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INTRODUCTION
Celiac disease (CD) is a chronic inflammatory condition of the small intestine involving both an innate and adaptive immune response that occurs among genetically predisposed individuals exposed to gluten-containing foods and other environmental factors [1]. While CD was once considered rare, the global prevalence of CD is now estimated to be 0.5%–1.0%, making it one of the most prevalent autoimmune disorders [1]. Patients with diabetes or other autoimmune disorders have an even higher risk of developing CD, partially due to shared human leukocyte antigen (HLA) typing. CD has a strong hereditary component with evidence of high familial recurrence and the relevant role of HLA class II heterodimers, DQ2 and DQ8 in heritability [1]. The pathophysiology behind CD involves the ingestion of gluten-derived peptides such as gliadin impairing intestinal mucosal surfaces leading to abnormal absorption of nutrients [2]. This leads to a wide range of gastrointestinal and systemic symptoms such as chronic abdominal pain, diarrhea, steatorrhea, aphthous mouth ulceration, dermatitis herpetiformis, cerebellar ataxia and many more [3]. Due to the highly variability of symptoms in CD, adults with this disorder are often underdiagnosed or misdiagnosed [4]. Gliadin interacts with intestinal cells triggering the disassembling of inter-enterocyte tight junctions, increasing gut permeability [5]. The increase in permeability leads to increased activation of T-lymphocytes producing pro-inflammatory cytokines [5]. Recent studies have shown that gut microbiota has a well-established role in gluten metabolism, modeling the immune response, and regulating the permeability of the intestinal barrier [6]. Research has shown that individuals with CD often exhibit differences in the composition of their gut microbiome leading to a significant absence of specific bacterial species [6]. Specifically, Lactobacilli and Bifidobacterium spp. may play a role in the breakdown of gluten leading to increased or decreased immunogenicity influencing autoimmune risk in individuals [7]. This may further contribute to worsening immune dysregulation and systemic inflammation that is present in CD.
As we continue to explore the complex interactions between CD and the gut microbiome, obesity has been recognized as a multifaceted condition that also has an intricate relationship with the gut microbiome [8]. Obesity is defined as when a person has a body mass index greater than or equal to 30 [9]. While the pathophysiology of obesity remains open to several possible mechanisms, the traditional view is characterized by the excessive accumulation of body fat, generally stemming from imbalances in calorie intake and expenditure [10]. The excess energy stored in fat cells leads to alteration in nutrient signals responsible for obesity. Obesity is known to affect over a third of the world’s population today with approximately 36% of the U.S. population considered obese due to various genetic and lifestyle factors impacting this condition [11]. In recent years, research studies have indicated that the gut microbiome plays a pivotal role in the development of obesity as well [8]. The gut microbiome is composed of bacteria, viruses, fungi, and numerous microorganisms which impact digestion, nutrient absorption, and immune function [12]. It has been identified that individuals with obesity exhibit distinct differences in their gut microbiome compared to those of a healthy weight [8].
The autoimmune response to gluten in CD can lead to changes in the gut microbiome, which may be contributing to excessive inflammation and intestinal damage [6]. The reduction in microbial diversity and overrepresentation of certain bacterial species may be potentially contributing to an increased propensity for weight gain, leading to an increased risk of obesity. While the precise mechanism between microbiome alterations in CD and obesity are still under investigation, it is evident that there are multiple factors including gut microbiome influencing the 2 conditions.
In this paper, we aim to investigate the prevalence of obesity in patients diagnosed with CD. We will discuss the alterations in the gut microbiome observed in CD and explore the existing evidence which suggest intriguing connections with obesity. In addition to the gut microbiome theory, we will discuss further proposed mechanisms for the relationship between CD and obesity in relation to inflammatory pathways and beta-oxidation pathways. We believe our results add significant findings to support further epidemiological and biomedical studies regarding the complex relationship between gut microbiome composition, CD, obesity, and the relevant pathophysiological pathways connecting both obesity and CD.
METHODS
Baseline data from a large national database, All of Us (AoU) Research Program, was used to evaluate the relationship between CD and obesity. The AoU Workbench is a cloud-based platform with Systematized Nomenclature of Medicine (SNOMED) codes and electronic health records (EHR) used to identify data. Disclosure of group counts under 20 was not included per the AoU Data and Statistics Dissemination Policy. Participants 18 years of age or older are included from over 340 recruitment sites. Consented data includes health surveys, EHR data, physical measurements such as systolic and diastolic blood pressure measurements, height, weight, heart rate, waist and hip measurements, wheelchair usage, and current pregnancy status. Participant privacy was maintained through a series of data transformations series. Race, sex, and age ranges are categorized by the AoU Research Program Database. The Workbench was utilized for selecting groups of participants (cohort builder), creating datasets for analysis (dataset builder), and analyzing the data using Jupyter Notebooks. Saved datasets and direct queries were completed using R and Python 3 programming languages. R version 4.0.3. was used to perform the analysis. Excel was used to create figures and display the prevalence of obesity amongst CD patients with 95% confidence intervals.
A retrospective cohort study was conducted with individuals with CD and without CD who were matched by inclusion and exclusion criteria including age, health surveys, CD, and obesity. Four different groups were identified by selecting for and excluding certain criteria. Group 1 included patients with obesity and CD; group 2 included patients with CD, without obesity; group 3 included patients with obesity, and without CD; and group 4 included patients without obesity and CD. The SNOMED codes for CD included CD and adult form of CD. We used the overarching diagnosis of CD as defined by the National Institute of Health (NIH) AoU Research Program Database, which encompasses all specific diagnoses related to the disease within its coding structure. This standardized definition ensures inclusion of individuals recognized as having CD according to NIH AoU Research Program Database guidelines. The SNOMED codes for obesity included obesity, morbid obesity, maternal obesity syndrome, maternal obesity complicated pregnancy, childbirth, obesity in mother complicating childbirth, obesity by fat distribution pattern, localized adiposity, severe obesity, simple obesity, obesity by age of onset, childhood obesity, generalized obesity, adult-onset obesity, central obesity, lifelong obesity, constitutional obesity, obesity by contributing factors. The prevalence of obesity amongst patients with CD was identified and analyzed. Prism statistical software was used to conduct chi-square analysis for identifying significance and relative risk.
IRB Approval
This study was conducted with data that has undergone AoU Institutional Review Board (IRB) approval which reviews protocol, informed consent, and other participant-facing materials for the AoU Research Program. The IRB follows the regulation and guidance of the Office for Human Research Protections for all studies ensuring that the right and welfare for research participants are overseen and protected uniformly. Informed consent for each participant was done via an e-Consent evaluation. Consent for sharing EHR was done as well with an e-Consent evaluation. The consent forms follow the primary e-Consent and HIPAA Authorizations. In order to contact the IRB, this email can be utilized: AoUIRBContact@ emmes.com. The hyperlinks to both of these consent forms are listed below: https://allofus.nih.gov/sites/default/files/appendix_primary_consent_form-stamped.pdf and https://allofus.nih.gov/sites/default/files/AoU_HIPAA_Authorization_23June2022.pdf.
RESULTS
Amongst participants with and without a history of CD, 407,333 participants were matched by age ranges and health surveys (Fig. 1). Within patients with a history of CD, the prevalence of obesity was 595 (32.2%) compared to 74,665 (18.41%) in the control group (Fig. 2). This difference was statistically significant by P<0.0001 with an (odds ratio, 2.111; 95% confidence interval, 1.914–2.328).
From a demographic standpoint, overall, females accounted for a greater proportion of patients with obesity with and without CD as 52% and 68% respectively (Table 1, Fig. 3). Additionally, there was an increased number of male patients with obesity and CD compared to those without CD with 44% and 30% respectively (Table 1, Fig. 3). When considering the age ranges, the largest percentage of patients with obesity and CD was the > 65 years age group with 48% (Table 2, Fig. 4). The largest percentage of patients with obesity and without CD was patients in the 45–64 years age group with 40% (Table 2, Fig. 4). Finally, the largest percentage of obese patients with and without a history of CD for race was the white population with 50% and 51% respectively followed by African Americans (Table 3, Fig. 5).
DISCUSSION
Our primary aim is to investigate the relationship between obesity and patients diagnosed with CD. From a sex standpoint, females accounted for a greater proportion of the patients with both conditions while there was an increased number of male patients with obesity and CD compared to those without CD. In terms of age ranges, the largest percentage of patients with obesity and CD was found to be in the > 65 years age group. From a racial standpoint, the largest percentage of obese patients with and without CD was the white population followed by African Americans. Finally, our results indicate the prevalence of obesity was significantly higher in patients with CD as opposed to patients without with 32.6% in the CD group and 18.4% in the control group. These results support our hypothesis that there is an increased prevalence of obesity in patients diagnosed with CD. Possible explanations for this include modulation of gut microbiota composition in patients with CD and is discussed in detail below.
1. The Role of Gut Microbiome in CD and Obesity Cross Talk
There are several relevant pathophysiological pathways which may possibly connect obesity and CD. The gut microbiome is comprised of a complex community of microorganisms that is influenced by factors such as diet, genetics, age, and environmental exposures [13]. Balanced and diverse microbiomes have been associated with improved health outcomes. On the other hand, disruptions of the gut microbiome equilibrium is known as dysbiosis which has been implicated in the pathogenesis of numerous disorders such as obesity and CD [14].
Recent literature has identified notable shifts in the gut microbiome of individuals with obesity. Specifically, within the obese population group, it has been noted that the ratio of Firmicutes phylum in comparison to Bacteroidetes phylum is significantly higher [15]. This finding was identified with high clinical significance. As Lactobacillus reuteri is part of the Firmicutes species, this species has been linked to alterations in gastrointestinal peptides such as gastrin, cholecystokinin, somatostatin, and ghrelin [16]. Ultimately, elevated levels of this species causing alterations in gut hormone regulation may be leading to decreased satiety, increased appetite, and obesity [16]. Furthermore, in another study examining gut microbial community structure in undernourished and obese Mexican children, the Proteobacteria phylum was found to be overrepresented in the obese population group [17].
Dysbiosis in CD can lead to modification of the mucosal barrier with concomitant persistent immune system activation [14]. Noteworthy findings in literature have identified the presence of excessive Gram-negative bacteria within the gut microbiome of genetically susceptible individuals for CD, potentially contributing to the loss of glucose tolerance [7]. Similar to what has been noted in the context of obesity, individuals with CD exhibit an increased abundance of Gram-negative species, specifically within the Proteobacteria phylum [17]. More specifically, Proteobacteria was found amongst the majority of children diagnosed with CD [17]. In adults with CD, the Firmicutes species has been recognized as the most abundant bacteria [7].
Based on this data, the gut microbiomes of individuals with CD and obesity have several findings that may potentially assist in the development of therapeutic treatments to modulate gut microbiome compositions. Both conditions identified gut microbiomes with high levels of the Firmicutes phylum which includes the Lactobacillus species [7,15]. Additionally, the Proteobacteria phylum was overrepresented amongst obese individuals and children with CD [17].
As probiotics have been identified as a potential treatment option for both obesity and CD, these insights regarding the gut microbiome may play a significant role for future treatment modalities [18,19]. Probiotics have demonstrated to diminish inflammatory factors such as interleukin-6 and tumor necrosis factor-alpha, lowering inflammation, a factor that is highly pivotal in the pathophysiology of both obesity and CD [15]. These probiotics prevent overgrowth of the pathogenic bacteria in the flora and improve intestinal epithelial barrier while reducing gut permeability [20]. Probiotics formulations, particularly those rich in Lactobacillus and Bifidobacterium have been found to benefit against Gram-negative gut bacteria observed in CD patients [15]. Since Lactobacillus has been identified to alter gut hormonal metabolism, increasing the risk for obesity, potential probiotics with decreased amounts of this species may result in increased benefit for the obese and CD population groups.
Conversely, the role of gluten-free diets as a therapeutic approach provides conflicting evidence for patients with obesity and CD. Gluten-free diets have been found to increase Gram-negative bacteria which has been identified to be elevated in obesity due to hormonal regulation [21]. On the other hand, gluten-free diets have also been associated with decrease in lactobacilli bacteria which may help combat obesity [21]. However, gluten-free diets remain the mainstay treatment for patients with CD. More recent literature has also identified gluten-free diets as a possible cause to obesity amongst adult CD patients compared to pediatric patients [22]. On the other hand, this study conversely indicated that individuals with CD typically present with a lower body mass index compared to controls primarily due to malabsorption issues. Several factors may explain this discrepancy. The meta-analysis included diverse studies with varying populations focusing on newly diagnosed or untreated CD patients who experienced weight loss due to malabsorption. However, our study may have included a significant proportion of diagnosed celiac patients adhering to a gluten-free diet. Explanations for this include that a gluten-free diet causes restoration of original gut mucosa, which results in increased absorptive capacities and an increase in weight [23]. Our data also indicated that the largest percentage of celiac patients with obesity were women greater than older than 65 years while the meta-analysis did not specifically address these demographic factors. Other theories involve gluten-free diets being rich in sugar and fat with deficiencies in fiber, all of which may be contributing to weight gain [24].
In addition to the gut microbiome theory, there are additional proposed mechanisms for the relationship between CD and obesity in relation to inflammatory pathways and beta-oxidation pathways. There is clinical evidence that suggests CD may result in organic acid abnormalities [25]. This eventually leads to slowed fat metabolism which can then lead to obesity. As patients with CD have decreased absorption, patients poorly absorb carnitine [25]. With carnitine playing a critical role in the beta-oxidation pathway for fat metabolism, this may contribute to the relationship findings between CD and obesity.
2. Influence of Sex on Obesity Prevalence in CD Patients
Based on our results, females accounted for a greater population of patients with both obesity and CD [26,27]. This falls in line with research studies that have indicated female hormones may be playing a significant role in this [28]. From a sex standpoint, females accounted for a greater proportion of the patients with both conditions while there was an increased number of male patients with obesity and CD compared to those without CD. Research suggests that hormones, particularly estrogen, modulates the immune response and is linked to higher prevalence of autoimmune conditions including CD [28]. Moreover, studies have shown that females are more likely to seek medical attention and diagnostic testing, leading to a higher rate of CD [29].
On the other hand, our results also indicated that there is an increased number of obese males with a history of CD compared to those without CD. This may be attributed to several different factors including lifestyle, dietary habits, and genetic factors. Males may have various dietary habits, such as diets rich in high processed foods, which increases their risk for obesity [30]. Some studies have found that consumption of processed foods, which often contains hidden sources of gluten, may contribute to CD development [31]. Additionally, genetic factors may predispose certain individuals to be more susceptible to both obesity and CD [32,33]. Finally, literature has identified differences in the way CD manifests in males compared to females with atypical or less severe symptoms [34]. This may have led to the underdiagnosis of CD in patients without obesity.
3. Influence of Age on Obesity Prevalence in CD
Patients Based on this study, the influence of age on obesity and CD prevalence reveals a distinctive pattern. The largest percentage of patients with obesity and CD was found to be in participants greater than 65 years old. Contrary to the prevalent notions that CD is primarily a diagnosis in younger age groups with 2 peaks at 2 years old and 20–30 years old, CD with obesity may present in a different age range [35]. This suggests that older individuals with CD may be a higher risk of developing obesity due to several factors such as dietary habits, lifestyle, and metabolic alterations. The older age group may experience a compounded effect of nutritional deficiencies due to CD’s pathophysiological mechanisms as well as decreased metabolic rates and diminished nutrient absorption capabilities due to older age making them more susceptible to obesity [36]. These findings emphasize the importance of age as a risk factor when assessing for comorbidities of CD.
On the other hand, the largest percentage of patients with obesity and without CD was found to be in the 45–64 years age range. This data follows the conventional trends in literature with a higher prevalence of obesity amongst the 45–64 age groups with the highest prevalence amongst adults aged 40–59 years old was 44.3% while 41.5% of adults were aged 60 and older [37]. The divergence in age-related trends between CD and non-CD population groups with relation to obesity supports the need for further exploration into age-specific mechanisms of comorbidities of CD.
4. Influence of Racial Identity on Obesity Prevalence in CD Patients
From a racial standpoint, the largest percentage of obese patients with and without CD was the white population followed by African Americans. The obese white population group with and without CD respectively accounted for 50% and 51% of the population compared to the obese African American population with and without CD accounting for 23%. These findings are consistent with current literature indicating CD is most commonly found in non-Hispanic white populations due several genetic and autoimmune factors such as HLA haplotypes [38]. However, this is contrary to findings that suggest racial disparities in the prevalence of CD-related obesity [39]. African Americans has been found to have the highest rates of obesity (49.6%) compared to other racial groups in America [40]. Our results lean to a more uniform distribution of CD and its comorbidities across different racial groups.
There may be several genetic, socio-economic, and cultural factors that differ amongst these racial groups, impacting lifestyle choices and dietary habits, and ultimately our results. Racial disparities in healthcare access for patients with CD should be researched further to understand the complex interplay between race, CD, and obesity.
5. Implications and Future Directions
This study offers a unique perspective on the relationship between obesity and CD. As the results from our data indicate there is an increased prevalence of obesity amongst patients with CD, there are several potential pathophysiological mechanisms that offer an explanation to this. These mechanisms include the gut microbiome, inflammatory pathways, and beta-oxidation pathways. However, our study is subject to certain limitations. As EHR’s were limited to single healthcare networks, out of network care individuals were not covered in the population groups. Furthermore, as this study employs a retrospective cohort methodology, this limits the ability to pinpoint confounding factors within the pathogenesis such as the influence of dietary patterns, physical activity levels, race, age, sex, and other health conditions.
Having established that there is an increased prevalence of obesity amongst CD patients, there are several possible avenues for future directions. Longitudinal studies focusing on the dynamic changes of the gut microbiome over time in individuals with obesity and CD may help identify the relationship between microbial shifts and disease progression. Additionally, mechanistic studies to identify how specific microbial species influence gut hormones and metabolic pathways could provide insight into better therapeutic options for patients with obesity and CD. As mentioned previously, investigating precision probiotics tailored to gut microbiome profiles may lead to more successful treatment options for patients with both obesity and CD. As CD is often undiagnosed or misdiagnosed in adults, recognizing the association between obesity and CD may assist in earlier detection techniques of CD in obese patients.
6. Conclusion
This study provides evidence of a significant association between CD and an elevated prevalence of obesity. The comprehensive analysis of patient demographics including age, sex, and race provides an understanding of the association between CD and obesity. Based on our study, the complex interplay between sex, age ranges, and racial factors with obesity and CD should be explored. Recent literature has implicated dysbiosis playing a key role from a pathophysiological in both CD and obesity. This paper highlights specific gut microbial shifts which may be involved in the increased prevalence of obesity amongst CD patients. Recognizing the influence of the gut microbiome on obesity and CD provides insight into therapeutic strategies such as precision probiotics. While the precise mechanism between microbiome alterations in CD and obesity still needs to be studied further, additional factors such as inflammatory pathways and beta-oxidation pathways may provide insight into the increased prevalence of obesity amongst CD patients. Recognizing the association between obesity and CD may facilitate earlier detection of CD in obese individuals. This study warrants further investigation into microbial changes and dietary exposures that affect the pathogenesis of both diseases with the goal of developing new therapeutic approaches.
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