Sex hormone-binding globulin and sex-specific association between irritable bowel syndrome and type 2 diabetes: a prospective cohort study

doi:10.21203/rs.3.rs-5311886/v1

Objective

To investigate the sex-specific association between irritable bowel syndrome (IBS) and type 2 diabetes (T2D), and further explore whether sex-hormone binding globulin (SHBG) was the potential cause of the sex-specific association.

Methods

The study was a prospective analysis based on the UK biobank (UKB) data. We included 359 503 participants, all of whom were without T2D diagnosis and had complete SHBG information at baseline. Hazard ratio (HR) and 95% confidence interval (CI) were calculated using non-IBS group as the reference, further stratified by sex and SHBG levels in multi-adjusted models.

Results

During a median follow-up of 10.4 years, 14 317 incident T2D cases had been documented. A statistically significant increased risk of T2D with IBS compared to those without IBS was observed in all multi-adjusted models (HR = 1.32, 95% CI = 1.23–1.42, P < 0.001). Additionally, a sex-specific association between IBS and T2D was found (P_interaction=0.008), with the risk in women (HR = 1.43, 95% CI = 1.31–1.57) being higher than in men (HR = 1.14, 95% CI = 1.01–1.29). A significant effect modification of SHBG was also observed in the association between IBS and T2D (P_interaction=0.001). The risk of incident T2D was higher in participants with higher SHBG levels (HR = 1.42, 95% CI = 1.25–1.63) than in those with lower SHBG levels (HR = 1.26, 95% CI = 1.16–1.37). Furthermore, we observed that the elevated risk of T2D associated with IBS may be strengthened by the genetic susceptibility to T2D.

Conclusions

A sex-specific association between IBS and T2D was found, and SHBG level might be the cause of the sex-specific association.

Irritable bowel syndrome

Type 2 diabetes

prospective cohort

stratified association

Irritable bowel syndrome (IBS) is one of the world’s most common disorders of gut-brain interaction, presenting with long-term recurrent abdominal pain accompanied by changes in bowel habits, but with no organic disease detected¹. The global prevalence of IBS was 11.2%², affecting individuals’ quality of life and imposing a considerable health and economic burden on society³. During the Coronavirus disease 2019 (COVID-19) pandemic, IBS patients in tertiary care had a higher symptom burden⁴. Diabetes is a major global health problem, and 536.6 million people had diabetes in 2021⁵. The prevalence of diabetes is projected to be 12.2% in 2045 worldwide⁵. Previous studies have already shown that several gastrointestinal symptoms, such as bloating diarrhea and constipation, are associated with diabetes^6,7, and IBS might correlate to fasting glucose⁸. In a case-control study, patients with IBS had a higher frequency of pre-diabetic symptoms compared to controls ⁹. Lately, a potential pathway linking T2D and IBS has been discovered, R. gnavus and its-derived tryptamine and phenethylamine, might have a positive correlation with insulin resistance in patients with IBS¹⁰. There is also an association between genetic liability to T2D with IBS¹¹.

Recently, genetic studies¹² have identified sex-specific associations in T2D, indicating that genetic predisposition to T2D may differ between men and women. Notably, the prevalence of IBS in women (14%) was higher than that for men (8.9%)², but the mechanism behind it is still yet to be found^13–15. Moreover, a study based on data from the National Health Insurance Research Database in Taiwan, 2011-2018¹⁶, also found that the incidence of IBS for women was higher than for men. However, though the sex-related differences in the two diseases both exist, there still lacks studies focusing on the sex-specific differences in the association between IBS and T2D.

Sex hormone-binding globulin (SHBG) is a glycoprotein that is synthesized primarily in the liver, playing a crucial role in regulating the bioavailability of sex steroids, including estrogen and testosterone by binding to them. Because it could influence adiposity and metabolic functions by regulating the concentration of bioavailable sex hormones, SHBG has already been used as a sex-stratified factor in studying the body fat percentage in genome-wide association studies (GWAS) using UK Biobank (UKB) data¹⁷. Of note, rising evidence shows that SHBG might have a relation with insulin resistance, then further influences the progress of T2D^18,19. Several epidemiological studies have shown that lower SHBG is an independent risk factor of T2D^20–22. In addition, the association between SHBG and T2D seems to be stronger in women than men²².

Hence, this study aims to find the potential sex-specific association between IBS and T2D using data from the UKB, and further investigate whether the sex-specific association is casued by SHBG levels.

This study was a prospective analysis based on the UKB. A detailed description of the study design was reported previously²³. Briefly, over 500,000 participants aged between 37 and 73 years were enrolled in 22 assessment centers across England, Wales, and Scotland between 2006 and 2010. All participants provided information through touch-screen questionnaire data, physical measurements, and blood and urine samples. The UKB was approved by the North West Multi-Centre Research Ethics Committee (Ref 11/NW/0382), and all participants signed written informed consent. This research was conducted using the UKB resources under application 66137.

We excluded 26 741 participants with prevalent diabetes diagnosis, 7524 with celiac disease or inflammatory bowel disease diagnosis, 44581 with any cancer diagnosis at baseline. In addition, 64073 participants whose SHBG information was missing were also excluded, leaving 359 503 participants included in the final analysis. And the definition of sex contains a mixture of the sex the participants were assigned at birth for the self-reported sex

Definition of irritable bowel syndrome

UKB used first occurrences data to define prevalent and incident cases. First occurrences information was derived from linkage to primary care or hospital admission data as well as self-reports. IBS diagnosis was confirmed using International Classification of Disease-10 (ICD-10) code of K58. Participants with first-reported diagnosis date prior to baseline survey time were considered as the prevalent IBS group, whereas the others were in the non-IBS group.

Ascertainment of type 2 diabetes

Incident T2D cases were identified when the diagnosis time of individuals with T2D were after the baseline survey date. T2D status was also determined by first occurrences data with ICD-10 code of E10.

Assessment of sex hormone-binding globulin

Serum SHBG concentrations (nmol/L) were measured by Beckman Coulter DXI 800 on the Beckman Coulter (UK), Ltd using blood sample collected at baseline survey. The detailed information of calibration and quality control process on the measurement is provided at the UKB website (https://biobank.ndph.ox.ac.uk/showcase/ukb/docs/serum_biochemistry.pdf). The coefficients of variation of SHBG for the samples with low, medium and high levels of internal quality control were 5.22%, 5.25%, and 5.67%, respectively. In addition, the external quality assurance result for SHBG was 95%.

Covariates

Potential confounders, namely sociodemographic characteristics, lifestyle behaviors, and comorbidities at baseline were all adjusted when examining the association between IBS and T2D. Sociodemographic characteristics included age (continuous, years), sex (men/women), ethnicity (white European, mixed, South Asian, black, others), Townsend Deprivation index (continuous). Townsend Deprivation index was calculated immediately prior to participants joining in UKB using indicators of unemployment, non-home ownership, non-car ownership, and household overcrowding. A higher Townsend Deprivation index represents a higher level of deprivation²⁴. Lifestyle behaviors included smoking status (never, current or former), alcohol drinking (never, current, or former), physical activity (continuous, MET-minutes), healthy diet score (0, 1, 2, 3, 4, 5), and body mass index (BMI, continuous, kg/m²). Physical activity was classified into low, moderate, and high levels based on the International Physical Activity Questionnaire. The healthy diet score was calculated using five components: vegetables, fruit, fish, processed meat, and unprocessed red meat. Each one point was given for each favorable diet factor, with the healthy diet score ranging from 0 to 5²⁵. Comorbidities included hypertension (yes/no), cardiovascular disease (yes/no), cholesterol levels (continuous, mmol/L), family history of diabetes (yes/no), and menopause status (yes/no, women only). The history of prevalent hypertension and cardiovascular disease was determined from self-reported information and medical records. Additionally, serum cholesterol levels were measured using the enzymatic method (Beckman Coulter (UK), Ltd). The family history of T2D was defined by an illness history of first-order relatives.

Genetic risk score

A genome-wide association study (GWAS)²⁶ published a total of 128 single nucleotide polymorphisms (SNPs) associated with T2D in Europeans, of which 42 SNPs reached a genome-wide significance level (P<5×10^-8). Genetic risk score (GRS) for T2D was calculated after 37 out of the 42 SNPs passed quality control in the UKB based on the existing publishment²⁷ for genotyping, quality control, and imputation procedures in this database. Supplementary Table 1 shows detailed information about the selected SNPs. Only 335 544 individuals of European descent were included in this genetic analysis. The GRS of T2D was derived by a weighted sum approach: GRS = (β₁×SNP₁+ β₂×SNP₂+…+β_n×SNP_n), where SNP_n is the count of risk alleles (0, 1, or 2) for each SNP, and β_n is the effect size for that SNP associated with T2D from the published GWAS. The GRS for T2D ranged between 3.04 and 7.30, with a higher score indicating a greater genetic predisposition to T2D. Study participants were categorized as being at low genetic risk, intermediate genetic risk, and high genetic risk of developing T2D according to the three tertiles of the GRS.

Statistical analysis

Baseline characteristics were described by mean for continuous variables and percentage for categorical variables. The Cox proportional hazard model was adopted to examine the sex-specific association between IBS and T2D. Hazard ratio (HR) and 95% confidence interval (CI) were calculated using non-IBS group as the reference. The follow-up period was defined from baseline survey time to the date of T2D diagnosis, death, or lost to follow-up (31 July 2019), which came first. For missing data, mean values were imputed for continuous variables and missing indicators were used for categorical variables. Proportional hazard assumption was ascertained by Schoenfeld residuals method.

Three adjustment models were conducted: (1) model 1 adjusted for demographics including age, UKB assessment center, race, and Townsend Deprivation index; (2) model 2 further adjusted for alcohol consumption, smoking status, physical activity, healthy diet score, and BMI; and (3) model 3 additionally adjusted for hypertension, cardiovascular disease, serum cholesterol levels, family history of diabetes, and menopause status (women only). Subgroup analysis was further performed to explore whether the association between IBS and T2D differed by sex. Furthermore, effect modification was evaluated by adding a cross-product interaction term of sex and IBS group in all models. To explore where the association between IBS and T2D was modified by SHBG concentrations, stratified analysis by median SHBG concentrations (<45.6 nmol/L and ≥45.6 nmol/L) was also conducted. In the genetic analysis, the genotyping array and the first ten genetic principal components were also adjusted.

Additionally, several sensitivity analyses were conducted to assess the robustness of the findings. First, we further adjusted for oestradiol and testosterone levels in the models. In addition, T2D cases during 2 years of follow-up were excluded to avoid reverse causality.

A 2-tailed P value of <0.05 was defined as statistically significant. All analyses were conducted using SAS software version 9.4.

Table 1 shows the sex-specific characteristics of the study participants at baseline. Men participants were older and had higher Townsend Deprivation index and BMI levels. In addition, men were more likely to be current drinkers and current smokers. They also tended to be physically active and have an unhealthier diet. Furthermore, the prevalence rates of hypertension and CVD of men were higher than women.

During a median follow-up of 10.4 years (3,661,035 person-years), we documented 14 317 incident T2D cases. For both men and women cases, statistically significant increased risks of T2D with IBS compared to those without IBS were observed in all three models (all P<0.001) (Supplementary Table 2). In the first model adjusted for age, sex, UKB assessment center, race, and Townsend Deprivation index, for participants with IBS, the HR for developing T2D is 1.34 (95% CI:1.25-1.43). Similar results were shown in model 2, with additively adjusted for alcohol consumption, smoking status, physical activity, healthy diet score, and BMI. When further adjusted for prevalent hypertension, prevalent CVD, cholesterol levels, and family history of diabetes based on model 2, model 3 presented a slightly lower HR of 1.32 (95% CI: 1.23-1.42).

We further found a sex-specific association between IBS and T2D in all adjusted models (Table 2). In men, models 1, 2, and 3 showed HRs of 1.15 (95% CI:1.02-1.30), 1.14 (95% CI:1.01-1.29), and 1.14 (95% CI:1.01-1.29), respectively. For women, the HRs were higher across the models: 1.47 (95% CI: 1.35-1.60), 1.48 (95% CI: 1.36-1.62), 1.43 (95% CI: 1.31-1.57), respectively. In addition, the interaction between sex and IBS on incident T2D was also tested in all models, revealing a significant effect modification of sex in the association between IBS and incident T2D (P _interaction=0.002, 0.001, 0.008, respectively) .

A stratified analysis was conducted according to SHBG levels, and significant interactions between SHBG and IBS on the risk of T2D incidence were observed in all models, as Figure 1 shows. In the group with higher SHBG level, IBS has a stronger relation with incident T2D cases generally: HR=1.43 (95%CI: 1.25-1.63), 1.43 (95%CI: 1.26-1.64), 1.42 (95%CI: 1.25-1.63), respectively when compared to the group with lower SHBG level: HR=1.28 (95%CI: 1.17-1.39), 1.28 (95%CI: 1.18-1.39), 1.26 (95%CI: 1.16-1.37), respectively in all three models. We also tested the interaction between SHBG levels and IBS on incident T2D, and significant modification effects of SHBG in the relation between IBS and T2D has also been discovered in all three models (P _interaction=0.003, 0.001, 0.001, respectively).

Additionally, we also evaluated the joint association between IBS prevalence and T2D genetic risk, and the results showed that individuals having IBS and were at higher genetic risk had a higher risk of developing T2D, even though there was no statistically significant interaction between the IBS and genetic susceptibility to T2D. Participants with IBS and the highest GRS had a 134% greater risk of T2D (95% CI, 108%-163%) than participants with the lowest GRS and without IBS diagnosis. Detailed information is provided in Figure 2.

We performed sensitivity analyses, showing that the results were essentially unchanged after further adjustment for oestradiol and testosterone levels (Supplementary Table 3) or limiting participants with a follow-up time of more than 2 years (Supplementary Table 4).

In this large prospective cohort study of the UKB, we mainly found that there was a significant, and sex-specific association between IBS and incident T2D. Additionally, a significant modification effect of SHBG in the association between IBS and T2D was observed, and in women with higher SHBG levels, the association between IBS and T2D was stronger in all multivariate-adjusted models. As SHBG could be a sex-stratified factor, the results could provide mutual corroboration for the sex-specific association between IBS and T2D. Also, a joint association between IBS prevalence and T2D genetic risk was observed, identifying the influence of genetics in this association.

Some existing studies have suggested a higher prevalence of T2D symptoms among individuals with IBS compared to the general population^9,10, which support the results in the current study. The potential mechanisms leading to the higher prevalence of T2D symptoms in IBS patients might relate to insulin resistance, possibly caused by gut microbiota-derived tryptamine and phenethylamine¹⁰. Besides, dysbiosis²⁸ is a common feature in both conditions and may represent a shared pathophysiological pathway. Moreover, the gut-brain axis as a bridge linking gut microbiota and brain nerves, has been proven to be involved in the pathology process of IBS and might be devoted to T2D by glucose homeostasis²⁹. As for medications, those used to treat T2D which particularly affect gastrointestinal motility, can influence IBS symptoms³⁰. Conversely, the management of IBS on certain medications or dietary changes may have implications for blood glucose control in T2D³¹.

To the best of our knowledge, it is the first study to assess the effect modification of sex in the association between IBS and T2D. The results showed a stronger association between IBS and T2D in women than in men, which exactly matches the findings of previous studies focusing on the sex differences in the risk of IBS³² and T2D^21,22, respectively. Additionally, the current study also found that the association between IBS and T2D was stronger in participants with higher SHBG levels than those with lower SHBG levels. As the level of SHBG is higher in women than men, the current results may come down to one possible explanation: SHBG is the potential cause of this sex difference between the two diseases.

Scholars have been far working on pathophysiological and epidemiological studies to understand the complex mechanism between SHBG and T2D^10,33. One hypothesis is that low SHBG levels may reflect a hormonal environment conducive to insulin resistance³⁴, a hallmark of T2D. Insulin resistance, in turn, may lead to compensatory, which has been suggested to suppress hepatic SHBG production, and then create a feedback loop³⁴. On the other hand, genetic association studies^35,36 have identified variants in the sex-hormone gene associated with T2D risk, further supporting a potential role of SHBG in the pathogenesis of T2D. Additionally, related to estrogen, SHBG may be involved in the modulation of inflammatory pathways, which are known to contribute to the development of insulin resistance and T2D³⁷. Therefore, SHBG may have a direct role in the pathophysiological processes leading to T2D. Also, the sex classification of IBS is to be focused on, a higher prevalence of IBS in women compared to men^32,38. First, hormonal differences, particularly the effects of estrogen and progesterone might influence gastrointestinal motility and sensitivity³⁹. Fluctuations in these hormones during the menstrual cycle can affect gut function, which might explain the increased prevalence and severity of IBS symptoms in women during certain phases of their menstrual cycle^39,40. As for genetics, genetic predispositions⁴¹ might be a factor contributing to the sex difference, influencing the susceptibility to IBS. Also, rising evidence⁴² suggests that gut microbiota composition varies between men and women, which might impact the development and manifestations of IBS, but the response toward fecal microbiota transplantation of both sexes turns out to be no difference by a study including 164 IBS patients⁴³. Besides, the psychology⁴⁰ of different sexes may lead to the difference in IBS reporting and medication, further form the sex classification. Considering both two diseases, SHBG could be the reason why sex differences were observed in their association, possibly related to the sex-specific hormones, insulin resistance and gut microbiota.

There are some strengths in this current large prospective cohort study. First, three multivariate-adjusted models were conducted to best minimize the possible bias, with sociodemographic, lifestyle and hormonal factors being considered. Second, several sensitivity analyses were conducted, with stable results to validate the robustness of the findings. Third, we newly included genetic factors in the study to verify their potential role in the association between IBS and T2D. However, the study has some limitations. First, we could not detect the causal relationship of SHBG levels and IBS with T2D due to the observational design. Second, there is only one detection of SHBG level, more measurements in the future are needed to validate the fluctuation of the association. Third, due to the low response rate to recruitment invitations from UKB⁴⁴, less socioeconomically deprivation might lead to fewer risk factors in long-term diseases of participants, further leading to uncontrolled selection bias.

In this study, we found a significant association between IBS and T2D, in which women are at higher risk than men. SHBG has effect modification in this relation, and it might be one of the reasons for the sex-specific association between IBS and T2D. There is also a joint association between IBS prevalence and T2D genetic risk in this relation. In the future, to better understand the sex-specific relationship between IBS and T2D, so as to develop better prevention or medication measures for these diseases, further clinical trials are expected to validate the findings above urgently.

Ethical Approval

The UKB was approved by the North West Multi-Centre Research Ethics Committee (Ref 11/NW/0382), and all participants signed written informed consent.

Consent for Publication

All participants provided informed consent.

Financial Disclosures:

All authors declare no conflict of interest.

Funding

This work was supported by grants from the National Natural Science Foundation of China (Grant No. 82204135), the Beijing Municipal Natural Science Foundation (7232237), the China Postdoctoral Science Foundation (Grant No. BX2021021, 2022M710249), Fujian Provincial Health Technology Project (Grant No. 2020CXB009), and the Natural Science Foundation of Fujian Province, China (Grant No. 2021J01352).

Author Contribution

M.W., T.H., and T.W. made the conception and designed the work; Y.T., H.G., H.P. did the data extraction and statistical analysis; S.W., Y.Z., and T.H. helped with software use in the work; C.G. and W. X. helped the data visualization. M.W. and Y.T. wrote the manuscript draft; T.W. and T.H. made revisions. All authors approved the submitted version.

Acknowledgements

This research was conducted using the UK Biobank resources under application 66137. We are grateful to the participants for sharing their health information.

Mayer EA, Ryu HJ, Bhatt RR. The neurobiology of irritable bowel syndrome. Mol Psychiatry. 2023. https://doi.org/10.1038/s41380-023-01972-w.
Lovell RM, Ford AC. Global prevalence of and risk factors for irritable bowel syndrome: a meta-analysis. Clin Gastroenterol Hepatol 10, 712–721 e714 (2012). https://doi.org/10.1016/j.cgh.2012.02.029
Black CJ, Ford AC. Global burden of irritable bowel syndrome: trends, predictions and risk factors. Nat Rev Gastroenterol Hepatol. 2020;17:473–86. https://doi.org/10.1038/s41575-020-0286-8.
Noble H, Hasan SS, Whorwell PJ, Vasant DH. The symptom burden of Irritable Bowel Syndrome in tertiary care during the COVID-19 pandemic. Neurogastroenterol Motil. 2022;34:e14347. https://doi.org/10.1111/nmo.14347.
Sun H, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. https://doi.org/10.1016/j.diabres.2021.109119.
Bytzer P, et al. Prevalence of gastrointestinal symptoms associated with diabetes mellitus: a population-based survey of 15,000 adults. Arch Intern Med. 2001;161:1989–96. https://doi.org/10.1001/archinte.161.16.1989.
Sang M, et al. Prevalence of Gastrointestinal Symptoms in Chinese Community-Dwelling Adults with and without Diabetes. Nutrients. 2022;14. https://doi.org/10.3390/nu14173506.
Javadekar NS, et al. Prevalence of irritable bowel syndrome and metabolic syndrome among young adults in an annual health check-up setting. JGH Open. 2021;5:1148–53. https://doi.org/10.1002/jgh3.12639.
Gulcan E, Taser F, Toker A, Korkmaz U, Alcelik A. Increased frequency of prediabetes in patients with irritable bowel syndrome. Am J Med Sci. 2009;338:116–9. https://doi.org/10.1097/MAJ.0b013e31819f7587.
Zhai L, et al. Gut microbiota-derived tryptamine and phenethylamine impair insulin sensitivity in metabolic syndrome and irritable bowel syndrome. Nat Commun. 2023;14:4986. https://doi.org/10.1038/s41467-023-40552-y.
Chen J, et al. Gastrointestinal Consequences of Type 2 Diabetes Mellitus and Impaired Glycemic Homeostasis: A Mendelian Randomization Study. Diabetes Care. 2023;46:828–35. https://doi.org/10.2337/dc22-1385.
Yang G, Schooling CM. Investigating sex-specific associations of lipid traits with type 2 diabetes, glycemic traits and sex hormones using Mendelian randomization. Cardiovasc Diabetol. 2023;22:3. https://doi.org/10.1186/s12933-022-01714-2.
Labus JS, et al. Sex-specific brain microstructural reorganization in irritable bowel syndrome. Pain. 2023;164:292–304. https://doi.org/10.1097/j.pain.0000000000002699.
Bonfiglio F, et al. Female-Specific Association Between Variants on Chromosome 9 and Self-Reported Diagnosis of Irritable Bowel Syndrome. Gastroenterology. 2018;155:168–79. https://doi.org/10.1053/j.gastro.2018.03.064.
Pretorius L, Van Staden ADP, Van der Merwe JJ, Henning N, Smith C. Alterations to microbial secretome by estrogen may contribute to sex bias in irritable bowel syndrome. Inflammopharmacology. 2022;30:267–81. https://doi.org/10.1007/s10787-021-00906-8.
Lai YT, Chen CY, Bair MJ, Epidemiology. Clinical Features, and Prescribing Patterns of Irritable Bowel Syndrome in Taiwan. Front Pharmacol. 2021;12:788795. https://doi.org/10.3389/fphar.2021.788795.
Roshandel D, Lu T, Paterson AD, Dash S. Beyond apples and pears: sex-specific genetics of body fat percentage. Front Endocrinol (Lausanne). 2023;14:1274791. https://doi.org/10.3389/fendo.2023.1274791.
Le TN, Nestler JE, Strauss JF, Wickham EP 3. Sex hormone-binding globulin and type 2 diabetes mellitus. Trends Endocrinol Metab. 2012;23:32–40. https://doi.org/10.1016/j.tem.2011.09.005.
Huang R, Wang Y, Yan R, Ding B, Ma J. Sex Hormone Binding Globulin is an Independent Predictor for Insulin Resistance in Male Patients with Newly Diagnosed Type 2 Diabetes Mellitus. Diabetes Ther. 2023;14:1627–37. https://doi.org/10.1007/s13300-023-01445-x.
Hedderson MM, et al. Prepregnancy SHBG concentrations and risk for subsequently developing gestational diabetes mellitus. Diabetes Care. 2014;37:1296–303. https://doi.org/10.2337/dc13-1965.
Ding EL, et al. Sex hormone-binding globulin and risk of type 2 diabetes in women and men. N Engl J Med. 2009;361:1152–63. https://doi.org/10.1056/NEJMoa0804381.
Ding EL, Song Y, Malik VS, Liu S. Sex differences of endogenous sex hormones and risk of type 2 diabetes: a systematic review and meta-analysis. JAMA. 2006;295:1288–99. https://doi.org/10.1001/jama.295.11.1288.
Sudlow C, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779. https://doi.org/10.1371/journal.pmed.1001779.
Townsend P. Deprivation and ill health. Nurs (Lond). 1991;4:11–5.
Pazoki R, et al. Genetic Predisposition to High Blood Pressure and Lifestyle Factors: Associations With Midlife Blood Pressure Levels and Cardiovascular Events. Circulation. 2018;137:653–61. https://doi.org/10.1161/CIRCULATIONAHA.117.030898.
Scott RA, et al. An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans. Diabetes. 2017;66:2888–902. https://doi.org/10.2337/db16-1253.
Bycroft C et al. Genome-wide genetic data on ~ 500,000 UK Biobank participants. bioRxiv, 166298 (2017). https://doi.org/10.1101/166298
Hyun CK. Molecular and Pathophysiological Links between Metabolic Disorders and Inflammatory Bowel Diseases. Int J Mol Sci. 2021;22. https://doi.org/10.3390/ijms22179139.
Bessac A, Cani PD, Meunier E, Dietrich G, Knauf C. Inflammation and Gut-Brain Axis During Type 2 Diabetes: Focus on the Crosstalk Between Intestinal Immune Cells and Enteric Nervous System. Front Neurosci. 2018;12:725. https://doi.org/10.3389/fnins.2018.00725.
Azmy Nabeh O, Ishak Attallah M. El-Sayed El-Gawhary, N. The pivotal relation between glucagon-like peptides, NFκB and inflammatory bowel disease. Clin Exp Pharmacol Physiol. 2020;47:1641–8. https://doi.org/10.1111/1440-1681.13361.
Michalak A, Mosińska P, Fichna J. Common links between metabolic syndrome and inflammatory bowel disease: Current overview and future perspectives. Pharmacol Rep. 2016;68:837–46. https://doi.org/10.1016/j.pharep.2016.04.016.
Lovell RM, Ford AC. Global prevalence of and risk factors for irritable bowel syndrome: a meta-analysis. Clin Gastroenterol Hepatol. 2012;10:712–e721714. https://doi.org/10.1016/j.cgh.2012.02.029.
Yaghootkar H, et al. Genetic evidence for a normal-weight metabolically obese phenotype linking insulin resistance, hypertension, coronary artery disease, and type 2 diabetes. Diabetes. 2014;63:4369–77. https://doi.org/10.2337/db14-0318.
Qu X, Donnelly R. Sex Hormone-Binding Globulin (SHBG) as an Early Biomarker and Therapeutic Target in Polycystic Ovary Syndrome. Int J Mol Sci. 2020;21. https://doi.org/10.3390/ijms21218191.
Syed AAS, He L, Shi Y. The Potential Effect of Aberrant Testosterone Levels on Common Diseases: A Mendelian Randomization Study. Genes (Basel). 2020;11. https://doi.org/10.3390/genes11070721.
Herrera-Lopez EE, et al. The rs1256031 of estrogen receptor β gene is associated with type 2 diabetes. Diabetes Metab Syndr. 2018;12:631–3. https://doi.org/10.1016/j.dsx.2018.04.018.
Mkhize BC, Mosili P, Ngubane PS, Sibiya NH, Khathi A. The Relationship between Renin-Angiotensin-Aldosterone System (RAAS) Activity, Osteoporosis and Estrogen Deficiency in Type 2 Diabetes. Int J Mol Sci. 2023;24. https://doi.org/10.3390/ijms241511963.
Ford AC, Sperber AD, Corsetti M, Camilleri M. Irritable bowel syndrome. Lancet. 2020;396:1675–88. https://doi.org/10.1016/s0140-6736(20)31548-8.
Meleine M, Matricon J. Gender-related differences in irritable bowel syndrome: potential mechanisms of sex hormones. World J Gastroenterol. 2014;20:6725–43. https://doi.org/10.3748/wjg.v20.i22.6725.
Lenhart A, et al. Postmenopausal women with irritable bowel syndrome (IBS) have more severe symptoms than premenopausal women with IBS. Neurogastroenterol Motil. 2020;32:e13913. https://doi.org/10.1111/nmo.13913.
Sasaki A, et al. Associations between Single-Nucleotide Polymorphisms in Corticotropin-Releasing Hormone-Related Genes and Irritable Bowel Syndrome. PLoS ONE. 2016;11:e0149322. https://doi.org/10.1371/journal.pone.0149322.
So SY, Savidge TC. Sex-Bias in Irritable Bowel Syndrome: Linking Steroids to the Gut-Brain Axis. Front Endocrinol (Lausanne). 2021;12:684096. https://doi.org/10.3389/fendo.2021.684096.
El-Salhy M, Casen C, Valeur J, Hausken T, Hatlebakk JG. Responses to faecal microbiota transplantation in female and male patients with irritable bowel syndrome. World J Gastroenterol. 2021;27:2219–37. https://doi.org/10.3748/wjg.v27.i18.2219.
Fontvieille E, et al. Body mass index and cancer risk among adults with and without cardiometabolic diseases: evidence from the EPIC and UK Biobank prospective cohort studies. BMC Med. 2023;21:418. https://doi.org/10.1186/s12916-023-03114-z.

Table 1 Baseline characteristics of participants by sex

	Male	Female
N	167 010	192 493
Age (years)	56.1	55.9
Race, white (%)	94.3	94.4
Townsend Deprivation index	-1.3	-1.4
Current drinkers (%)	93.9	90.9
Current smokers (%)	12.7	8.8
Physical activity level (%)
Low	15.7	14.2
Moderate	32.3	33.6
High	36.7	30.2
Healthy diet score
0-1	14.2	7.4
2-3	53.1	45.5
4-5	29.4	47.2
Body mass index (kg/m²)	27.6	26.9
Prevalent hypertension (%)	59.7	47.0
Prevalent cardiovascular disease (%)	8.5	3.9
Family history of diabetes (%)	19.1	21.6
Cholesterol (mmol/L)	1.1	1.1
Sex hormone binding globulin (nmol/L)	39.6	62.6
Prevalent irritable bowel syndrome (%)	2.89	6.76

Continuous and categorical variables are presented as mean and percentages (%), respectively.

Table 2 Sex-specific hazard ratio and 95% confidence interval of irritable bowel syndrome with incident type 2 diabetes

	Male			Female
	Irritable bowel syndrome			Irritable bowel syndrome
	No	Yes	P	No	Yes	P	P _interaction
Cases/N	8092/162 180	278/4830		5382/179 480	565/13013
Model 1^a	1.00	1.15 (1.02-1.30)	0.02	1.00	1.47 (1.35-1.60)	<0.001	0.002
Model 2^b	1.00	1.14 (1.01-1.29)	0.03	1.00	1.48 (1.36-1.62)	<0.001	0.001
Model 3^c	1.00	1.14 (1.01-1.29)	0.03	1.00	1.43 (1.31-1.57)	<0.001	0.008

^aAdjusted for age (continuous, years), UK Biobank assessment center, race (white European, mixed, South Asian, black, others), and Townsend Deprivation index (continuous).

^bAdjusted for age (continuous, years), UK Biobank assessment center, race (white European, mixed, South Asian, black, others), Townsend Deprivation index (continuous), alcohol consumption (current, former, never, missing), smoking status (current, former, never, missing), physical activity (continuous, MET-minutes), healthy diet score (0, 1, 2, 3, 4, 5), and body mass index (continuous, kg/m²).

^cAdjusted for age (continuous, years), UK Biobank assessment center, race (white European, mixed, South Asian, black, others), Townsend Deprivation index (continuous), alcohol consumption (current, former, never, missing), smoking status (current, former, never, missing), physical activity (continuous, MET-minutes), healthy diet score (0, 1, 2, 3, 4, 5), and body mass index (continuous, kg/m²), prevalent hypertension (yes/no), prevalent cardiovascular disease (yes/no), cholesterol (continuous, mmol/L), family history of diabetes (yes, no, or missing), and menopause status (yes/no, females only).

No competing interests reported.

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Sex hormone-binding globulin and sex-specific association between irritable bowel syndrome and type 2 diabetes: a prospective cohort study

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