Association between infection with Helicobacter pylori and Metabolic Syndrome among diabetic patients attending Jimma Medical Center in Jimma City, Ethiopia: A cross- sectional study

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However, findings remain contradictory, and data from developing countries are scarce. Methods We employed a cross-sectional study design to assess the relationship between H. pylori infection and metabolic syndrome among diabetic patients attending Jimma Hospital, Ethiopia. An interviewer-led questionnaire administered to study participants provided information on sociodemographic factors, and medical records were used to obtain medical history information. Metabolic parameters, including plasma glucose, triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), body-mass index (BMI), waist circumference (WC), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were collected. H. pylori infection status was assessed using IgG Enzyme-linked Immunosorbent Assays (ELISA). The effect of H. pylori infection on metabolic syndrome and metabolic parameters was determined using multivariate linear and logistic regressions. Results We found H. pylori infection status was positively but not significantly associated with metabolic syndrome (AOR = 1.507, 95% CI: 0.570–3.981, p = 0.408). When the analysis was restricted to individual metabolic parameters, H. pylori positivity was significantly associated with lower HDL-c and higher SB, respectively. Conclusions Our result confirms that individual metabolic parameters, not an overall metabolic syndrome, are significantly associated with H. pylori infection. Future studies should examine the relationship between H. pylori and metabolic syndrome, considering gastrointestinal conditions such as GERD, GU, and DU. Ethiopia Helicobacter pylori Metabolic Syndrome Metabolic parameters Introduction Helicobacter pylori is a gram-negative bacterium that affects over 50% of the world’s population with higher prevalence in developing countries ( 1 ). H. pylori persistently inhabits the gastric mucosa in the absence of treatment ( 2 ) although the majority of those infected are asymptomatic. There is good evidence supporting the role of H. pylori in the development gastric and duodenal ulcer, distal gastric adenocarcinoma, peptic ulcer disease, and primary gastric mucosa associated lymphoid tissue (MALT) lymphoma ( 3 , 4 ). Given the ability of H. pylori virulence factors to modulate host immune response ( 5 ), there is growing interest in investigating the effects of H. pylori in extragastroduodenal diseases. A growing body of evidence has supported an association between H. pylori and extragastroduodenal diseases including cardiopulmonary, hematologic, neurologic, dermatologic, and metabolic diseases ( 6 – 9 ). Our group in Ethiopia has found higher anemia prevalence, decreased growth trajectory, and reduced platelet indices among H. pylori -infected children compared with non-infected children ( 10 – 12 ), and provided supportive evidence for the potential role of H. pylori in extragastroduodenal diseases from a resource limited setting. However, these investigations didn’t explore the involvement of H. pylori in metabolic syndrome. The potential association between H. pylori and metabolic syndrome is a topic of great interest as metabolic syndrome affects an estimated one-quarter of the world population, and it is becoming increasingly prevalent ( 13 , 14 ). Metabolic syndrome is defined by a variety of factors that predispose patients to greater risk of cardiovascular disease, type II diabetes, as well as significantly higher total mortality ( 15 ). Metabolic parameters that predispose patients to developing metabolic syndrome include central obesity in addition to abnormal glucose levels, triglyceride levels, systolic blood pressure, diastolic blood pressure, as well as high-density lipoprotein and low-density lipoprotein cholesterol levels. Pervious clinical and epidemiological studies investigating the association between H. pylori infection status and prevalence of metabolic syndrome reported a positive association ( 16 – 26 ). While two studies to date have found no such association ( 27 , 28 ). More recently, a meta-analysis conducted by Azami et al. suggests that collective findings overwhelmingly support a significant association between H. pylori infection and metabolic syndrome ( 6 ). While previous epidemiological observations and mechanistic insights provide support for a role of H. pylori infection in metabolic syndrome development, most studies have been conducted in high-income populations, neglecting data from developing countries where the prevalence of H. pylori is greater ( 29 ). Therefore, the aim of this study was to assess the relationship between H. pylori infection and metabolic syndrome, as well as the relationship between H. pylori infection and metabolic syndrome indicators including glucose, triglycerides, HDL-C, waist circumference, systolic blood pressure, and diastolic blood pressure. We therefore used clinical setting data from a developing country to assess the relationship between H. pylori infection and metabolic syndrome among diabetic patients attending Jimma Medical Center in Jimma, Ethiopia. Methods Study setting This study was conducted at Jimma Medical Center in Jimma Town, Southwest Ethiopia. JMC is located in Jimma city, 352 km Southwest of Addis Ababa. Jimma, part of the Oromia region, sits at an elevation of 1780m above sea level, and average daily temperatures range from 20 o C in July to 27 o C in January. The population of Jimma is estimated to be 208 000. Jimma Medical Center, comprised of 1600 staff members, provides service to a catchment population 15 million people, and is currently the only teaching and referral hospital in the southwestern part of Ethiopia. The chronic care clinic section of the center provides services for non-communicable diseases including hypertension and diabetes. Over three-thousand diabetic patients attend the diabetic follow up clinic, which runs twice weekly and provides integrated care for diabetic patients. Physicians, nurses, and final year medical and B Sc nursing students all provide diabetic services at the clinic. Data collection took place from August 15, 2020, to October 30, 2020. A convenience sampling technique was used to select 321 diabetic patients. We recruited patients attending the JMC chronic clinic that were over 18 years old. Patients who were critically ill, pregnant, unconscious, or had communication difficulties were excluded from the study. Patients who received antibiotics, proton pump inhibitors, H2 blockers, or bismuth treatment within one month prior to the study were also excluded. Measurement and Data Collection Questionnaires Upon enrolling patients in this study, we then conducted structured questionnaires via face-to-face interviews, carried out by trained data collectors in the local language of patients, such as Afan Oromo or Amharic. These questionnaires were derived from the WHO stepwise approach to surveillance instruments, containing socio-demographic information, medical information, and modifiable risk factors such as smoking, alcohol consumption, khat chewing, fruit and vegetable consumption, type of oil used, and engagement in physical activity. Physical activity questionnaires were used to obtain data on the engagement of participants in physical activity in their daily lives. A participant engaging in greater than 150 minutes of moderate-intensity physical activity or greater than 75 minutes of high-intensity physical activity throughout the week, or greater than an equivalent combination of moderate- and high-intensity physical activity, was defined as physically active and labeled as “Yes”. Participants who did not meet this physically active definition criteria were defined as physically inactive and labeled as “No”. Anthropomorphic Data Anthropomorphic measurements including waist circumference (cm), weight (kg), and height (m) were measured, and body mass indices were computed as (weight (kg)) / [height (m)] 2 . All anthropomorphic measurements were performed by a trained data collector. Participants were instructed to remove their shoes, and digital scales were used to measure each participant’s weight. Standing height of shoeless participants was measured using a stadiometer. Waist circumference (WC) was measured around the midway portion between the lower border of the ribs and the bone crest at the widest portion over lightweight clothing using a soft tape measure, applying no pressure to the body. Blood pressure Data A mercury-based sphygmomanometer was used to measure systolic blood pressure (SBP) and diastolic blood pressure (DBP). For blood pressure measurement, participants were instructed to lay and rest in a supine position for more than ten minutes, and then blood pressure measurements were obtained. Clinical data including but not limited to family history of diabetes, duration of diabetes, hypertension status, and medication for hypertension were obtained from patient self-reporting and analysis of personal health record files. Blood sample collection and processing Blood samples were taken from the antecubital vein of patients with minimal tourniquet time. After that, the punctured area was disinfected with 70% ethanol, and the samples were collected in a serum separator tube. Five milliliters (mL) of venous blood were collected after an overnight fast (8–12 hours), and then transported to the clinical chemistry unit of the JMC laboratory for analysis. The blood specimens were held at room temperature for 20–30 minutes to facilitate clot formation, then centrifuged at 3000 rpm for five minutes to separate the serum from the blood. After separation, the serum samples were stored in Nunc tubes at -20 o C until analysis was performed. Laboratory Testing All biochemical analyses were performed in the clinical chemistry unit of Jimma Medical Center Laboratory by the spectrophotometric method of the Cobas 6000 chemistry analyzer. Plasma glucose was determined by enzymatic hexokinase principle. Total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein (LDL-c), and triglycerides (TG) were determined by enzymatic colorimetric methods. From the same sample, H. pylori antibody levels were determined by the IgG Enzyme-linked Immunosorbent Assays (ELISA) method according to the manufacturer’s instructions. This was performed by the ELISA reader in the screening room at the Jimma blood bank laboratory. A patient was considered to be positive for H. pylori infection if the IgG anti H pylori antibody concentration was > 20 U/mL. Data quality assurance Questionnaires were prepared in English and translated to local language (Amharic and Afan Oromo). Data were collected by licensed laboratory technologist and nurses under the supervision of a principal investigator. To avoid mislabeling of samples, test tube labels were cross-checked with unique patient identification numbers. To ensure that specimens were free from hemolysis and lipemia, collected samples were visually checked. Blood samples were immediately processed and separated. All laboratory activities were completed according to standard operating procedures and reagents and instrumental equipment were thoroughly checked prior to analyzing patient samples. Outcome and exposure variables The primary study outcome was metabolic syndrome status. We defined metabolic syndrome according to modified IDF criteria, present in Rafaeli et al. (2018) ( 24 ), which defined metabolic syndrome as two or more of the following conditions in addition to central obesity, defined as BMI ≥ 30kg/m 2 : triglycerides ≥ 150mg/dL, HDL < 40 mg/dL, plasma glucose ≥ 100mg/dL. ‘Exposure to H. pylori infection’ was defined as a positive result provided by the ELISA reader. Statistical Analysis Survey data and laboratory data from the JMC patient population was cleaned and coded for statistical analysis using IBM SPSS Statistics V.27. Before investigating the association between H. pylori infection and metabolic syndrome, univariate analyses were used to identify potential confounders. Variables associated with both the exposure ( H. pylori infection) and outcome (metabolic syndrome status) variables in the crude analysis using a statistical significance cutoff of p < 0.3 were considered to be possible confounders. For our modified IDF definition of metabolic syndrome, these included family history of diabetes and coffee drinking status. The primary outcome of this analysis was metabolic syndrome status. Our hypothesis that H. pylori infection would be associated with metabolic syndrome status was assessed using univariate logistic regression to calculate a crude odds ratio and obtain a p value. Then multivariate logistic regression was performed, adding potential confounders to the model and then removing them according to the backward elimination technique. Additionally, we examined the relationship between H. pylori infection status and continuous variables (metabolic parameters) using univariate and multivariate generalized linear models. These variables included plasma glucose, triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), body-mass index (BMI), waist circumference (WC), systolic blood pressure (SBP), and diastolic blood pressure (DBP). First, we analyzed the crude mean difference between H. pylori-positive and H. pylori -negative individuals, and then we repeated the analysis while adjusting for possible confounders, using the backwards elimination technique. Results Selected demographic characteristics and H. pylori infection status A total of 321 patients were enrolled in this study. Of these participants, 62.6% (201/321) were male and a slight majority of 55.1% (177/321) lived in a rural area. The age range of the study population was 18–69 years and 29.3% (94/321) of participants were within the 60–69 years old range. The income range of the study participants spanned from no monthly income to monthly income ≥ 4000 Birr and 38% (122/321) reported having no monthly income. Analyzing education, 31.5% (101/321) of study participants had no formal education while 43.6% (140/321) of participants had received primary education. Most participants 85.4% (274) were married and 81.9% (263) reported regularly drinking coffee. The prevalence of H. pylori infection was 69.5% (223/321) (Table 1 ). Table 1 Demographic characteristics of Study Subjects. Variable N Percent (%) Sex Male 201 62.6 Female 120 37.4 Participant Occupation Employee 71 22.1 Farmer 110 34.3 Housewife 69 21.5 Other 71 22.1 Education Illiterate 101 31.5 Primary School 140 43.6 High School 35 10.9 Diploma and Beyond 45 14 Residence Rural 177 55.1 Urban 144 44.9 Diabetes Mellitus Type Type 1 DM 88 27.4 Type 2 DM 233 72.6 Hypertension Status Yes 135 42.1 No 186 57.9 Medication for Hypertension Yes 125 38.9 No 196 61.1 Family History of Diabetes Yes 27 8.4 No 294 91.6 Smoking Status Yes 5 1.6 No 316 98.4 Alcohol Consumption Status Yes 12 3.7 No 309 96.3 Fruit Consumption Status Yes 297 92.5 No 24 7.5 Vegetable Consumption Status Yes 299 93.1 No 22 6.9 Khat Chewing Status Yes 90 28 No 231 72 Type of Oil or Fat Used Liquid Oil 169 52.6 Solid Oil 152 47.4 Coffee Drinking Status Yes 263 81.9 No 58 18.1 Physical Activity Yes 115 35.8 No 206 64.2 Level of Physical Activity inactive 206 64.2 Light 57 17.8 Moderate 15 4.7 Vigorous 43 13.4 Income (Birr) No Monthly Income (0) 122 38 1 to 1999 109 34 2000–3999 56 17.4 ≥ 4000 34 10.6 Amount of Sleep 7–8 hrs. 196 61.1 less than 6 hrs. 29 9 more than 9 hrs. 96 29.9 Participant Marital Status Single 35 10.9 Married 274 85.4 Divorced 12 3.7 Diabetes Duration 1–5 yrs. 181 56.4 6–10 yrs. 86 26.8 ≥ 11yrs. 54 16.8 Age 18–29 44 13.7 30–39 49 15.3 40–49 57 17.8 50–59 77 24 60–69 94 29.2 H. pylori status Negative 98 30.5 Positive 223 69.5 Metabolic syndrome components in study population Of the entire study population, 8.4% (27/321) of participants were classified as having metabolic syndrome according to modified IDF criteria. All participants had glucose levels ≥ 100 mg/dL, and 59.5% (191/321) participants had triglycerides ≥ 150 mg/dL. Only 9% (29/321) of the population had elevated high-density lipoprotein (HDL-c) levels. Examining blood pressure, 42.7% of participants had elevated systolic blood pressure (SBP) ≥ 130 mmHg, and 22.7% (73/321) had elevated diastolic blood pressure (DBP) (Table 2 ). Table 2 Prevalence of metabolic syndrome components in study population. Variable N Percent (%) Glucose ≥ 100 mg/dL 321 100 Tryglycerides (TG) 40 mg/dL(M) or > 50 mg/dL (F) 29 9 ≤ 40 mg/dL(M) or ≤ 50 mg/dL (F) 292 91 Systolic Blood Pressure (SBP) < 130 mmHg 184 57.3 ≥ 130 mmHg 137 42.7 Diastolic Blood Pressure (DBP) < 85 mmHg 248 77.3 ≥ 85 mmHg 73 22.7 Metabolic syndrome * Yes 27 8.4 No 294 91.6 *Metabolic syndrome defined according to IDF criteria: two or more of the following conditions in addition to central obesity, defined as BMI ≥ 30kg/m 2 : triglycerides ≥ 150mg/dL, HDL < 40 mg/dL, plasma glucose ≥ 100mg/dL Univariate analysis for relationships between potential confounders and H. pylori infection The crude association between demographic variables and H. pylori infection was examined using univariate logistic regression. Univariate logistic regression was performed between demographic variables and metabolic syndrome status and then performed between demographic variables and H. pylori status to identify potential confounders. Family history of diabetes and coffee drinking status were found to be potential confounders in relation to the modified IDF definition of metabolic syndrome which is defined as two or more of the following conditions in addition to central obesity, defined as BMI ≥ 30kg/m 2 : triglycerides ≥ 150mg/dL, HDL < 40 mg/dL, plasma glucose ≥ 100mg/dL. (Supplementary Tables 1 and 2). Interestingly, maternal education status, urban or rural residence, and age were not identified as significant confounders. Association between H. pylori infection and metabolic syndrome In a multivariate logistic model between H. pylori status and metabolic syndrome status adjusted for potential confounders, patients infected with H. pylori showed higher odds of metabolic syndrome according to modified IDF criteria, yet results were not statistically significant (AOR = 1.507, 95% CI: 0.570–3.981, p = 0.408) (Table 3 ). Table 3 Univariate and multivariate logistic regression analysis of metabolic syndrome risk associated with H. pylori infection status. Variable Metabolic Syndrome N(%)* COR (95%CI) P-value AOR (95%CI) ** P-value H. pylori Status Yes No Positive 21 (9.4) 202 (90.6) 1.594 (0.623–4.082) 0.331 1.507 (0.570–3.981) 0.408 Negative 6 (6.1) 92 (93.9) 1 - 1 - *Metabolic Syndrome defined according to IDF criteria: two or more of the following conditions in addition to central obesity, defined as BMI ≥ 30kg/m2: triglycerides ≥ 150mg/dL, HDL < 40 mg/dL, plasma glucose ≥ 100mg/dL **Adjusted for family history of diabetes and coffee drinking status Association between H. pylori infection and metabolic syndrome-related outcomes Linear regression models related plasma glucose, triglycerides, high-density lipoprotein cholesterol, body-mass index, waist circumference, systolic blood pressure, and diastolic blood pressure as continuous outcomes to the individual estimates of H. pylori status. These models showed significant increase in waist circumference among patients infected with H. pylori (mean difference: 4.337, 95% CI: 0.151–8.523, p = 0.042). These models also showed significant increase in systolic blood pressure among patients who were infected with H. pylori (mean difference: 4.867, 95% CI: 0.350–9.384, p = 0.035). When the analysis was adjusted for potential confounders found for JIS criteria, findings no longer remained statistically significant. When the analysis was adjusted for potential confounders using modified IDF criteria, there was a significant decrease in HDL-c observed among patients infected with H. pylori (adjusted mean difference: 6.651, 95% CI: 0.902–12.401, p = 0.024). Additionally, the increase in systolic blood pressure among H. pylori -infected patients remained significant when adjusting for confounders that were found using the IDF definition of metabolic syndrome (mean difference: 5.650, 95% CI: -11.278 - -0.22, p = 0.049) (Table 4 ). Table 4 T-test analysis analyzing unadjusted and adjusted mean difference between H. pylori infection status and metabolic parameters including blood glucose, triglycerides, HDL-C, WC, SBP, and DBP. Variables H. pylori Positive Mean [SD] H. pylori Negative Mean [SD] Crude Mean Difference (95% CI) p-value Adjusted Mean Difference (95% CI) p-value Glucose 174.844 [4.582] 177.342 [6.912] 2.498 (-18.814–13.819) 0.763 10.318 (-43.660–23.024)* 0.543 TG 186.284 [6.753] 177.432 [10.187] 8.852 (-15.193–32.898) 0.469 17.813 (-47.587–11.961) † 0.24 HDL-C 30.820 [0.630] 32.887 [0.950] 2.067 (-4.309–0.176) 0.071 6.651 (0.902–12.401) § 0.024 WC 89.602 [1.176] 85.265 [1.773] 4.337 (.151–8.523) 0.042 4.321 (-14.092–2.912) ** 0.197 SBP 126.520 [1.269] 121.653 [1.914] 4.867 (0.350–9.384) 0.035 5.650 (-11.278–0.22) †† 0.049 DBP 78.287 [0.770] 76.092 [1.162] 2.195 (-0.548–4.938) 0.116 0.394 (-6.033–5.244) §§ 0.891 † adjusted for age, coffee drinking status, amount of sleep, marital status § adjusted for age ** adjusted for age, DM type, coffee drinking status, amount of sleep, marital status †† adjusted for age, DM type, marital status §§ adjusted for age, DM type, coffee drinking status, marital status Discussion This study contributes to our understanding of the influence of H. pylori on metabolic syndrome status in diabetic patients in Ethiopia. We found H. pylori infection status was positively but not significantly associated with metabolic syndrome. We also found H. pylori positivity was significantly associated with lower HDL-c and higher SB, respectively. Several other studies have examined the effects of H. pylori infection on various parameters of metabolic syndrome, and have found positive association between H. pylori with lower HDL-c levels ( 20 – 22 ), and higher systolic blood pressure ( 20 ). These individual findings are supported by a meta-analysis conducted by Upala et al. (2016), which also found that, overall, H. pylori positivity is significantly associated with higher systolic blood pressure (p = 0.01) and lower HDL-c levels (p < 0.01) ( 30 ). However, more recent cross-sectional studies in the United States of America and Iran have found no significant difference in HDL-c levels between H. pylori -positive and negative individuals ( 31 – 33 ). In this study, our finding of no significant association between H. pylori infection and metabolic syndrome status is supported by few studies. Naja et al. (2012) and Takeoka et al. (2016) which also found that odds of metabolic syndrome did not significantly differ between H. pylori- positive and negative individuals, respectively ( 27 , 28 ). Similarly, Chen et al. (2019) found that H. pylori was not significantly associated with metabolic syndrome in females only ( 19 ). However, this explanation disagrees with previous studies that have found an association between H. pylori infection and metabolic syndrome in apparently healthy populations ( 16 – 26 ). In a meta-analysis conducted by Azami et al. (2021) showed a significant association between H. pylori positivity and metabolic syndrome with a pooled odds ratio of 1.19 (95% CI 1.05–1.35) ( 6 ). The magnitude of the odds ratio from this result didn’t not materially different from our observations, although ours failed to reach statistically significant. This inconsistency could be due to variations in age, outcome ascertainment, and differences in the method used to assess H. pylori status. Several hypotheses have been proposed regarding the mechanism by which H. pylori induces the development of metabolic syndrome. As H. pylori is known to disrupt gastric barrier function through the dysregulation of epithelial tight junctions ( 34 ), this predisposes the gut to mucosal damage ( 35 ). It has been shown that mucosal damage induces the production of pro-inflammatory cytokines ( 5 ), which may affect glucose and lipid metabolism ( 36 , 37 ), as these cytokines promote systemic inflammatory response ( 34 , 38 ). Interestingly, Mokhtar et al. found that H. pylori eradication in H. pylori-positive patients with functional dyspepsia resulted in a significant reduction of LDL levels, plasma glucose counts, and waist circumference Field ( 39 ), suggesting that H. pylori affects metabolic parameters and increased metabolic syndrome risk through interaction with the gastric epithelium. These findings are further substantiated by others who found that gastroesophageal reflux disease (GERD) and gastric ulcer (GU) Field ( 19 ), as well as a duodenal ulcer (DU) ( 24 ), were predictive for metabolic syndrome. Our failure to conclude a significant association between H. pylori positivity and metabolic syndrome may be explained by the lack of data on H. pylori-induced gastrointestinal abnormalities such as GU, DU, or GERD in the study population. Our findings should be interpreted considering the following limitations. First, the cross-sectional design of our study makes it difficult to establish causal findings since we did not have patient data prior to H. pylori infection. Longitudinal studies are needed to improve our understanding of the effects of H. pylori infection. Similarly, H. pylori may be a symptom of other conditions, such as other infections or socioeconomic status. We collected demographic and lifestyle information using self-reported questionnaires, which may be susceptible to misclassification and recall bias. However, the questionnaire had previously been effective in a similar population in Ethiopia, increasing the validity of our findings. Furthermore, enrolling patients with known type I or type II diabetes mellitus introduces the possibility that diabetes status may be associated with H. pylori infection or metabolic syndrome. To account for these possibilities, we adjusted our findings for markers of socioeconomic status that had been significantly associated with the outcome variable (metabolic syndrome). A further potential limitation is our method of H. pylori detection. As we used the IgG Enzyme-linked Immunosorbent Assays (ELISA) method to diagnose H. pylori -positive individuals, Shin et al. (2012) found that serological detection methods were less sensitive than histological methods, and thus less capable of establishing a correlation between H. pylori and metabolic syndrome. However, no other studies have examined the association between H. pylori infection and metabolic syndrome using ELISA methods to detect H. pylori and ELISA methods have been proven to be a useful tool with high diagnostic performance in African settings ( 40 ). Finally, the potential of reverse causality may also explain found associations between H. pylori and metabolic syndrome. However, H. pylori infection in developing countries such as Ethiopia frequently occurs early in life ( 41 ), which makes it unlikely that patient metabolic parameters abnormality preceded to H. pylori infection. The definition of metabolic syndrome used for this study was another limitation of this study. We opted to use the modified International Diabetes Federation (IDF) criteria used by Rafaeli et al. (2018), which used a BMI cutoff of ≥ 30 kg/m 2 as a proxy for determining central obesity as opposed to waist circumference values ( 24 ). However, Body-mass index has been used as substitute for waist circumference because BMI and waist circumference have been found to be associated with BMI ( 42 ). In conclusion, we found that H. pylori infection was significantly associated with lower levels of HDL-c and higher systolic blood pressure. However, we didn’t find a significant association between H. pylori infection status and overall metabolic syndrome. Future studies should seek to examine the relationship between H. pylori and metabolic syndrome in light of gastrointestinal conditions such as GERD, GU, and DU. Declarations Ethical Approval The Institutional Review Board (IRB) of Jimma University, Ethiopia approved the study. Informed consent was obtained from each patient before recruitment after explaining the study's objectives in the language they understood. The patients were informed that participation in the study would be voluntary and free. They were also informed that refusing or withdrawing from the study wouldn't jeopardize their right to receive any services. To ensure participant privacy, confidential numerical identifiers are assigned to each study subject, and all participant information remains password protected in electronic files. All methods were carried out in accordance with the relevant ethical guidelines and regulations of the Jimma University Institutional Review Board (IRB). Funding Funding for H. pylori ELISA analysis provided by Colgate University Research council. Jimma university supported the data collection Conflicts of interest We declare that we do not have any conflicts of interest. Author Contribution BT conceived and designed the study, and critically reviewed the manuscript. TI participated in the study design, data collection, and analysis, and drafted the initial manuscript. WC supervised the field data collection and critically reviewed the manuscript. WR, AG, and EZ assisted in data analysis and interpretation and critically reviewed the manuscript. All authors have read and approved the final manuscript. Data Availability The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request References Hooi JKY, Lai WY, Ng WK, Suen MMY, Underwood FE, Tanyingoh D, et al. Global Prevalence of Helicobacter pylori Infection: Systematic Review and Meta-Analysis. 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Association between metabolic syndrome and Helicobacter pylori infection diagnosed by histologic status and serological status. J Clin Gastroenterol. 2012;46(10):840–5. Yu Y, Cai J, Song Z, Wang J, Wu L. Association of Helicobacter pylori infection with metabolic syndrome in aged Chinese females. Exp Ther Med. 2019;17(6):4403–8. Naja F, Nasreddine L, Hwalla N, Moghames P, Shoaib H, Fatfat M, et al. Association of H. pylori infection with insulin resistance and metabolic syndrome among Lebanese adults. Helicobacter. 2012;17(6):444–51. Takeoka A, Tayama J, Yamasaki H, Kobayashi M, Ogawa S, Saigo T, et al. Impact of Helicobacter pylori Immunoglobulin G Levels and Atrophic Gastritis Status on Risk of Metabolic Syndrome. PLoS ONE. 2016;11(11):e0166588. Amberbir A, Medhin G, Erku W, Alem A, Simms R, Robinson K, et al. Effects of Helicobacter pylori, geohelminth infection and selected commensal bacteria on the risk of allergic disease and sensitization in 3-year-old Ethiopian children. Clin Exp Allergy. 2011;41(10):1422–30. Upala S, Jaruvongvanich V, Riangwiwat T, Jaruvongvanich S, Sanguankeo A. Association between Helicobacter pylori infection and metabolic syndrome: a systematic review and meta-analysis. J Dig Dis. 2016;17(7):433–40. Eslami O, Shahraki M, Shahraki T, Ansari H. Association of Helicobacter pylori infection with metabolic parameters and dietary habits among medical undergraduate students in southeastern of Iran. J Res Med Sci. 2017;22:12. Gillum RF. Infection with Helicobacter pylori, coronary heart disease, cardiovascular risk factors, and systemic inflammation: the Third National Health and Nutrition Examination Survey. J Natl Med Assoc. 2004;96(11):1470–6. Sotuneh N, Hosseini SR, Shokri-Shirvani J, Bijani A, Ghadimi R. Helicobacter pylori infection and metabolic parameters: is there an association in elderly population? Int J Prev Med. 2014;5(12):1537–42. Wroblewski LE, Shen L, Ogden S, Romero-Gallo J, Lapierre LA, Israel DA, et al. Helicobacter pylori dysregulation of gastric epithelial tight junctions by urease-mediated myosin II activation. Gastroenterology. 2009;136(1):236–46. Marcus EA, Vagin O, Tokhtaeva E, Sachs G, Scott DR. Helicobacter pylori impedes acid-induced tightening of gastric epithelial junctions. Am J Physiol Gastrointest Liver Physiol. 2013;305(10):G731–9. Albaker WI. Helicobacter pylori infection and its relationship to metabolic syndrome: is it a myth or fact? Saudi J Gastroenterol. 2011;17(3):165–9. Aslan M, Nazligul Y, Horoz M, Bolukbas C, Bolukbas FF, Gur M, et al. Serum paraoxonase-1 activity in Helicobacter pylori infected subjects. Atherosclerosis. 2008;196(1):270–4. Chmiela M, Gonciarz W. Molecular mimicry in Helicobacter pylori infections. World J Gastroenterol. 2017;23(22):3964–77. Mokhtare M, Mirfakhraee H, Arshad M, Samadani Fard SH, Bahardoust M, Movahed A, et al. The effects of helicobacter pylori eradication on modification of metabolic syndrome parameters in patients with functional dyspepsia. Diabetes Metab Syndr. 2017;11(Suppl 2):S1031–5. Tshibangu-Kabamba E, Phuc BH, Tuan VP, Fauzia KA, Kabongo-Tshibaka A, Kayiba NK, et al. Assessment of the diagnostic accuracy and relevance of a novel ELISA system developed for seroepidemiologic surveys of Helicobacter pylori infection in African settings. PLoS Negl Trop Dis. 2021;15(9):e0009763. Jaganath D, Saito M, Gilman RH, Queiroz DM, Rocha GA, Cama V, et al. First detected Helicobacter pylori infection in infancy modifies the association between diarrheal disease and childhood growth in Peru. Helicobacter. 2014;19(4):272–9. Ford ES, Mokdad AH, Giles WH. Trends in waist circumference among U.S. adults. Obes Res. 2003;11(10):1223–31. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4830688","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":335072217,"identity":"515a78d0-965d-4ec6-96b5-91be6c53fd76","order_by":0,"name":"Temam Ibrahim","email":"","orcid":"","institution":"Jimma University","correspondingAuthor":false,"prefix":"","firstName":"Temam","middleName":"","lastName":"Ibrahim","suffix":""},{"id":335072222,"identity":"9be1c3f2-ddfb-4ffe-aca3-6054a386c3f6","order_by":1,"name":"William Russel","email":"","orcid":"","institution":"Colgate University","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"Russel","suffix":""},{"id":335072223,"identity":"9e930085-c81f-4e63-82d9-62f2a21835b2","order_by":2,"name":"Aklilu Getachew","email":"","orcid":"","institution":"Jimma University","correspondingAuthor":false,"prefix":"","firstName":"Aklilu","middleName":"","lastName":"Getachew","suffix":""},{"id":335072227,"identity":"82a3984c-88d8-43af-9bfa-c0ef54bc8bcd","order_by":3,"name":"Endalew Zemene","email":"","orcid":"","institution":"Jimma University","correspondingAuthor":false,"prefix":"","firstName":"Endalew","middleName":"","lastName":"Zemene","suffix":""},{"id":335072228,"identity":"a02595a9-a5cb-4ce6-835e-9de8d6b00440","order_by":4,"name":"Waqtola Cheneke","email":"","orcid":"","institution":"Jimma University","correspondingAuthor":false,"prefix":"","firstName":"Waqtola","middleName":"","lastName":"Cheneke","suffix":""},{"id":335072229,"identity":"2f0b9afc-ded6-443b-acb0-2465e2c143d6","order_by":5,"name":"Bineyam Taye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACAzBZwcDABxVgbCCohQ1EnmFgYIOqJlILYxspWszlm59JfJxnJ8fGv/j5gw8MNrIbDhDQYtnGZiY5c1uyMZvEM8PGGQxpxgS1GBxjMJPm3XYgsU3igGEzD8PhRCK0sH+T/jsHpOX4x+Y/DP+J0cJjJs3YANTC32PYzMBwgLAWy7acYsueYyC/8BTO7DFINp5JSIs58/GNN37U2Mnx8x/f8OFHhZ1sHyEtQMAiAaYkEhhgiYEgYP4ApviJMH0UjIJRMApGJgAALjpEEuS5l08AAAAASUVORK5CYII=","orcid":"","institution":"Colgate University","correspondingAuthor":true,"prefix":"","firstName":"Bineyam","middleName":"","lastName":"Taye","suffix":""}],"badges":[],"createdAt":"2024-07-30 18:21:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4830688/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4830688/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-024-09840-w","type":"published","date":"2024-09-05T16:06:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64186067,"identity":"04127ef1-51e9-4c86-9d19-a0c3cffcc1eb","added_by":"auto","created_at":"2024-09-09 16:24:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":873533,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4830688/v1/e0c32e3c-331a-45c0-a95b-2e44b04efe11.pdf"},{"id":63476011,"identity":"cd149f65-1291-45d2-beeb-9d15fc606791","added_by":"auto","created_at":"2024-08-28 14:07:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38432,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesHPandMSDJan2024.docx","url":"https://assets-eu.researchsquare.com/files/rs-4830688/v1/fe99dc2a939062e56a98ccd3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between infection with Helicobacter pylori and Metabolic Syndrome among diabetic patients attending Jimma Medical Center in Jimma City, Ethiopia: A cross- sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cem\u003eHelicobacter pylori\u003c/em\u003e is a gram-negative bacterium that affects over 50% of the world\u0026rsquo;s population with higher prevalence in developing countries (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). \u003cem\u003eH. pylori\u003c/em\u003e persistently inhabits the gastric mucosa in the absence of treatment (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) although the majority of those infected are asymptomatic. There is good evidence supporting the role of \u003cem\u003eH. pylori\u003c/em\u003e in the development gastric and duodenal ulcer, distal gastric adenocarcinoma, peptic ulcer disease, and primary gastric mucosa associated lymphoid tissue (MALT) lymphoma (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Given the ability of \u003cem\u003eH. pylori\u003c/em\u003e virulence factors to modulate host immune response (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), there is growing interest in investigating the effects of \u003cem\u003eH. pylori\u003c/em\u003e in extragastroduodenal diseases. A growing body of evidence has supported an association between \u003cem\u003eH. pylori\u003c/em\u003e and extragastroduodenal diseases including cardiopulmonary, hematologic, neurologic, dermatologic, and metabolic diseases (\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Our group in Ethiopia has found higher anemia prevalence, decreased growth trajectory, and reduced platelet indices among \u003cem\u003eH. pylori\u003c/em\u003e-infected children compared with non-infected children (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), and provided supportive evidence for the potential role of \u003cem\u003eH. pylori\u003c/em\u003e in extragastroduodenal diseases from a resource limited setting. However, these investigations didn\u0026rsquo;t explore the involvement of \u003cem\u003eH. pylori\u003c/em\u003e in metabolic syndrome.\u003c/p\u003e \u003cp\u003eThe potential association between \u003cem\u003eH. pylori\u003c/em\u003e and metabolic syndrome is a topic of great interest as metabolic syndrome affects an estimated one-quarter of the world population, and it is becoming increasingly prevalent (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Metabolic syndrome is defined by a variety of factors that predispose patients to greater risk of cardiovascular disease, type II diabetes, as well as significantly higher total mortality (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Metabolic parameters that predispose patients to developing metabolic syndrome include central obesity in addition to abnormal glucose levels, triglyceride levels, systolic blood pressure, diastolic blood pressure, as well as high-density lipoprotein and low-density lipoprotein cholesterol levels. Pervious clinical and epidemiological studies investigating the association between \u003cem\u003eH. pylori\u003c/em\u003e infection status and prevalence of metabolic syndrome reported a positive association (\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). While two studies to date have found no such association (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). More recently, a meta-analysis conducted by Azami et al. suggests that collective findings overwhelmingly support a significant association between H. pylori infection and metabolic syndrome (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile previous epidemiological observations and mechanistic insights provide support for a role of \u003cem\u003eH. pylori\u003c/em\u003e infection in metabolic syndrome development, most studies have been conducted in high-income populations, neglecting data from developing countries where the prevalence of \u003cem\u003eH. pylori\u003c/em\u003e is greater (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Therefore, the aim of this study was to assess the relationship between \u003cem\u003eH. pylori\u003c/em\u003e infection and metabolic syndrome, as well as the relationship between \u003cem\u003eH. pylori\u003c/em\u003e infection and metabolic syndrome indicators including glucose, triglycerides, HDL-C, waist circumference, systolic blood pressure, and diastolic blood pressure. We therefore used clinical setting data from a developing country to assess the relationship between \u003cem\u003eH. pylori\u003c/em\u003e infection and metabolic syndrome among diabetic patients attending Jimma Medical Center in Jimma, Ethiopia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting\u003c/h2\u003e \u003cp\u003eThis study was conducted at Jimma Medical Center in Jimma Town, Southwest Ethiopia. JMC is located in Jimma city, 352 km Southwest of Addis Ababa. Jimma, part of the Oromia region, sits at an elevation of 1780m above sea level, and average daily temperatures range from 20 \u003csup\u003eo\u003c/sup\u003eC in July to 27 \u003csup\u003eo\u003c/sup\u003eC in January. The population of Jimma is estimated to be 208 000. Jimma Medical Center, comprised of 1600 staff members, provides service to a catchment population 15\u0026nbsp;million people, and is currently the only teaching and referral hospital in the southwestern part of Ethiopia. The chronic care clinic section of the center provides services for non-communicable diseases including hypertension and diabetes. Over three-thousand diabetic patients attend the diabetic follow up clinic, which runs twice weekly and provides integrated care for diabetic patients. Physicians, nurses, and final year medical and B Sc nursing students all provide diabetic services at the clinic. Data collection took place from August 15, 2020, to October 30, 2020. A convenience sampling technique was used to select 321 diabetic patients. We recruited patients attending the JMC chronic clinic that were over 18 years old. Patients who were critically ill, pregnant, unconscious, or had communication difficulties were excluded from the study. Patients who received antibiotics, proton pump inhibitors, H2 blockers, or bismuth treatment within one month prior to the study were also excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement and Data Collection\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eQuestionnaires\u003c/h2\u003e \u003cp\u003e Upon enrolling patients in this study, we then conducted structured questionnaires via face-to-face interviews, carried out by trained data collectors in the local language of patients, such as Afan Oromo or Amharic. These questionnaires were derived from the WHO stepwise approach to surveillance instruments, containing socio-demographic information, medical information, and modifiable risk factors such as smoking, alcohol consumption, khat chewing, fruit and vegetable consumption, type of oil used, and engagement in physical activity. Physical activity questionnaires were used to obtain data on the engagement of participants in physical activity in their daily lives. A participant engaging in greater than 150 minutes of moderate-intensity physical activity or greater than 75 minutes of high-intensity physical activity throughout the week, or greater than an equivalent combination of moderate- and high-intensity physical activity, was defined as physically active and labeled as \u0026ldquo;Yes\u0026rdquo;. Participants who did not meet this physically active definition criteria were defined as physically inactive and labeled as \u0026ldquo;No\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnthropomorphic Data\u003c/h2\u003e \u003cp\u003eAnthropomorphic measurements including waist circumference (cm), weight (kg), and height (m) were measured, and body mass indices were computed as (weight (kg)) / [height (m)]\u003csup\u003e2\u003c/sup\u003e. All anthropomorphic measurements were performed by a trained data collector. Participants were instructed to remove their shoes, and digital scales were used to measure each participant\u0026rsquo;s weight. Standing height of shoeless participants was measured using a stadiometer. Waist circumference (WC) was measured around the midway portion between the lower border of the ribs and the bone crest at the widest portion over lightweight clothing using a soft tape measure, applying no pressure to the body.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBlood pressure Data\u003c/h2\u003e \u003cp\u003eA mercury-based sphygmomanometer was used to measure systolic blood pressure (SBP) and diastolic blood pressure (DBP). For blood pressure measurement, participants were instructed to lay and rest in a supine position for more than ten minutes, and then blood pressure measurements were obtained. Clinical data including but not limited to family history of diabetes, duration of diabetes, hypertension status, and medication for hypertension were obtained from patient self-reporting and analysis of personal health record files.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBlood sample collection and processing\u003c/h2\u003e \u003cp\u003eBlood samples were taken from the antecubital vein of patients with minimal tourniquet time. After that, the punctured area was disinfected with 70% ethanol, and the samples were collected in a serum separator tube. Five milliliters (mL) of venous blood were collected after an overnight fast (8\u0026ndash;12 hours), and then transported to the clinical chemistry unit of the JMC laboratory for analysis. The blood specimens were held at room temperature for 20\u0026ndash;30 minutes to facilitate clot formation, then centrifuged at 3000 rpm for five minutes to separate the serum from the blood. After separation, the serum samples were stored in Nunc tubes at -20 \u003csup\u003eo\u003c/sup\u003eC until analysis was performed.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eLaboratory Testing\u003c/h2\u003e \u003cp\u003eAll biochemical analyses were performed in the clinical chemistry unit of Jimma Medical Center Laboratory by the spectrophotometric method of the Cobas 6000 chemistry analyzer. Plasma glucose was determined by enzymatic hexokinase principle. Total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein (LDL-c), and triglycerides (TG) were determined by enzymatic colorimetric methods. From the same sample, \u003cem\u003eH. pylori\u003c/em\u003e antibody levels were determined by the IgG Enzyme-linked Immunosorbent Assays (ELISA) method according to the manufacturer\u0026rsquo;s instructions. This was performed by the ELISA reader in the screening room at the Jimma blood bank laboratory. A patient was considered to be positive for\u003c/p\u003e \u003cp\u003e \u003cem\u003eH. pylori\u003c/em\u003e infection if the IgG anti \u003cem\u003eH pylori\u003c/em\u003e antibody concentration was \u0026gt;\u0026thinsp;20 U/mL.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData quality assurance\u003c/h2\u003e \u003cp\u003eQuestionnaires were prepared in English and translated to local language (Amharic and Afan Oromo). Data were collected by licensed laboratory technologist and nurses under the supervision of a principal investigator. To avoid mislabeling of samples, test tube labels were cross-checked with unique patient identification numbers. To ensure that specimens were free from hemolysis and lipemia, collected samples were visually checked. Blood samples were immediately processed and separated. All laboratory activities were completed according to standard operating procedures and reagents and instrumental equipment were thoroughly checked prior to analyzing patient samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eOutcome and exposure variables\u003c/h2\u003e \u003cp\u003eThe primary study outcome was metabolic syndrome status. We defined metabolic syndrome according to modified IDF criteria, present in Rafaeli et al. (2018) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), which defined metabolic syndrome as two or more of the following conditions in addition to central obesity, defined as BMI\u0026thinsp;\u0026ge;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e: triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150mg/dL, HDL\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL, plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;100mg/dL. \u0026lsquo;Exposure to \u003cem\u003eH. pylori\u003c/em\u003e infection\u0026rsquo; was defined as a positive result provided by the ELISA reader.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eSurvey data and laboratory data from the JMC patient population was cleaned and coded for statistical analysis using IBM SPSS Statistics V.27. Before investigating the association between \u003cem\u003eH. pylori\u003c/em\u003e infection and metabolic syndrome, univariate analyses were used to identify potential confounders. Variables associated with both the exposure (\u003cem\u003eH. pylori\u003c/em\u003e infection) and outcome (metabolic syndrome status) variables in the crude analysis using a statistical significance cutoff of p\u0026thinsp;\u0026lt;\u0026thinsp;0.3 were considered to be possible confounders. For our modified IDF definition of metabolic syndrome, these included family history of diabetes and coffee drinking status. The primary outcome of this analysis was metabolic syndrome status. Our hypothesis that \u003cem\u003eH. pylori\u003c/em\u003e infection would be associated with metabolic syndrome status was assessed using univariate logistic regression to calculate a crude odds ratio and obtain a p value. Then multivariate logistic regression was performed, adding potential confounders to the model and then removing them according to the backward elimination technique. Additionally, we examined the relationship between \u003cem\u003eH. pylori\u003c/em\u003e infection status and continuous variables (metabolic parameters) using univariate and multivariate generalized linear models. These variables included plasma glucose, triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), body-mass index (BMI), waist circumference (WC), systolic blood pressure (SBP), and diastolic blood pressure (DBP). First, we analyzed the crude mean difference between \u003cem\u003eH.\u003c/em\u003e pylori-positive and \u003cem\u003eH. pylori\u003c/em\u003e-negative individuals, and then we repeated the analysis while adjusting for possible confounders, using the backwards elimination technique.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSelected demographic characteristics and H. pylori infection status\u003c/h2\u003e \u003cp\u003eA total of 321 patients were enrolled in this study. Of these participants, 62.6% (201/321) were male and a slight majority of 55.1% (177/321) lived in a rural area. The age range of the study population was 18\u0026ndash;69 years and 29.3% (94/321) of participants were within the 60\u0026ndash;69 years old range. The income range of the study participants spanned from no monthly income to monthly income\u0026thinsp;\u0026ge;\u0026thinsp;4000 Birr and 38% (122/321) reported having no monthly income. Analyzing education, 31.5% (101/321) of study participants had no formal education while 43.6% (140/321) of participants had received primary education. Most participants 85.4% (274) were married and 81.9% (263) reported regularly drinking coffee. The prevalence of \u003cem\u003eH. pylori\u003c/em\u003e infection was 69.5% (223/321) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of Study Subjects.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipant Occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiploma and Beyond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Mellitus Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 1 DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 2 DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication for Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily History of Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Consumption Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruit Consumption Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetable Consumption Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKhat Chewing Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of Oil or Fat Used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiquid Oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid Oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee Drinking Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of Physical Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome (Birr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Monthly Income (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 to 1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u0026ndash;3999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmount of Sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u0026ndash;8 hrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eless than 6 hrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emore than 9 hrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipant Marital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;5 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;10 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;11yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH. pylori status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic syndrome components in study population\u003c/h2\u003e \u003cp\u003eOf the entire study population, 8.4% (27/321) of participants were classified as having metabolic syndrome according to modified IDF criteria. All participants had glucose levels\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/dL, and 59.5% (191/321) participants had triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL. Only 9% (29/321) of the population had elevated high-density lipoprotein (HDL-c) levels. Examining blood pressure, 42.7% of participants had elevated systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg, and 22.7% (73/321) had elevated diastolic blood pressure (DBP) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of metabolic syndrome components in study population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;100 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTryglycerides (TG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;150 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;150 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40 mg/dL(M) or \u0026gt;\u0026thinsp;50 mg/dL (F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;40 mg/dL(M) or \u0026le;\u0026thinsp;50 mg/dL (F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSystolic Blood Pressure (SBP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;130 mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;130 mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDiastolic Blood Pressure (DBP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;85 mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;85 mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMetabolic syndrome *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Metabolic syndrome defined according to IDF criteria: two or more of the following conditions in addition to central obesity, defined as BMI\u0026thinsp;\u0026ge;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e: triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150mg/dL, HDL\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL, plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;100mg/dL\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate analysis for relationships between potential confounders and H. pylori infection\u003c/h2\u003e \u003cp\u003eThe crude association between demographic variables and \u003cem\u003eH. pylori\u003c/em\u003e infection was examined using univariate logistic regression. Univariate logistic regression was performed between demographic variables and metabolic syndrome status and then performed between demographic variables and \u003cem\u003eH. pylori\u003c/em\u003e status to identify potential confounders. Family history of diabetes and coffee drinking status were found to be potential confounders in relation to the modified IDF definition of metabolic syndrome which is defined as two or more of the following conditions in addition to central obesity, defined as BMI\u0026thinsp;\u0026ge;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e: triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150mg/dL, HDL\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL, plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;100mg/dL. (Supplementary Tables\u0026nbsp;1 and 2). Interestingly, maternal education status, urban or rural residence, and age were not identified as significant confounders.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between H. pylori infection and metabolic syndrome\u003c/h2\u003e \u003cp\u003eIn a multivariate logistic model between \u003cem\u003eH.\u003c/em\u003e pylori status and metabolic syndrome status adjusted for potential confounders, patients infected with \u003cem\u003eH. pylori\u003c/em\u003e showed higher odds of metabolic syndrome according to modified IDF criteria, yet results were not statistically significant (AOR\u0026thinsp;=\u0026thinsp;1.507, 95% CI: 0.570\u0026ndash;3.981, p\u0026thinsp;=\u0026thinsp;0.408) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate logistic regression analysis of metabolic syndrome risk associated with \u003cem\u003eH. pylori\u003c/em\u003e infection status.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetabolic Syndrome N(%)*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAOR (95%CI) **\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH. pylori Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202 (90.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.594 (0.623\u0026ndash;4.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.507 (0.570\u0026ndash;3.981)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (93.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Metabolic Syndrome defined according to IDF criteria: two or more of the following conditions in addition to central obesity, defined as BMI\u0026thinsp;\u0026ge;\u0026thinsp;30kg/m2: triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150mg/dL, HDL\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL, plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;100mg/dL\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e**Adjusted for family history of diabetes and coffee drinking status\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between H. pylori infection and metabolic syndrome-related outcomes\u003c/h2\u003e \u003cp\u003eLinear regression models related plasma glucose, triglycerides, high-density lipoprotein cholesterol, body-mass index, waist circumference, systolic blood pressure, and diastolic blood pressure as continuous outcomes to the individual estimates of \u003cem\u003eH. pylori\u003c/em\u003e status. These models showed significant increase in waist circumference among patients infected with \u003cem\u003eH. pylori\u003c/em\u003e (mean difference: 4.337, 95% CI: 0.151\u0026ndash;8.523, p\u0026thinsp;=\u0026thinsp;0.042). These models also showed significant increase in systolic blood pressure among patients who were infected with \u003cem\u003eH. pylori\u003c/em\u003e (mean difference: 4.867, 95% CI: 0.350\u0026ndash;9.384, p\u0026thinsp;=\u0026thinsp;0.035). When the analysis was adjusted for potential confounders found for JIS criteria, findings no longer remained statistically significant. When the analysis was adjusted for potential confounders using modified IDF criteria, there was a significant decrease in HDL-c observed among patients infected with \u003cem\u003eH. pylori\u003c/em\u003e (adjusted mean difference: 6.651, 95% CI: 0.902\u0026ndash;12.401, p\u0026thinsp;=\u0026thinsp;0.024). Additionally, the increase in systolic blood pressure among \u003cem\u003eH. pylori\u003c/em\u003e-infected patients remained significant when adjusting for confounders that were found using the IDF definition of metabolic syndrome (mean difference: 5.650, 95% CI: -11.278 - -0.22, p\u0026thinsp;=\u0026thinsp;0.049) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eT-test analysis analyzing unadjusted and adjusted mean difference between \u003cem\u003eH. pylori\u003c/em\u003e infection status and metabolic parameters including blood glucose, triglycerides, HDL-C, WC, SBP, and DBP.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH. pylori Positive Mean [SD]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH. pylori Negative Mean [SD]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrude Mean Difference (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted Mean Difference (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174.844 [4.582]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177.342 [6.912]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.498 (-18.814\u0026ndash;13.819)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.318 (-43.660\u0026ndash;23.024)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186.284 [6.753]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177.432 [10.187]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.852 (-15.193\u0026ndash;32.898)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.813 (-47.587\u0026ndash;11.961) \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.820 [0.630]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.887 [0.950]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.067 (-4.309\u0026ndash;0.176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.651 (0.902\u0026ndash;12.401) \u0026sect;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.602 [1.176]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.265 [1.773]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.337 (.151\u0026ndash;8.523)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.321 (-14.092\u0026ndash;2.912) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.520 [1.269]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121.653 [1.914]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.867 (0.350\u0026ndash;9.384)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.650 (-11.278\u0026ndash;0.22) \u0026dagger;\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.287 [0.770]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.092 [1.162]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.195 (-0.548\u0026ndash;4.938)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.394 (-6.033\u0026ndash;5.244) \u0026sect;\u0026sect;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026dagger; adjusted for age, coffee drinking status, amount of sleep, marital status\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026sect; adjusted for age\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e** adjusted for age, DM type, coffee drinking status, amount of sleep, marital status\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026dagger;\u0026dagger; adjusted for age, DM type, marital status\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026sect;\u0026sect; adjusted for age, DM type, coffee drinking status, marital status\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study contributes to our understanding of the influence of \u003cem\u003eH. pylori\u003c/em\u003e on metabolic syndrome status in diabetic patients in Ethiopia. We found \u003cem\u003eH. pylori\u003c/em\u003e infection status was positively but not significantly associated with metabolic syndrome. We also found \u003cem\u003eH. pylori\u003c/em\u003e positivity was significantly associated with lower HDL-c and higher SB, respectively.\u003c/p\u003e \u003cp\u003eSeveral other studies have examined the effects of \u003cem\u003eH. pylori\u003c/em\u003e infection on various parameters of metabolic syndrome, and have found positive association between \u003cem\u003eH. pylori\u003c/em\u003e with lower HDL-c levels (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), and higher systolic blood pressure (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). These individual findings are supported by a meta-analysis conducted by Upala et al. (2016), which also found that, overall, \u003cem\u003eH. pylori\u003c/em\u003e positivity is significantly associated with higher systolic blood pressure (p\u0026thinsp;=\u0026thinsp;0.01) and lower HDL-c levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). However, more recent cross-sectional studies in the United States of America and Iran have found no significant difference in HDL-c levels between \u003cem\u003eH. pylori\u003c/em\u003e-positive and negative individuals (\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, our finding of no significant association between \u003cem\u003eH. pylori\u003c/em\u003e infection and metabolic syndrome status is supported by few studies. Naja et al. (2012) and Takeoka et al. (2016) which also found that odds of metabolic syndrome did not significantly differ between H. pylori- positive and negative individuals, respectively (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Similarly, Chen et al. (2019) found that \u003cem\u003eH. pylori\u003c/em\u003e was not significantly associated with metabolic syndrome in females only (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, this explanation disagrees with previous studies that have found an association between \u003cem\u003eH. pylori\u003c/em\u003e infection and metabolic syndrome in apparently healthy populations (\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In a meta-analysis conducted by Azami et al. (2021) showed a significant association between \u003cem\u003eH. pylori\u003c/em\u003e positivity and metabolic syndrome with a pooled odds ratio of 1.19 (95% CI 1.05\u0026ndash;1.35) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The magnitude of the odds ratio from this result didn\u0026rsquo;t not materially different from our observations, although ours failed to reach statistically significant. This inconsistency could be due to variations in age, outcome ascertainment, and differences in the method used to assess H. pylori status.\u003c/p\u003e \u003cp\u003eSeveral hypotheses have been proposed regarding the mechanism by which \u003cem\u003eH. pylori\u003c/em\u003e induces the development of metabolic syndrome. As \u003cem\u003eH. pylori\u003c/em\u003e is known to disrupt gastric barrier function through the dysregulation of epithelial tight junctions (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), this predisposes the gut to mucosal damage (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). It has been shown that mucosal damage induces the production of pro-inflammatory cytokines (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), which may affect glucose and lipid metabolism (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), as these cytokines promote systemic inflammatory response (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Interestingly, Mokhtar et al. found that \u003cem\u003eH. pylori\u003c/em\u003e eradication in H. pylori-positive patients with functional dyspepsia resulted in a significant reduction of LDL levels, plasma glucose counts, and waist circumference Field (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), suggesting that \u003cem\u003eH. pylori\u003c/em\u003e affects metabolic parameters and increased metabolic syndrome risk through interaction with the gastric epithelium. These findings are further substantiated by others who found that gastroesophageal reflux disease (GERD) and gastric ulcer (GU) Field (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), as well as a duodenal ulcer (DU) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), were predictive for metabolic syndrome. Our failure to conclude a significant association between \u003cem\u003eH. pylori\u003c/em\u003e positivity and metabolic syndrome may be explained by the lack of data on \u003cem\u003eH.\u003c/em\u003e pylori-induced gastrointestinal abnormalities such as GU, DU, or GERD in the study population.\u003c/p\u003e \u003cp\u003eOur findings should be interpreted considering the following limitations. First, the cross-sectional design of our study makes it difficult to establish causal findings since we did not have patient data prior to \u003cem\u003eH. pylori\u003c/em\u003e infection. Longitudinal studies are needed to improve our understanding of the effects of \u003cem\u003eH. pylori\u003c/em\u003e infection. Similarly, \u003cem\u003eH. pylori\u003c/em\u003e may be a symptom of other conditions, such as other infections or socioeconomic status. We collected demographic and lifestyle information using self-reported questionnaires, which may be susceptible to misclassification and recall bias. However, the questionnaire had previously been effective in a similar population in Ethiopia, increasing the validity of our findings. Furthermore, enrolling patients with known type I or type II diabetes mellitus introduces the possibility that diabetes status may be associated with H. pylori infection or metabolic syndrome. To account for these possibilities, we adjusted our findings for markers of socioeconomic status that had been significantly associated with the outcome variable (metabolic syndrome). A further potential limitation is our method of \u003cem\u003eH. pylori\u003c/em\u003e detection. As we used the IgG Enzyme-linked Immunosorbent Assays (ELISA) method to diagnose \u003cem\u003eH. pylori\u003c/em\u003e-positive individuals, Shin et al. (2012) found that serological detection methods were less sensitive than histological methods, and thus less capable of establishing a correlation between \u003cem\u003eH. pylori\u003c/em\u003e and metabolic syndrome. However, no other studies have examined the association between \u003cem\u003eH. pylori\u003c/em\u003e infection and metabolic syndrome using ELISA methods to detect \u003cem\u003eH. pylori\u003c/em\u003e and ELISA methods have been proven to be a useful tool with high diagnostic performance in African settings (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Finally, the potential of reverse causality may also explain found associations between H. pylori and metabolic syndrome. However, \u003cem\u003eH. pylori\u003c/em\u003e infection in developing countries such as Ethiopia frequently occurs early in life (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), which makes it unlikely that patient metabolic parameters abnormality preceded to \u003cem\u003eH. pylori\u003c/em\u003e infection.\u003c/p\u003e \u003cp\u003eThe definition of metabolic syndrome used for this study was another limitation of this study. We opted to use the modified International Diabetes Federation (IDF) criteria used by Rafaeli et al. (2018), which used a BMI cutoff of \u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e as a proxy for determining central obesity as opposed to waist circumference values (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, Body-mass index has been used as substitute for waist circumference because BMI and waist circumference have been found to be associated with BMI (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, we found that \u003cem\u003eH. pylori\u003c/em\u003e infection was significantly associated with lower levels of HDL-c and higher systolic blood pressure. However, we didn\u0026rsquo;t find a significant association between \u003cem\u003eH. pylori\u003c/em\u003e infection status and overall metabolic syndrome. Future studies should seek to examine the relationship between \u003cem\u003eH. pylori\u003c/em\u003e and metabolic syndrome in light of gastrointestinal conditions such as GERD, GU, and DU.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eThe Institutional Review Board (IRB) of Jimma University, Ethiopia approved the study. Informed consent was obtained from each patient before recruitment after explaining the study\u0026apos;s objectives in the language they understood. The patients were informed that participation in the study would be voluntary and free. They were also informed that refusing or withdrawing from the study wouldn\u0026apos;t jeopardize their right to receive any services. To ensure participant privacy, confidential numerical identifiers are assigned to each study subject, and all participant information remains password protected in electronic files. All methods were carried out in accordance with the relevant ethical guidelines and regulations of the Jimma University Institutional Review Board (IRB).\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eFunding for H. pylori ELISA analysis provided by Colgate University Research council. Jimma university supported the data collection\u003c/p\u003e\n\u003ch2\u003eConflicts of interest\u003c/h2\u003e\n\u003cp\u003eWe declare that we do not have any conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eBT conceived and designed the study, and critically reviewed the manuscript. TI participated in the study design, data collection, and analysis, and drafted the initial manuscript. WC supervised the field data collection and critically reviewed the manuscript. WR, AG, and EZ assisted in data analysis and interpretation and critically reviewed the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study available from the corresponding author on reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHooi JKY, Lai WY, Ng WK, Suen MMY, Underwood FE, Tanyingoh D, et al. Global Prevalence of Helicobacter pylori Infection: Systematic Review and Meta-Analysis. Gastroenterology. 2017;153(2):420\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlaser MJ, Atherton JC. Helicobacter pylori persistence: biology and disease. J Clin Invest. 2004;113(3):321\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtherton JC. The pathogenesis of Helicobacter pylori-induced gastro-duodenal diseases. 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Obes Res. 2003;11(10):1223\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ethiopia, Helicobacter pylori, Metabolic Syndrome, Metabolic parameters","lastPublishedDoi":"10.21203/rs.3.rs-4830688/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4830688/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrevious studies have implicated the role of H. pylori infection in developing the metabolic syndrome. However, findings remain contradictory, and data from developing countries are scarce.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe employed a cross-sectional study design to assess the relationship between H. pylori infection and metabolic syndrome among diabetic patients attending Jimma Hospital, Ethiopia. An interviewer-led questionnaire administered to study participants provided information on sociodemographic factors, and medical records were used to obtain medical history information. Metabolic parameters, including plasma glucose, triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), body-mass index (BMI), waist circumference (WC), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were collected. H. pylori infection status was assessed using IgG Enzyme-linked Immunosorbent Assays (ELISA). The effect of H. pylori infection on metabolic syndrome and metabolic parameters was determined using multivariate linear and logistic regressions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe found H. pylori infection status was positively but not significantly associated with metabolic syndrome (AOR\u0026thinsp;=\u0026thinsp;1.507, 95% CI: 0.570\u0026ndash;3.981, p\u0026thinsp;=\u0026thinsp;0.408). When the analysis was restricted to individual metabolic parameters, \u003cem\u003eH. pylori\u003c/em\u003e positivity was significantly associated with lower HDL-c and higher SB, respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur result confirms that individual metabolic parameters, not an overall metabolic syndrome, are significantly associated with \u003cem\u003eH. pylori\u003c/em\u003e infection. Future studies should examine the relationship between \u003cem\u003eH. pylori\u003c/em\u003e and metabolic syndrome, considering gastrointestinal conditions such as GERD, GU, and DU.\u003c/p\u003e","manuscriptTitle":"Association between infection with Helicobacter pylori and Metabolic Syndrome among diabetic patients attending Jimma Medical Center in Jimma City, Ethiopia: A cross- sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-28 14:07:42","doi":"10.21203/rs.3.rs-4830688/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-02T07:05:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-01T08:26:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-01T08:24:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2024-07-30T18:20:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2ad803dc-646e-4f3c-b6bc-83cecf72401a","owner":[],"postedDate":"August 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-09T16:15:53+00:00","versionOfRecord":{"articleIdentity":"rs-4830688","link":"https://doi.org/10.1186/s12879-024-09840-w","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2024-09-05 16:06:01","publishedOnDateReadable":"September 5th, 2024"},"versionCreatedAt":"2024-08-28 14:07:42","video":"","vorDoi":"10.1186/s12879-024-09840-w","vorDoiUrl":"https://doi.org/10.1186/s12879-024-09840-w","workflowStages":[]},"version":"v1","identity":"rs-4830688","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4830688","identity":"rs-4830688","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}