Metabolic Syndrome Is Associated With Increased Risk of Clostridioides difficile Infection Diagnosis and Severe Outcomes

Abstract

Background

Clostridioides difficile infection (CDI) is a major cause of antibiotic-associated diarrhea in the United States. Gut dysbiosis and chronic inflammation are key contributors to CDI susceptibility and severity. Metabolic syndrome (MetS)—defined by central obesity, hypertriglyceridemia, low HDL cholesterol, hypertension, and type 2 diabetes mellitus (T2DM)—is increasingly prevalent worldwide and is characterized by chronic immune dysregulation and alterations in gut microbiota. These pathophysiologic features may overlap with mechanisms that predispose individuals to CDI and its complications.

Method

Using a large electronic health record database encompassing 102 health care organizations, we examined the association between metabolic conditions (MetS, obesity, and T2DM) and the risk of CDI diagnosis and severe clinical outcomes. Individuals with a diagnosis of each metabolic condition were compared with matched controls.

Results

All 3 metabolic conditions were associated with an increased risk of CDI. The strongest association was observed in patients with MetS (odds ratio [OR], 1.94), followed by obesity (OR, 1.14) and T2DM (OR, 1.11). The impact of metabolic disorders on CDI severity varied based on the specific condition. Patients with MetS and obesity were more likely to develop sepsis, leukocytosis, and neutrophilia and to require ICU admission; however, they had lower risk of hypoalbuminemia, recurrent CDI, and all-cause mortality. In contrast, patients with T2DM had greater odds of developing all of the CDI-associated complications.

Conclusions

MetS, obesity, and T2DM were all associated with an increased likelihood of CDI diagnosis. However, their effects on CDI severity varied among the 3 conditions examined—patients with T2DM had the greatest risk of adverse outcomes, including sepsis, ICU admission, recurrent CDI, and mortality.

Clostridioides difficile infection (CDI) is a nosocomial infection; it is listed as 1 of the top 5 antibiotic-related urgent public health threats in the United States [1]. CDI affects about 500 000 individuals and causes nearly 30 000 deaths annually, with community-acquired cases increasingly contributing to the overall burden [1–3]. The causative pathogen is a gram-negative, spore-forming bacterium transmitted via the fecal–oral route [4]. The clinical presentation of CDI ranges from asymptomatic colonization to mild, self-limited diarrhea to severe pseudomembranous colitis, which can cause death [5, 6]. Development of symptoms and their severity after exposure to C. difficile are determined by a complex interplay between pathogen-specific and host-related factors [7]; advanced age, antibiotic exposure, and hospitalization are established risk factors of C. difficile acquisition [8], whereas intensity and type of host immune response after infection are key determinants of disease severity and clinical outcomes [9, 10].

Metabolic syndrome (MetS) is an aggregation of different yet closely linked disorders that are increasingly prevalent worldwide [11, 12] and increase the risk of adverse outcomes following an infection [13–15]. The International Diabetes Foundation defines MetS as a combination of central obesity with 2 or more of the following: insulin resistance, dyslipidemia, hypertension, and elevated fasting blood glucose [11]. Body mass index (BMI) is the most common measure used to classify obesity based on an individual's height and weight, whereas central (abdominal) obesity refers to excess accumulation of fat around the abdomen and internal organs and is typically assessed using waist circumference [16, 17]. Although BMI is still widely used, in the past few years central obesity has emerged as a potentially superior marker of obesity-associated adverse outcomes [18, 19]. Therefore, both general obesity (BMI ≥30 kg/m²) and central obesity (waist circumference >40 inches for males, >35 inches for females) are considered a core component of MetS [17]. In addition, obesity facilitates the development of other MetS components such as insulin resistance, microbiota dysbiosis, and chronic inflammation [20–22]. Type 2 diabetes mellitus (T2DM) is a core MetS component that is closely intertwined with obesity, as ∼80%–90% of individuals with T2DM are overweight or obese [23]. Further, T2DM is characterized by impaired immune responses that may worsen infection outcomes [24]. Thus, we posit that a state of altered immune responses and gut dysbiosis in MetS, obesity, and/or T2DM can in turn impact CDI susceptibility and severity. Here, we evaluated the association between 3 metabolic conditions and risk of CDI acquisition as well as disease severity.

While some studies have studied the impact of individual MetS components (ie, obesity or T2DM) on CDI risk and severity, to date no study has examined the overall impact of MetS on CDI risk and severity. In a retrospective case–control study from Israel, obesity (defined as BMI ≥30 kg/m²) was associated with increased risk of acquiring CDI [25]. Another study from the United States showed that BMI ≥35 kg/m² was an independent risk factor for severe CDI in a tertiary health care population [6] and individuals with community-onset CDI were found to have higher BMI than the general population [26]. However, more recent studies have not replicated this association between obesity and CDI severity [27–29]. Similarly, an association between T2DM and CDI remains inconclusive: While a recent meta-analysis reported T2DM as an independent risk factor for CDI and a 10-year retrospective study from Israel found comparable results [30–32], 2 other studies reported no significant association between T2DM and either primary CDI or recurrent infection [33, 34]. Therefore, in this study, we utilized data from an extensive worldwide health care database (TriNetX) to improve our understanding of the link between metabolic diseases and CDI risk and its severity.

METHODS

Data were extracted from the TriNetX Research Network, which includes 102 health care organizations (HCOs) in 30 countries, and included all relevant patient information collected during a 5-year period before the query date of March 20, 2026. CDI cases were defined as patients with a positive C. difficile test by polymerase chain reaction (PCR) or enzyme immunoassay and/or a documented diagnosis under International Classification of Diseases, 10th Revision (ICD-10), codes A04.7 (enterocolitis due to C. difficile) or A04.72 (enterocolitis due to C. difficile, not specified as recurrent). MetS was defined as having at least 3 of 5 established criteria according to International Diabetes Foundation and American Heart Association consensus (Supplementary Table 1) [11, 35]. Patients were classified as having MetS if they had a documented ICD-10 code for metabolic syndrome and insulin resistance (E88.81) and/or met at least 3 of the standard MetS components. To minimize misclassification due to transient abnormalities during acute illness or hospitalization (eg, isolated blood glucose or blood pressure readings), we used ICD codes to identify established diagnoses of T2DM and hypertension.

Patients with obesity were identified as those with BMI ≥30 kg/m² using ICD codes in the absence of other MetS components. Normal-weight patients were defined as those with BMI 18.5–24.9 kg/m² and no MetS criteria. Similarly, patients with T2DM were identified using ICD codes for T2DM only, in the absence of other MetS components. Data from both inpatient and outpatient settings were included using ICD codes rather than single-encounter measurements. To ensure temporal relevance, MetS component diagnoses were ascertained within a 1-year lookback period before the index date (ie, CDI diagnosis). A summary of the prevalence of comorbid conditions in the MetS cohort is provided in Supplementary Table 2, while the corresponding ICD codes are listed in Supplementary Table 3.

Baseline demographic data, comorbidities, prior medical encounters, antibiotics prescription records, proton pump inhibitor (PPI) prescriptions, laxative records, and history of abdominal surgery were collected (Supplementary Tables 4a–9a). Antibiotic exposure in all cohorts was assessed within a 1-year lookback period before CDI diagnosis—a time window that balances recent and clinically meaningful cumulative antibiotic exposure and is commonly used in CDI epidemiology studies [36]. To eliminate known confounding factors for CDI [37, 38], we performed 1:1 propensity score matching based on age, gender, antibiotic classes (eg, beta-lactams, fluoroquinolones, sulfonamides, etc.), and other comorbidities (including prior health care encounters, chronic kidney disease, liver disease, HIV, leukemia, and other neoplasms) (Supplementary Tables 4b–9b). The primary outcome of interest was risk of developing CDI in patients with MetS, as well as in those with obesity or T2DM, in the absence of other MetS criteria.

To assess the severity of CDI, we identified patients with MetS, obesity, or T2DM who had a confirmed CDI diagnosis. To minimize heterogeneity and reduce the likelihood of counting recurrent CDI from the same patient, analysis was restricted to each patient's most recent CDI episode. We then compared established measures of CDI severity, including sepsis, toxic megacolon, hypoalbuminemia, ICU admission, recurrent CDI, and all-cause mortality [39], occurring within 30 days of CDI diagnosis. Additionally, leukocytosis, defined as elevated white blood cells ≥15 × 10³/µL, and neutrophilia, defined as blood neutrophils ≥7.9 × 10³/µL corresponding to the upper limit of the normal reference range [40, 41], were assessed within 7 days after CDI diagnosis. A schematic of cohort selection for both CDI risk and severity outcomes is shown in Figure 1. Risk ratios (RRs), odds ratios (ORs), 95% CIs, and z-values were calculated using built-in TriNetX tools; t tests were used to assess differences between groups. Statistical significance was defined as P < .05.

Figure 1.

A, Schematic of cohort selection and propensity matching for CDI risk. Flowcharts depict the identification of patient cohorts from the TriNetX Research Network and the 1:1 propensity score matching process based on age, gender, antibiotic use, comorbidities, and prior medical exposure. Selection of MetS vs non-MetS and obese vs normal-BMI cohorts and the proportion that developed CDI. B, Schematic of cohort selection and propensity matching for CDI severity outcomes flowcharts depict the identification of patient cohorts from the TriNetX Research Network and the 1:1 propensity score matching process based on age, gender, antibiotic use, comorbidities, and prior medical exposure. Selection of CDI cases with MetS, obesity. Abbreviations: BMI, body mass index; CDI, Clostridioides difficile infection; MetS, metabolic syndrome.

Data Availability

Data presented and analyzed using the TriNetX platform are reported in aggregate form, with all patient-level data fully de-identified in accordance with protected health information regulations. Detailed documentation of TriNetX queries and analyses, including propensity score density function plots demonstrating cohort balance before and after matching, is available upon reasonable request.

RESULTS

Diagnosis of Metabolic Diseases Is Associated With Increased Risk of Acquiring CDI

Metabolic disorders are comprised of independent yet interconnected conditions associated with changes in gut microbiota compositions and altered immune homeostasis, which can lead to delayed or incomplete pathogen clearance [42–45]. Previous studies have reported an association between MetS and elevated susceptibility to bacterial and viral infections in hospital settings [46–48]. We assessed CDI risk in patients with MetS, obesity, and T2DM using ICD codes and compared incidence with matched controls. Our analyses revealed that patients with MetS, obesity, or T2DM had a greater likelihood of acquiring CDI compared with their matched controls. Specifically, risk of acquiring CDI was higher in the MetS group compared with the non-MetS cohort (OR, 1.94) (Table 1). We also compared risk of CDI diagnosis in patients with obesity and T2DM in the absence of any other MetS criteria with that of controls. Individuals with obesity (BMI ≥30) and those with T2DM had a higher risk of acquiring CDI than individuals with normal BMI (18–25) and those without T2DM, respectively (OR, 1.14 and 1.11) (Table 1).

Table 1.

Association of MetS, Obesity, and T2DM With CDI Risk

Cohort	Patients, No.	Patients With Outcome, No.	Risk, %	Odds Ratio (95% CI)	Z	P Value
MetS	184 051	3114	1.692	1.939 (1.826–2.059)	21.994	<.0001
No MetS	187 232	1647	0.880	…	…

Cohort	Patients, No.	Patients With Outcome, No.	Risk, %	Odds Ratio (95% CI)	Z	P Value
BMI ≥30	1 140 942	6163	0.540	1.138 (1.097–1.180)	6.927	<.0001
BMI 18–25	1 143 697	5433	0.475	…	…	…

Cohort	Patients, No.	Patients With Outcome, No.	Risk, %	Odds Ratio (95% CI)	Z	P Value
Patients with T2DM	616 801	4836	0.784	1.111 (1.066–1.157)	5.006	<.0001
Patients without T2DM	614 352	4340	0.706	…	…	…

Top panel: comparison between individuals with MetS and those without MetS. Middle panel: comparison between individuals with BMI ≥30 and those with normal BMI in the absence of other MetS criteria. Bottom panel: comparison between individuals with T2DM and those without T2DM. For the middle and bottom panels, the cohorts were selected based on a single criterion, either BMI (middle panel) or T2DM (bottom panel), without any other MetS components. Odds ratio with corresponding 95% CIs, z-statistics, and P values are reported. Differences in patient counts across matched cohorts reflect the exclusion of individuals with prior CDI before the index event.

Abbreviations: BMI, body mass index; CDI, Clostridioides difficile infection; MetS, metabolic syndrome; T2DM, type 2 diabetes mellitus.

MetS, Obesity, and T2DM Differentially Influence CDI Clinical Severity and Inflammatory Marker Expression

The impact of MetS and its individual components (eg, obesity and T2DM) on severity of bacterial and viral infection outcomes remains an active area of research [49, 50]. To evaluate their influence on CDI severity, we examined the risk of adverse clinical outcomes and inflammatory marker expression in CDI patients with MetS, obesity, or T2DM. Patients with MetS and those with obesity (in the absence of other MetS criteria) exhibited increased rates of several CDI severity indicators and inflammatory markers compared with matched controls. In the MetS cohort, severe sepsis (P < .0001), ICU admission (P < .0001), leukocytosis (P = .0131), and neutrophilia (P = .007) were all significantly more common than in patients without MetS (Figure 2). Similarly, patients with obesity (BMI ≥ 30) had significantly higher rates of severe sepsis (P < .0001), leukocytosis (P < .0001), and neutrophilia (P = .0016). Although ICU admission and all-cause mortality were higher in obese patients, neither outcome reached statistical significance. In contrast, patients with T2DM demonstrated significant increase across all CDI severity measures (Figure 2).

Figure 2.

A–C, Forest plots comparing clinical outcomes and inflammatory markers in CDI patients with MetS, obesity (BMI >30 kg/m²), and T2DM. The vertical dashed line represents OR = 1. Point estimate to the right of the vertical dashed lines indicates higher odds of the outcome, whereas point estimate to the left indicate lower odds. Outcomes shown include severe sepsis, leukocytosis, low albumin, all-cause mortality, recurrent CDI, ICU admission, and neutrophilia. For the middle and bottom panels, the cohorts were selected based on a single criterion, either BMI (middle panel) or T2DM (bottom panel), without any other MetS components. Abbreviations: BMI, body mass index; CDI, Clostridioides difficile infection; ICU, intensive care unit; MetS, metabolic syndrome; OR, odds ratio; T2DM, type 2 diabetes mellitus.

Despite these associations with increased disease severity, MetS and obesity were linked to more favorable outcomes for several other CDI-related end points. Both groups exhibited lower rates of hypoalbuminemia and recurrent CDI, defined as recurrence within 3 months of the initial diagnosis (Figure 2). In contrast, patients with T2DM exhibited significantly higher odds of hypoalbuminemia, recurrent CDI, and all-cause mortality (Figure 2). Toxic megacolon was observed infrequently across all 3 cohorts. Because the number of cases fell below the TriNetX reporting threshold (<10 cases per cohort), exact counts could not be disclosed, and statistical comparisons were not performed (Supplementary Table 10).

Class III Obesity Confers the Highest Risk of Severe CDI Outcomes

We next examined CDI severity outcomes across different obesity classes to determine whether increasing obesity severity influenced laboratory and clinical outcomes (Figure 3; Supplementary Table 1). No statistically significant differences were observed between patients with Class I obesity and Class II obesity (BMI 30–34.9 and 35–39.9, respectively) (Figure 3). In contrast, patients with Class III obesity (BMI ≥40) exhibited significantly worse outcomes than those with Clase I obesity (BMI 30–34.9). Specifically, Class III obesity was associated with higher odds of severe sepsis (P < .001), ICU admission (P < .001), leukocytosis (P < .001), low albumin (P < .001), neutrophilia (P < .001), and all-cause mortality (P = .005) (Figure 3). Similar findings were observed when patients with Class III obesity were compared with those with Class II obesity: individuals with Class III obesity had significantly greater risk of severe sepsis (P < .001), ICU admission (P < .001), leukocytosis (P = .001), low albumin (P < .001), neutrophilia (P < .001), and all-cause mortality (P = .001) (Figure 3). Notably, recurrent CDI did not follow this graded pattern. Recurrence rates were higher among patients with Class I obesity than among those with either Class II (P = .020) or Class III obesity (P = .017), whereas no significant difference was observed between Class II and Class III obesity (P = .657) (Figure 3).

Figure 3.

A–C, Forest plots comparing CDI-related outcomes across obesity classes. The figure displays ORs and P values for key clinical outcomes among patients with CDI, comparing (A) Class II vs Class I obesity, (B) Class III vs Class I obesity, and (C) Class III vs Class II obesity. Outcomes assessed include severe sepsis, toxic megacolon, leukocytosis, low albumin, ICU admission, neutrophilia, all-cause mortality, and recurrent CDI. Red markers indicate higher odds of the outcome in the higher obesity class; blue markers indicate lower odds. The vertical dashed line denotes the null value (OR, 1). Abbreviations: CDI, Clostridioides difficile infection; ICU, intensive care unit; OR, odds ratio.

MetS Is Associated With Changes in Peripheral Eosinophil Counts During CDI

Buonomo et al. demonstrated that eosinophils, innate immune cells traditionally recognized for their role in protecting against gut parasitic infections, act as critical effector cells through IL-25-mediated pathways to protect against mortality in CDI in mice [51]. In human patients with CDI, higher eosinophil counts have been associated with reduced risk of persistent diarrhea and death [52]. Building on these findings, we hypothesized that the lower risk of all-cause mortality observed in patients with MetS compared with No MetS might, in part, reflect eosinophil-mediated immunologic responses. To explore this possibility, we examined peripheral blood eosinophil counts at the time of CDI diagnosis in patients with MetS compared with those without MetS. Our analysis shows that peripheral blood eosinophil counts at the time of CDI diagnosis were slightly higher in patients with MetS compared with patients without MetS (Table 2).

Table 2.

Comparison of Peripheral Blood Eosinophil Percentages in Patients With CDI at the Time of Diagnosis With and Without Metabolic Syndrome

Group	Patients, No.	Mean ± SD	Median	IQR	P Value
MetS	8429	2.42 ± 3.61	1.9	2	<.0001
No MetS	58 674	2.16 ± 2.68	1.5	2.3

Values are presented as mean ± SD.

Abbreviations: CDI, Clostridioides difficile infection; IQR, interquartile range; MetS, metabolic syndrome.

DISCUSSION

MetS, obesity, and T2DM are distinct but related conditions characterized by chronic inflammation, altered gut microbiota composition, impaired intestinal barrier, and altered immune cell response [53–55]. Severe CDI outcomes are typically defined by sepsis, toxic megacolon, leukocytosis, neutrophilia, recurrent CDI, ICU admission, and increased mortality [39]. Although host metabolic state and gut dysbiosis are recognized contributors to CDI pathogenesis [56], few studies have examined the impact of various metabolic diseases on CDI risk and severity. To our knowledge, this is the first study to evaluate the association between a composite of various metabolic disorders (MetS) and individual MetS components (obesity and T2DM) in terms of the risk and severity outcomes of CDI.

Using data from the TriNetX research network, we assessed the impact of metabolic disorders on CDI risk and severity. The most important findings of our study are that metabolic disease was associated with an increased risk of CDI diagnosis. The highest risk was observed in patients with MetS, who had ∼94% higher odds of CDI diagnosis compared with individuals without MetS. In comparison, obesity or T2DM also increased the risk of getting CDI, although to a lesser extent (OR, 1.14 and 1.11, respectively). Regarding disease severity, MetS was associated with an increased odds of acute complications, including sepsis, leukocytosis, neutrophilia, and ICU admission, even after adjustment for antibiotic exposure and comorbid conditions. A similar pattern was observed in obese individuals who had higher odds of acute inflammatory manifestations such as sepsis, leukocytosis, and neutrophilia; however, ICU admission and all-cause mortality did not reach statistical significance. In contrast, T2DM was associated with increased risk across all measures of CDI severity, including inflammatory markers and severe clinical disease. Despite such associations with acute inflammatory complications, a divergent pattern was observed for selected end points. MetS was associated with reduced risk of hypoalbuminemia, recurrent CDI, and all-cause mortality. Similarly, obese patients had a lower risk of hypoalbuminemia and recurrent CDI. Stratification by obesity class revealed a clear relationship with the degree of obesity, with Class III obesity associated with highest risk of severe CDI outcomes and all-cause mortality, whereas outcomes were largely comparable between Class I and Class II obesity.

Overall, our findings are consistent with previous studies showing that obesity (BMI ≥30) and T2DM are associated with increased risk of acquiring CDI [57–60] and other infections requiring hospitalization [57]. A study in a suburban Shanghai population further reported that patients with MetS had more severe infections, with severity increasing as the number of MetS criteria increased [57]. Although we did not stratify patients by number of MetS components, the substantially higher odds of CDI observed in the MetS cohort compared with obesity or T2DM alone may reflect the cumulative and synergistic effects of multiple metabolic comorbidities on immune dysfunction and gut microbiota disruption. Prior studies have shown that BMI ≥35 kg/m² is associated with higher risk of ICU admission and mortality [61, 62]. Consistent with these reports, we observed significantly higher all-cause mortality among patients with class II & III obesity, suggesting a potential threshold effect whereby increasing obesity and its secondary effects on host physiology beyond a certain level contributes to worse CDI outcomes. This may reflect progressive impairment of immune and cardiometabolic responses, including heightened inflammation and diminished ability to tolerate systemic stress from severe CDI. Notably, the observed 30-day all-cause mortality rates in our cohort were relatively low in MetS (1.82%) and obesity (1.37%) compared with previously reported estimates for hospitalized CDI populations [28], whereas mortality in the T2DM cohort (8.10%) was more consistent with prior studies (Supplementary Table 11) [30]. The lower mortality observed in patients with MetS and obesity may reflect differences in study period, as our analysis was limited to the past 5 years, as well as improvements in CDI diagnosis and management over time.

The findings may also explain the paradoxical observation that, despite increased risks of acute inflammatory complications such as sepsis, leukocytosis, neutrophilia, and ICU admission, patients with MetS exhibited lower risks of hypoalbuminemia, recurrent CDI, and all-cause mortality. Similarly, the unstratified obese cohort showed reduced risks of hypoalbuminemia and CDI recurrence. One potential explanation is that greater metabolic reserve in the MetS cohort may confer resilience to the heightened catabolic demands of acute infection. In addition, patients with multiple metabolic comorbidities may undergo earlier recognition and more aggressive clinical management because they are perceived as being at higher risk. Chronic low-grade inflammation associated with metabolic disease may also “prime” the immune system, enhancing pathogen control in the acute phase, thereby contributing to reduced recurrence and mortality. An additional mechanism may involve eosinophil-mediated immune responses. Eosinophils are key effector cells that confer protection against CDI-associated mortality in mice [51], and higher eosinophil counts have been associated with reduced risk of persistent diarrhea and death in human CDI cohorts [52, 63]. Consistent with these observations, the CDI patients with MetS in our study exhibited modestly higher peripheral eosinophil counts at diagnosis than patients without MetS. These findings suggest that enhanced eosinophil-associated immunity may contribute to the lower mortality and recurrence rates observed in the MetS cohort despite their increased risk of acute inflammatory complications.

This study does have certain limitations. It is difficult to explore granular details on TriNetX despite offering access to large and diverse patient cohorts. It provides de-identified, aggregate-level data, which are reported as summary counts and measures rather than individual patient-level records. Although we matched specific patients’ characteristics such as antibiotic usage and comorbid conditions, the presence of unmatched baseline characteristics might have introduced some confounding, which may have influenced our findings. In order to attenuate these cofounding variables, we only included the most recent CDI diagnosis of patients and excluded patients with documented prior CDI before the index event. Identifying deaths due to C. difficile is challenging on this database. Hence, to minimize inclusion of deaths from other causes, we limited our analysis to a 30-day window following the index CDI diagnosis. It is also important to note that CDI cases were defined using ICD coding and laboratory confirmation. While these codes are commonly used to identify patients with CDI, they are primarily intended for billing purposes and may be subject to misclassification. Also, PCR or enzyme immunoassay laboratory evidence of CDI does not reliably distinguish between colonization and active infection. Detailed information regarding PCR test timing relative to admission was not consistently available in the database and hence could not be reported. Despite these limitations, our study leverages a large, multicenter data set, which provides statistical power and generalizability across diverse patient populations. Additionally, using both ICD coding and laboratory confirmation increases the likelihood of accurately identifying clinically significant CDI cases, and our use of a 30-day outcome window helps to focus on events most likely related to the infection.

Together, our results show that while MetS, obesity, and T2DM increase susceptibility to CDI acquisition and vulnerability to more severe acute CDI presentations, MetS may simultaneously be associated with lower recurrence and all-cause mortality. Overall, from this study, we will be able to consider the metabolic health of patients when assessing prognosis and adjusting management for patients with CDI. Most importantly, our findings highlight the need for larger prospective studies to better define risk by more accurately capturing the timing of PCR testing relative to hospital admission and precisely characterizing antibiotic exposure. In addition, mechanistic studies are needed to elucidate how different facets of MetS influence CDI pathogenesis and severity.

Supplementary Data

Supplementary materials are available at Open Forum Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.