist natural removal and mechanical debridement ( 38 ).

Another protective mechanism of these microorganisms is the production of capsules that prevent phagocytosis and release proteases that affect chemotaxis and neutrophil activation to evade host defense mechanisms. Porphyromonas gingivalis can also release outer membrane vesicles that scavenge interleukin-8 (IL-8), thereby protecting itself from host defense systems ( 40 ). In addition, bacteria such as Porphyromonas gingivalis , Tannerella forsythia , Aggregatibacter actinomycetemcomitans , and Fusobacterium nucleatum can invade host cells and escape the immune system ( 38 ).

Finally, bacterial exotoxins and endotoxins contribute to the virulence of these pathogenic species by damaging host cells and promoting the release of inflammatory cytokines. Enzymes, such as collagenases and gingipains from Porphyromonas gingivalis , destroy tissue components and host defense molecules ( 41 ).

This change in the microbiome can trigger periodontitis in susceptible individuals, characterized by an inadequate inflammatory response and the consequent destruction of connective tissue and alveolar bone ( 42 , 43 ).

Periodontitis is a multifactorial disease. Various risk factors are associated with the onset of periodontitis that can affect the relationship between the host and microorganisms. Smoking is the most significant risk factor ( 44–47 ), along with metabolic diseases like diabetes mellitus ( 48–51 ), obesity ( 7 , 10 , 52 ), stress ( 53 , 54 ), genetic factors ( 55 ), and oral hygiene habits ( 56 ).

Inflammatory response

Inflammation is the immune system’s biological response to organic, chemical, or physical stimuli to protect living organisms from harmful factors, including fungi, viruses, and bacteria ( 57 ). In its controlled form, as in acute inflammation, this process is crucial in eliminating pathogens, cellular debris, and inflammatory mediators while stimulating tissue repair. This leads to the resolution of inflammation and the restoration of tissue homeostasis ( 57 , 58 ).

In the acute phase of the inflammatory response, immune system cells, including platelets and granulocytic cells such as basophils, mast cells, neutrophils, and eosinophils, become activated and subsequently produce and release a variety of chemical mediators, including cytokines, chemokines, and acute-phase proteins ( 59 ). These substances promote vasodilation and increase vascular permeability, facilitate the migration of immune cells to the site of inflammation, and stimulate and regulate the inflammatory response ( 59 , 60 ). Depending on the extent of the injury, this acute phase may be sufficient to resolve the damage ( 61 ).

Conversely, failure to resolve inflammation and persistent inflammation, either as a result of prolonged exposure to a stimulus or a persistent pathogen, non-degradable foreign bodies, or an inappropriate autoimmune response against self-cells, can lead to the chronic phase of inflammation in which tissue damage ( 60 , 61 ), fibrosis and granuloma formation can occur ( 60 ). The mechanisms involved in chronic inflammation contribute to the development of many diseases, including arthritis, asthma, atherosclerosis, autoimmune diseases, type 2 diabetes mellitus, cystic fibrosis, inflammatory bowel disease, Parkinson’s disease, Alzheimer’s disease, cardiovascular diseases, cancer, and conditions associated with aging ( 57 , 61 , 62 ).

In obesity, chronic inflammation is marked by elevated levels of pro-inflammatory cytokines such as TNF-α, interleukin-1 beta (IL-1β), and interleukin-6 (IL-6), primarily produced by adipose tissue-derived macrophages ( 63 ), and by the adipose tissue itself, as previously mentioned ( 25 ). Furthermore, various factors currently under investigation can exacerbate the inflammatory process. Among these, non-esterified fatty acids may induce inflammation through mechanisms such as modulation of adipokine production or activation of Toll-like receptors; excess nutrients and adipocyte expansion can cause endoplasmic reticulum stress; and hypoxia in hypertrophied adipose tissue could stimulate the expression of inflammatory genes and activate immune cells ( 64 ). In contrast, in periodontitis, chronic inflammation originates from a complex immune response triggered by persistent microbial elements in the oral cavity, causing local damage and systemic effects ( 65 ), suggesting a potential interaction with the systemic inflammation observed in obesity ( 28 ).

The bidirectional relationship between obesity and periodontitis

The intricate connection between obesity and periodontitis has emerged as a crucial research area in periodontal medicine. Adipose tissue, acting as an endocrine organ, releases cytokines and proinflammatory hormones, known as adipocytokines, triggering inflammatory processes and oxidative stress disorders ( 7 , 29 , 66 ). This generates a shared pathophysiology between both diseases. Explored through epidemiological studies and clinical trials, this link reveals a bidirectional relationship between obesity and periodontitis ( 10–12 ), where exacerbated proinflammatory factors worsen the severity of both conditions.

Since the early reports of the relationship between obesity and periodontitis in animals in 1977 ( 67 ) and in humans in 1998 ( 66 ), numerous studies have supported the hypothesis that obesity constitutes a risk factor for the development and worsening of periodontitis. Epidemiological research results indicate that individuals with obesity show a higher prevalence of periodontal disease compared to the normal-weight population ( 11 ). Furthermore, the strength of this correlation seems to intensify with an increase in obesity ( 11 , 12 ).

During obesity, adipose tissue increases, and adipocytes secrete fewer anti-inflammatory substances, such as adiponectin, while increasing the secretion of proinflammatory substances, such as leptin and chemokines ( 68 ). This leads to an infiltration of immune cells, likely early arrivals being B and T cells, influencing the secretion of proinflammatory cytokines and Interferon gamma (IFN-γ), essential for activating macrophages and inflammation. Inflammation in obesity is characterized by the abnormal presence of these cytokines, which may hinder the elimination of pathogenic microorganisms in the oral cavity ( 69 ). And induce the destruction of characteristic periodontal connective tissue and bone ( 70 ).

Inflammatory biomarkers such as IL-1, IL-6, TNF-α, and matrix metalloproteinases (MMP) ( 63 ) play a crucial role in the relationship between obesity and periodontitis ( 71 ). Elevated levels of these biomarkers, commonly associated with obesity, correlate with losing the extracellular matrix, inhibiting osteoblastogenesis, and activating osteoclasts, leading to collagen and bone destruction ( 8 , 72 ).

Several studies have analyzed the cytokine profile in the crevicular fluid of patients with and without obesity and chronic periodontitis. Some have reported significantly higher levels of these proinflammatory substances in patients with obesity ( 73–76 ). Others show no differences between these two groups ( 77–79 ), highlighting the need for further analysis of the effects of obesity control on the cytokine profile in crevicular fluid and other fluids of patients with obesity and periodontal disease ( 71 ) ( Figure 1 ).

Relationship between obesity and periodontitis. Inflammation in periodontal disease, mediated by the release of cytokines such as IL-6, TNF-α, and IL-1β, can be exacerbated in individuals with obesity due to a systemic proinflammatory state. This inflammatory condition contributes to dysbiosis and oxidative stress, worsening periodontitis. Additionally, chronic periodontal inflammation can negatively influence metabolic disorders and increase the risk of pregnancy complications, perpetuating a negative feedback cycle that impacts both oral and systemic health. Created with BioRender.com .

Our understanding of these findings enables us to deduce that obesity and periodontitis are related. That being said, more research is necessary to ascertain whether these two disorders are causally related.

Obesity and bone loss

Initially, it was believed that obesity stimulated bone formation ( 80 ), but now the available evidence supports that obesity induces changes in bone density and affects periodontal health ( 81 ).

The increase of fatty tissue in the bone marrow acts as an endocrine organ that secretes various pro-inflammatory adipokines such as leptin and resistin while decreasing the secretion of anti-inflammatory substances such as adiponectin ( 82 ). These pro-inflammatory adipokines induce a chronic low-grade inflammatory state characterized by the elevation of inflammatory biomarkers such as TNF-α and IL-6, which increase osteoclastic function and reduce osteoblast formation—leading to increased bone resorption and decreased bone mineral density ( 83–85 ).

Similarly, obesity can trigger changes in the intestinal microbiota, affecting bones, including the jaw, through pathobionts or circulating metabolites that stimulate bone resorption ( 86 ).

On the other hand, studies addressing the relationship between obesity and alveolar bone loss are scarcer but also present obesity as an established risk factor for periodontitis ( 10 ). Several animal studies have reported that obesity and dyslipidemia ( 87 ), as well as a diet high in carbohydrates and palmitic acid ( 88 , 89 ), contribute to increased bone loss in Porphyromonas gingivalis -induced periodontitis ( 89 ). This includes deterioration of trabecular bone architecture, decreased cortical bone density in the alveolar bone area, and increased serum leptin levels ( 90 ).

Another significant finding is that individuals with obesity are more susceptible to alveolar bone loss, clinical attachment loss, and, consequently, edentulism ( 12 ) compared to those without obesity ( 91 ). Obesity-induced systemic inflammation may interfere with eliminating pathogenic microorganisms in the oral cavity, promoting the destruction of periodontal connective tissue and alveolar bone. The release of proinflammatory cytokines and oxidative stress contribute to the progression of periodontitis in individuals with obesity, exacerbating the destruction of periodontal tissue ( 82 ). In addition, factors such as subgingival calculus, probing depth greater than 4 mm, and bleeding on probing are more frequent in patients with obesity ( 92 ), suggesting that obesity could be a significant risk factor, even in patients with clinically healthy periodontium ( 93 ).

These mechanisms underscore the need for a comprehensive approach to address obesity, bone density, and periodontal health.

Oxidative stress

Oxidative stress is an imbalance between reactive oxygen species (ROS) and the body’s antioxidant systems, causing damage to proteins, lipids, and DNA ( 94 ). This condition can act as a defense mechanism of the immune system against the presence of bacteria, such as those causing periodontitis ( 95 ). After periodontal pathogenic bacteria trigger host defense responses in the biofilm, neutrophils become the most common inflammatory cells in the periodontal tissue and gingival crevice. Neutrophils are believed to be the primary sources of ROS in periodontitis ( 96 ).

The interplay between periodontitis, obesity, and oxidative stress is a significant area of study that highlights the complex interactions contributing to chronic inflammatory conditions. Oxidative stress exacerbates both conditions, leading to cellular and tissue damage ( 97 ).

Recent studies have shown that oxidative stress plays a crucial role in the pathogenesis of both periodontitis and obesity ( 98 ). Excessive adipose tissue in individuals with obesity increases ROS production, which induces oxidative damage in gingival tissues, contributing to periodontal destruction and alveolar bone loss. This oxidative damage is more pronounced in patients with obesity compared to those of average weight, indicating a strong link between obesity and periodontal oxidative stress ( 97 , 99 ).

Another study highlighted higher oxidative stress markers, such as myeloperoxidase and nitric oxide, in the gingival crevicular fluid of individuals with obesity and periodontitis. These markers are associated with increased inflammation and tissue destruction in periodontal disease ( 97 ). Additionally, the study found that non-surgical periodontal therapy significantly reduced these oxidative stress markers, suggesting that periodontal treatment can mitigate oxidative damage and improve periodontal health in patients with obesity ( 97 , 100 ).

Evidence also suggests that periodontitis can influence systemic oxidative stress, causing a sustained inflammatory response that may contribute to insulin resistance, a common phenomenon in obesity ( 99 ). This resistance can affect glucose metabolism and appetite regulation, contributing to weight gain ( 97 ) ( Figure 2 ).

Impact of obesity on periodontal inflammation: a bidirectional cycle of damage. In obesity, adipose tissue acts as an endocrine organ releasing inflammatory substances such as TNF-α, IL-1β, and IL-6, leading a dysbiosis that contributes to periodontal inflammation and exacerbation of periodontitis, resulting in the destruction of periodontal tissue and bone loss. Chronic inflammation is also associated with metabolic complications like insulin resistance, creating a bidirectional cycle of inflammation and damage between obesity and periodontitis. Created with BioRender.com .

This interaction underscores the need for comprehensive therapeutic approaches addressing periodontal and systemic health. Periodontal therapy and lifestyle modifications can mitigate the adverse effects of these chronic conditions by reducing oxidative stress and managing inflammation.

Periodontal microbiota

The periodontal microbiota and obesity are closely related through a process of dysbiosis, an alteration in the composition of the oral microbiome that can exacerbate periodontitis and be influenced by the individual’s obesity status.

Periodontitis is characterized by a dysbiotic oral microbiome characterized by an increase in periodontal pathogens such as Porphyromonas gingivalis , Aggregatibacter actinomycetemcomitans , and Tannerella forsythia ( 101 ). In patients with obesity, a higher prevalence and severity of periodontitis are observed, which is related to an altered microbial composition in the oral cavity ( 102 , 103 ).

Obesity contributes to the dysbiosis of the subgingival microbiome due to several factors, including systemic inflammation and altered immune response. Excess fatty tissue in individuals with obesity produces inflammatory mediators and ROS, affecting systemic metabolism and periodontal health. Several studies have reported an increase in the proportion of Tannerella forsythia in subgingival plaque and Porphyromonas gingivalis in the saliva of patients with obesity compared to those without obesity ( 86 ), which exacerbates gingival inflammation and reduces the effectiveness of periodontal treatment in these patients ( 103 , 104 ).

Conversely, periodontal inflammation can also contribute to systemic inflammation ( 104 , 105 ), exacerbating obesity and its metabolic complications, such as insulin resistance and chronic inflammation, which are common in obesity ( 106 ). Periodontal inflammation can contribute to intestinal dysbiosis ( 107 ), creating a vicious cycle perpetuating poor oral and systemic health ( 108 ). This bidirectional link underscores the importance of addressing oral health and obesity in an integrated manner to improve clinical outcomes.

Interventions such as periodontal therapy and lifestyle modifications are crucial to breaking this cycle of dysbiosis and inflammation. Including dietary strategies, regular exercise, and reasonable oral hygiene control can help restore microbial balance and reduce the impact of obesity on periodontal health.

Periodontitis in pregnant women with obesity

Obesity and periodontitis are both health concerns that interact in complex ways, particularly affecting pregnant women. During pregnancy, women undergo significant hormonal, immunological, and metabolic changes essential for proper fetal development and the provision of blood, nutrients, and oxygen ( 109 ). These changes and high hormone levels impair connective tissue regeneration in the periodontium, increasing the inflammatory response in these tissues. This phenomenon may increase the proliferation of aerobic and anaerobic bacteria, thereby raising the prevalence of pregnancy-related periodontal disease ( 109 , 110 ).

Maternal obesity further complicates this scenario by inducing systemic immunological and inflammatory changes that may exacerbate pregnancy’s inherent inflammatory state ( 111 ). This altered immune response can increase susceptibility to infections and excessive immunological reactivity, influencing the severity of maternal periodontitis ( 112 ).

Several studies have shown a positive association between obesity and periodontal disease ( 109–111 , 113 , 114 ), suggesting that both conditions may synergistically increase the inflammatory and oxidative state in pregnant women. This is reflected in an increase in local and systemic biomarkers ( 111 ) and could lead to an increase in complications associated with maternal obesity, such as gestational diabetes mellitus, hypertension, placental abnormalities, pre-eclampsia, prematurity, fetal death, and spontaneous abortion ( 109 , 111 ). These adverse outcomes are believed to be linked to direct and indirect mechanisms involving periodontal pathogens and systemic inflammation. Direct mechanisms involve the translocation of oral bacteria to the placenta, triggering inflammatory responses, while indirect mechanisms involve elevated systemic inflammatory cytokines that disrupt placental function ( 115 , 116 ).

Although there is evidence of an association between obesity and periodontal disease during pregnancy, the certainty of the evidence for these associations and their implications is inconclusive. This is due to current studies’ methodological, clinical, and statistical heterogeneity, a potential risk of bias, and a lack of control for confounding factors. Therefore, new studies with research designs that use rigorous methods that minimize the risk of bias are needed to gain a better understanding and accuracy of these associations and their clinical implications.

Periodontitis in bariatric surgery patients

There are multiple types of bariatric surgery, the most common being gastric bypass, sleeve gastrectomy, and adjustable gastric banding ( 117 ). Regardless of the type of surgery performed, these surgical procedures are superior to non-surgical interventions in terms of weight loss outcomes and improvement in obesity-related comorbidities ( 118 ).

Studies investigating the relationship between bariatric surgery and periodontitis yield mixed results. On the one hand, some studies suggest that surgery is associated with improvements in various metabolic and physiological aspects of the body, including improvements in periodontal health due to a reduction in the inflammatory state and adipose tissue burden ( 119–121 ), as well as improved control of dental biofilm ( 120 , 121 ). One study found no apparent reduction in periodontitis after bariatric surgery but noted that malabsorption of critical nutrients could affect periodontal health ( 122 ). Meanwhile, two cohort studies ( 123 , 124 ) and a systematic review suggest that periodontal status may worsen in the first 6 months after bariatric surgery ( 125 ). Therefore, it is recommended to conduct periodontal evaluations and appropriately manage oral health before undergoing surgical interventions to prevent further deterioration of periodontal health post-surgery ( 123–125 ).

Non-surgical periodontal therapy in patients with obesity

The therapeutic approach to periodontitis encompasses various strategies, among which fundamental clinical interventions such as scaling and root planing stand out and are recognized as one of the pillars of non-surgical periodontal therapy. This treatment involves the meticulous removal of tartar and impurities from the root surfaces of teeth with a probing depth ≥5 mm ( 126 ).

Several studies have evaluated the effect of periodontal scaling and root planing on gingival bleeding, probing depth, and cytokine levels in patients with and without obesity and chronic periodontitis ( 127 ). While most research reports greater probing depth and higher levels of IL-1β, IL-6, TNF-α, IFN-γ, leptin, adiponectin, and CRP in patients with obesity compared to those with normal weight ( 127–130 ), the effects of periodontal therapy are inconclusive. Subgroup analysis in specific studies has provided a deeper insight into how obesity and periodontitis interact. In some instances, treatment decreases serum levels of proinflammatory substances in patients with obesity. Still, after 3 months of follow-up, high levels of IL-6 and tumor necrosis factor-α are observed in this patient group ( 131 ). Resistin, another proinflammatory mediator, exhibits higher levels in individuals with periodontitis than those without the disease. Despite efforts of periodontal treatment, resistin shows no significant changes in serum or gingival crevicular fluid levels in individuals with and without obesity over time, indicating that its proinflammatory expression persists ( 127 , 129 , 131 ).

In the pharmacological realm, various studies assert that controlled administration of antibiotics can play a significant role in managing the bacterial load associated with periodontitis ( 132–135 ) and leads to significant improvement in treatment by reducing probing depth and enhancing clinical attachment ( 136 ). Specific case considerations guide the choice of antimicrobial agents, which can be administered systemically or locally ( 137 , 138 ).

Long-term maintenance is an essential treatment component, involving regular clinical follow-up, periodontal evaluations, and periodic professional cleanings. Patient education, focusing on effective oral hygiene practices and understanding risk factors, strengthens the preventive component and contributes to the sustainability of therapeutic outcomes ( 126 ).

Surgical periodontal therapy in patients with obesity

Regarding surgical periodontal therapy, there are currently no studies directly comparing the outcomes of surgical periodontal therapy with non-surgical treatment in patients with obesity. However, there is evidence suggesting that patients with obesity may experience slower healing due to an exacerbated inflammatory response ( 63 , 71 ), which could affect the results of surgical interventions ( 63 ), including surgical periodontal treatment.

In addition, it is common for patients with obesity to have coexisting comorbidities that may complicate surgical periodontal therapy ( 72 , 74 ). This intersection of health conditions highlights the need for a comprehensive and personalized approach to the periodontal management of these patients. Based on the available evidence, non-surgical periodontal therapy may be preferable to minimize postoperative morbidity in this patient population ( 73 , 75–77 ).

The results of this review indicate that obesity and periodontitis are interrelated through inflammatory and oxidative stress mechanisms, generating a cycle where each condition may aggravate and perpetuate the other. Adipose tissue, acting as an endocrine organ, triggers inflammatory responses that affect periodontal tissues, and the chronic inflammation associated with periodontitis may contribute to the metabolic imbalances seen in obesity. However, the causal relationship between these two pathologies is unclear.

Many studies suggest that obesity is a significant risk factor for periodontitis and that there could be a dose–response relationship associated with body mass index ( 10 , 129 , 139 , 140 ). However, other studies that consider the type of obesity only associate altered periodontal parameters with abdominal obesity and discard the relationship between general obesity and gingival attachment loss and bleeding ( 128 , 141 ).

Another factor analyzed in this study was the level of cytokines present in patients with and without obesity and periodontitis. While there are studies that reported considerably high levels of IL-8, IL-1 β, TNF- α, progranulin, monocyte chemoattractant protein-4 (MCP-4), lipocalin, and resistin ( 73–76 , 142 ), other investigations report no difference in the levels of these biomarkers in both subgroups ( 77–79 ). This variability in the results may be because the studies that reported comparable levels of pro-inflammatory substances in patients with and without obesity and periodontitis did not consider other factors such as systemic diseases, smoking, or the depth of periodontal probing.

It is also essential to evaluate cytokine and adipocytokine levels in different biological fluids, such as saliva, gingival crevicular fluid, and serum. While saliva and gingival crevicular fluid are more specific indicators of local periodontal conditions, serum provides a more comprehensive view of the organism ( 71 ). The choice of biological fluid can influence the interpretation of results, highlighting the need for comprehensive approaches in periodontal and obesity research.

The results related to the impact of obesity on periodontal treatment are diverse. Some authors suggest that clinical attachment levels and probing depth are comparable in subjects with and without obesity after non-surgical periodontal treatment ( 131 , 143 ). At the same time, other investigations reported that patients with obesity have a lower response to periodontal therapy compared to those with normal weight ( 143–145 ), highlighting the negative effects of chronic inflammation on the periodontium. This variability calls for studies with higher methodological quality to evaluate the clinical impact of periodontal therapy in patients with obesity in the long term. Conversely, some studies indicate that periodontal treatment can improve the lipid profile ( 146 ), positively impacting obesity control.

Obesity and periodontal disease during pregnancy may also be associated. Still, the evidence is not definitive because of methodological and statistical heterogeneity, potential biases, and the inability of current research to control for confounding factors. More rigorous research is needed to clarify these associations and their clinical implications.

Regarding bariatric surgery, it has been reported that patients who lost weight after this intervention significantly improved periodontal health compared to those who did not undergo surgery ( 147 ). These results indicate that individualizing nutritional counseling, physical exercise for weight reduction, and periodontal therapy in this group of patients is imperative to improving oral and general health ( 147 ).

It should also be noted that evidence on the results of surgical periodontal therapy in patients with obesity is limited. There are no studies that directly compare the clinical effects of surgical and non-surgical periodontal treatment in patients with obesity, but the possible exacerbated inflammatory response in patients with obesity could influence the speed of healing and the results of surgical interventions, suggesting that non-surgical therapy could be preferable in this group ( 63 , 71 ).

This review had certain limitations that must be considered. Firstly, the heterogeneity of the included study designs generates variability in the results, making it difficult to generalize the conclusions. Differences in study populations, methodologies, and outcome measures contribute to this heterogeneity. Additionally, the potential for various biases exists, such as selection bias, reporting bias, and confounding factors that were not consistently controlled across studies. These biases can affect the validity and reliability of the findings. The lack of control for confounding variables in observational studies significantly limits the ability to establish a causal relationship between both pathologies. Many studies did not report controlling for confounding factors like systemic diseases, smoking, dietary habits, and physical activity, which could influence the observed relationships.

Secondly, the scarcity of longitudinal designs also represents a weakness since the temporal dynamics in the relationship between obesity and periodontitis cannot be assessed. Longitudinal studies are essential to determine the directionality and causality of the observed relationship over time.

It is important to proceed cautiously when extrapolating these results. Most evaluated investigations were carried out in particular populations, frequently in specific geographical areas or clinical situations. Diverse populations possess varying genetic, environmental, and lifestyle components, which may impact the generalizability of the findings in broader settings. For example, dietary habits, socioeconomic status, and healthcare access can all significantly impact periodontal health and obesity.

Future studies should strive to include varied populations from various socioeconomic backgrounds and geographic locations to improve the generalizability of the results. They should also look at how these correlations appear in particular subgroups, such as older people and other ethnic groups, to create tailored interventions that take into account their specific requirements.

Despite the limitations, this review presents several strengths. The breadth of the research, addressing aspects ranging from inflammatory mechanisms to outcomes in specific groups such as pregnant women and patients undergoing bariatric surgery, provides a comprehensive view of the relationship between obesity and periodontitis. Additionally, analyzing multiple factors, such as the potential causal relationship and responses to different available treatments, enriches the understanding of the interaction between periodontitis and obesity.

Several directions for future research are suggested to advance the understanding of this relationship. Prospective and longitudinal studies with long-term follow-ups are essential to establish causality and comprehend temporal dynamics. Focusing on specific populations, such as pregnant women, patients after bariatric surgery, and the younger population, will allow for more targeted therapeutic approaches. Exploration of modifying factors like genetics and the environment can provide valuable information for personalized therapeutic strategies.

In the realm of clinical practice, the analysis of the relationship between obesity and periodontitis has significant implications. A comprehensive patient assessment, considering obesity as a risk factor in periodontal evaluation, is recommended, especially in more susceptible populations such as pregnant women. A multidisciplinary approach involving healthcare professionals, including dentists, nutritionists, and surgeons, may be essential for effectively managing oral and general health in patients with obesity. Furthermore, patient education on the relationship between obesity and periodontitis and maintaining healthy habits can enhance awareness and promote prevention.

In conclusion, the relationship between obesity and periodontitis is multifaceted and complex, involving inflammatory and oxidative stress mechanisms. The evidence suggests that obesity significantly increases the risk of developing and exacerbating periodontitis, with elevated inflammatory biomarkers in patients with obesity, even during pregnancy. The response to periodontal treatment varies, with some improvements seen post-bariatric surgery, though evidence on surgical therapy outcomes is limited. Study heterogeneity and uncontrolled confounding factors limit the generalizability of findings. Further research is needed to understand the underlying mechanisms and develop more effective therapeutic strategies for periodontitis and obesity. Collaboration between periodontal health professionals and obesity experts is essential to moving toward integrated and personalized approaches to managing these interrelated conditions.

Author contributions

CR-G: Conceptualization, Methodology, Writing – original draft, Writing – review & editing, Investigation, Project administration. JP-V: Writing – original draft, Writing – review & editing, Methodology. NG: Writing – review & editing. AS-R: Methodology, Writing – review & editing. RZ-V: Writing – review & editing. AZ: Supervision, Validation, Writing – review & editing. DS-R: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Validation, Writing – review & editing, Investigation.

Acknowledgments

The authors are grateful to Universidad UTE for their support.

Funding Statement

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Universidad UTE covered the publication fee.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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