Psychometric profiling of patients with chronic gastroduodenal symptoms using body surface gastric mapping phenotypes

Psychometric profiling of patients with chronic gastroduodenal symptoms using body surface gastric mapping phenotypes | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Psychometric profiling of patients with chronic gastroduodenal symptoms using body surface gastric mapping phenotypes View ORCID Profile Mikaela Law , View ORCID Profile Gabriel Schamberg , View ORCID Profile Chris Varghese , Billy Wu , View ORCID Profile Charlotte Daker , View ORCID Profile Isabella Pickering , Gen Johnston , View ORCID Profile Daphne Foong , Vincent Ho , Christopher N. Andrews , View ORCID Profile Armen Gharibans , View ORCID Profile Greg O’Grady , View ORCID Profile Stefan Calder doi: https://doi.org/10.1101/2025.07.28.25332341 Mikaela Law 1 The Department of Surgery, The University of Auckland , New Zealand 2 Alimetry Ltd. , Auckland, New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mikaela Law Gabriel Schamberg 1 The Department of Surgery, The University of Auckland , New Zealand 2 Alimetry Ltd. , Auckland, New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gabriel Schamberg Chris Varghese 1 The Department of Surgery, The University of Auckland , New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chris Varghese Billy Wu 2 Alimetry Ltd. , Auckland, New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site Charlotte Daker 2 Alimetry Ltd. , Auckland, New Zealand 3 The Department of Gastroenterology , Te Whatu Ora - Waitematā, New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Charlotte Daker Isabella Pickering 4 The Department of Psychological Medicine, The University of Auckland , New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Isabella Pickering Gen Johnston 1 The Department of Surgery, The University of Auckland , New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daphne Foong 2 Alimetry Ltd. , Auckland, New Zealand 5 School of Medicine, Western Sydney University , Sydney, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daphne Foong Vincent Ho 5 School of Medicine, Western Sydney University , Sydney, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christopher N. Andrews 2 Alimetry Ltd. , Auckland, New Zealand 6 The Division of Gastroenterology, Cumming School of Medicine, University of Calgary , Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Armen Gharibans 1 The Department of Surgery, The University of Auckland , New Zealand 2 Alimetry Ltd. , Auckland, New Zealand 7 Auckland Bioengineering Institute, The University of Auckland , New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Armen Gharibans Greg O’Grady 1 The Department of Surgery, The University of Auckland , New Zealand 2 Alimetry Ltd. , Auckland, New Zealand 7 Auckland Bioengineering Institute, The University of Auckland , New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Greg O’Grady Stefan Calder 1 The Department of Surgery, The University of Auckland , New Zealand 2 Alimetry Ltd. , Auckland, New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stefan Calder For correspondence: stefan.calder{at}auckland.ac.nz Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background and aims Chronic gastroduodenal symptoms may be associated with psychological factors; however, recent evidence suggests these associations vary by previously undetermined disease factors. Body surface gastric mapping (BSGM) is a non-invasive diagnostic method integrating high-resolution myoelectrical measurement and validated symptom profiling. This cross-sectional study investigated associations between psychological factors and BSGM phenotypes. Methods Patients from the general community meeting the Rome IV Criteria for functional dyspepsia or chronic nausea and vomiting syndrome underwent BSGM using Gastric Alimetry ® . The test protocol included a 30-min fasting baseline, 482 kCal meal, and 4-hr postprandial recording. Measures of depression, anxiety, stress, and quality of life were assessed at baseline, and symptoms were logged throughout the test. BSGM phenotypes were classified using established rule-based criteria. Results Among 278 patients (mean age 39.5, 15-88; 77% female), clinical diagnoses of depression (45%) and anxiety (46%) were common. Depression, anxiety, and stress measures were positively associated with symptom severity; however, these associations varied substantially by BSGM phenotype. Abnormal rhythm stability predicted higher depression ( B =0.35, p =.044) and stress ( B =1.48, p =.026). Among patients with normal spectral metrics, continuous symptoms predicted higher levels of depression ( B =0.42, p =.003), anxiety ( B =0.30, p =.045), and stress ( B =1.43, p =.008), and worse quality of life ( B =-0.57, p < .001); while sensorimotor symptoms predicted higher anxiety ( B =0.46, p =.029) and worse quality of life ( B =-0.49, p =.033). Conclusion This study confirms significant connections between gastroduodenal symptoms and mental health, but refines these associations to specific BSGM phenotypes. Individuals exhibiting normal spectral metrics alongside continuous or sensorimotor symptoms may particularly benefit from integrated psychological interventions. Introduction Chronic gastroduodenal symptoms, which are hallmarks of functional dyspepsia (FD) and chronic nausea and vomiting syndrome (CNVS), place a substantial strain on patients and healthcare systems globally 1 , 2 . Due to heterogeneous pathophysiologies and overlapping symptomatology, affected patients often endure extensive testing and trial-and-error treatment, often with little effective symptom relief despite high healthcare utilization 3 , 4 . Improved diagnostic pathways and targeted therapies are needed to advance patient care. Gastroduodenal disorders are multifactorial, with biopsychosocial models emphasizing the complex interplay of biological, social, and psychological factors in the onset, exacerbation, and maintenance of symptoms 5 , 6 . Psychological comorbidities are prevalent, with depression, anxiety, and stress being highly correlated with symptom severity, likely mediated via the gut-brain axis 7 , 8 . Consequently, psychological interventions, including cognitive behavioral therapy (CBT) and hypnotherapy, have shown promise in improving both mental health and gastrointestinal symptoms in clinical studies 9 – 11 . However, current frameworks, including the Disorders of Gut–Brain Interaction (DGBI) nomenclature, may inadvertently imply a uniform role for psychological factors across all patients, potentially obscuring important physiological heterogeneity 12 . Recent translational advances are enabling a more granular understanding of the mechanisms of gastroduodenal symptoms 13 , 14 . One method to categorize these patients is body surface gastric mapping (BSGM), a new non-invasive test of gastric function that integrates high-resolution myoelectrical measurement with concurrent, validated symptom profiling 14 – 16 . This technology enables detailed characterization of patients with FD or CNVS into disease mechanism-specific subgroups (‘phenotypes’) 14 , 16 – 19 . Recent studies have indicated that BSGM phenotypes may reflect meaningful physiological differences that can inform diagnosis, reduce reliance on more generic labels, and improve healthcare utilization 14 , 20 – 22 . Preliminary evidence using BSGM has revealed links between psychological factors and specific BSGM test phenotypes 14 , 17 , 19 , 23 , 24 . Specifically, patients with normal gastric myoelectrical activity have been found to report higher depression and anxiety levels compared to those with detectable gastric dysfunction in preliminary studies 14 . These data have helped to inform an emerging BSGM classification scheme that could improve diagnostic specificity and targeted care 25 – 27 . This study, therefore, now aimed to comprehensively investigate the association between psychological factors and the spectral metrics, symptom patterns, and phenotypes derived from BSGM in patients with FD and CNVS in a large cohort. Materials and Methods Data were collected in Auckland (New Zealand), Calgary (Canada), and Western Sydney (Australia) as part of an international BSGM consortium study. Ethical approvals were granted by the Auckland Health Research Ethics Committee (AHREC; AH1130 & AH27068), the University of Calgary Conjoint Health Research Ethics Board (REB19-1925), and the Human Research Ethics Committee at Western Sydney (H15157). All participants provided written informed consent. Patients with chronic gastroduodenal symptoms were recruited from the general community to complete a standard BSGM test using Gastric Alimetry® (Alimetry, New Zealand) alongside a battery of psychometrics as part of a multi-national consortium database study, on behalf of the BSGM Consortium. Patients were included if they met the Rome IV symptom Criteria for FD or CNVS 28 . Exclusion criteria included pregnancy or breastfeeding, previous major gastric surgery, as well as standard cautions against Gastric Alimetry testing (e.g. adhesive allergies or damaged epigastric skin) 25 . All tests occurred between June 2022 and September 2024. Procedure Participants underwent a Gastric Alimetry test following standardized procedures 14 , 16 , 18 , 25 . Tests were conducted in the morning after an overnight fast of at least six hours. Participants abstained from taking medications affecting gastrointestinal motility for 48 hours before the test, including promotility agents and opiates, and avoided caffeine and nicotine on the test day 18 . The Gastric Alimetry system consists of a stretchable 8×8 electrode array on an adhesive patch and a wearable reader, which is placed over the epigastrium. After a 30-minute fasting baseline recording, participants consumed a standardized 482 kcal test meal, followed by a 4-hour postprandial recording 18 . The standard meal was an oatmeal energy bar (250 kcal, 5 g fat, 45 g carbohydrate, 10 g protein, 7 g fiber; Clif Bar & Company, CA, USA) and Ensure nutrient drink (232 kcal, 250 mL; Abbott Nutrition, IL, USA), or a calorie-matched diabetic or gluten-free alternative. Measures BSGM metrics A spectrogram is generated for each test, presenting the frequency and amplitude of gastric activity over time 16 . Three spectral metrics are then calculated 25 , 29 , 30 : BMI-Adjusted Amplitude (µV): characterizes the strength of the recorded gastric activity as an average of the whole-test amplitude, adjusted for body mass index (BMI) Gastric Alimetry Rhythm Index™ (GA-RI): a measure of the concentration of amplitude within the gastric frequency band over time (between 0-1), reflecting the rhythmic stability of gastric activity (also adjusted for BMI) Principal Gastric Frequency (cpm): characterizes the frequency associated with stable, consistent gastric activity (as defined by the GA-RI) Gastroduodenal symptoms Participants used the validated Gastric Alimetry App to record their gastroduodenal symptoms during the test 15 . Nausea, bloating, upper gut pain, heartburn, stomach burn, and excessive fullness were rated every 15 minutes on a scale of 0 (none) to 10 (most severe imaginable). Early satiety was assessed once after the meal, on the same scale. Average symptom scores during the test were calculated for each continuous symptom. A total symptom burden score was calculated by summing the average symptom scores, plus the early satiety score 15 . The app also recorded symptom events for reflux, belching, and vomiting. Psychometrics The following psychometrics were measured during the fasted baseline period: the Patient Health Questionnaire 9 (PHQ-9) 31 to measure depression, the Generalized Anxiety Disorder 7 (GAD-7) 32 to measure anxiety, the Perceived Stress Scale 4 (PSS-4) 33 to measure chronic stress, and the Patient Assessment of Upper GastroIntestinal Disorders-Quality of Life (PAGI-QoL) to measure quality of life 34 . Patients also self-reported whether they had a clinical diagnosis of anxiety or depression. Data analysis All patients were grouped based on their BSGM metrics and symptom profiles using rule-based criteria, aligning with emerging phenotypes currently undergoing further refinement (see Figure 1 ) 26 , 27 . Firstly, patients were independently categorized into distinct spectral phenotypes based on the normative reference intervals of the three BSGM metrics 25 , 30 . Gastric dysfunction was categorized as low rhythm stability (GA-RI <0.25), low amplitude (BMI-Adjusted Amplitude 70 μV), high frequency (>3.35 cpm), and low frequency (<2.65 cpm) 25 . If a patient did not fall outside the normative reference intervals for all three metrics, they were classified as ‘normal on the day’. Download figure Open in new tab Figure 1. Flow diagram representing the BSGM phenotypes. First, tests are assessed for the presence of a spectral phenotype (green boxes). Tests with normal spectral analyses proceed to symptom-based classifications, with potentially multiple symptom labels (blue boxes) assigned to a single test. Spectrograms and symptom curves provide a representative example from the cohort. Subsequently, patients with normal spectral metrics were further classified using symptom profiles if any of their symptoms measured from the validated Gastric Alimetry app met any of the following symptom patterns, described in detail elsewhere 27 , 30 . Six symptom profiles were applied here to comprehensively explore potential clinical relationships 35 – 37 . Continuous: symptom remains elevated (> 2/10) with minimal variation (range .5) between the symptom severity and gastric amplitude Post-gastric : symptom severity increases after postprandial gastric activity has concluded Meal-induced: symptom severity increases after meal consumption: average 1st hr post-meal symptoms - pre-meal symptoms > 2 Meal-alleviated: symptom severity decreases after meal consumption: average 1st hr post-meal symptoms - pre-meal symptoms < -2 Activity-relieved: symptom severity decreases with increased gastric response Patients could be assigned multiple symptom profiles if they had symptoms with differing patterns. Statistical analysis Data were analyzed using IBM SPSS Statistics v29. Statistical significance was set to p <.05. Means ( M ) and standard deviations ( SD ) were reported unless otherwise stated. The PHQ-9 and GAD-7 total scores were transformed using a natural log transformation to address normality violations and logged values were used in all subsequent analyses. Pearson’s correlations were conducted to assess the relationships between psychometric scores, symptoms, and BSGM metrics, both for the overall sample and within distinct Rome diagnostic groups (FD, CNVS, postprandial distress syndrome (PDS), and epigastric pain syndrome (EPS) alone, and overlapping CNVS/FD). Independent samples t-tests were performed to quantify the differences in the symptoms and spectral metrics between those with and without a clinical diagnosis of anxiety and depression. One-way ANOVAs were used to evaluate psychometric differences among the Rome diagnostic groups. Independent multivariable linear regressions were conducted to assess the associations between the spectral phenotypes and symptom profiles, and the psychometrics. The spectral phenotypes were dummy-coded in reference to the ‘normal on the day’ phenotype. Symptom profiles were coded as 1 in those with normal spectral metrics if any symptom experienced during the test met the relevant symptom pattern. These associations were adjusted for age, sex, and BMI, and outputs were reported with 95% confidence intervals to assess statistical significance. Four independent regression models were performed, one for each outcome variable (lnPHQ-9 total score, lnGAD-7 total score, PSS-4 total score, and PAGI-QoL total score). Results Sample characteristics The sample comprised 278 patients ( M age 39.52 years, range 15-88 years; 77% female; M BMI 26.14). The majority of the sample (n=193, 69%) met Rome criteria for both CNVS and FD. Only a smaller proportion met criteria for FD alone (n=73, 26%) or CNVS alone (n=12, 4%). Gastric emptying data were not collected. On average, the sample reported moderate levels of depression ( M = 11.02, SD = 6.80), mild levels of anxiety ( M = 6.72, SD = 5.56), and moderate levels of stress ( M = 7.29, SD = 3.31). Additionally, nearly half self-reported a clinical diagnosis of depression (45%) and anxiety (46%). Symptom-psychometric relationships As shown in Figure 2 , Pearson’s correlations revealed significant positive associations between most symptoms and psychometric scores, with the strongest effect sizes observed for quality of life and depression. Total symptom burden, nausea, upper gut pain, and stomach burn showed the strongest associations. Download figure Open in new tab Figure 2. Correlation matrix showing the significant Pearson correlation coefficients between the psychometric scales (orange), symptom scores (purple), and demographic variables (green). Significance was determined using a Benjamini-Hochberg correction for multiple comparisons with a false discovery rate of 0.1. Furthermore, as shown in Table 1 , independent-sample t-tests revealed that patients with a self-reported clinical diagnosis of anxiety or depression experienced significantly higher symptom levels compared to those without such a diagnosis. The strongest effects were observed in patients with a depression diagnosis. Specifically, nausea showed the largest effect sizes for both depression ( d = −0.47) and anxiety ( d = −0.42). Upper gut pain, bloating, heartburn, reflux, and total symptom burden also demonstrated moderate effect sizes (all d > View this table: View inline View popup Download powerpoint Table 1. Independent samples t-tests comparing gastroduodenal symptoms between those patients with and without self-reported clinical diagnoses of depression and anxiety .30). Relationships across Rome groupings A greater number of Rome diagnoses was associated with significantly poorer mental health outcomes. Specifically, patients meeting the criteria for both FD and CNVS exhibited significantly worse depression, anxiety, stress, and quality of life compared to those with FD alone (all p < .001) and significantly worse stress and quality of life than those with CNVS alone (all p < .031). Furthermore, the relationships between symptoms and psychometrics varied across different diagnostic groups. Patients with CNVS or PDS alone showed no significant correlations between any psychometrics and symptoms (all p > .103), whilst patients with EPS alone demonstrated only a few significant correlations, specifically between anxiety and belching ( r = .41, p = .032), and between stress and upper gut pain ( r = .49, p = .010). Consistent with the full sample analysis, patients with overlapping CNVS and FD exhibited significant correlations between the majority of symptoms and psychometric measures. Metric-psychometric relationships Pearson’s correlations showed no significant associations between the three BSGM metrics and depression, anxiety, stress, or quality of life (all p > .122), including when split by Rome diagnoses. Similarly, independent-sample t-tests demonstrated no significant differences in the BSGM metrics observed between those with and without a self-reported clinical diagnosis of anxiety or depression (all p > .074). BSGM Phenotypes As shown in Figure 3 , the majority of patients had normal spectral metrics with continuous and/or meal-induced symptom profiles. Download figure Open in new tab Figure 3. Distribution of (A) spectral phenotypes and (B) symptom profiles in patients with normal spectral metrics. The regression analyses (see Table 2 ) revealed several significant associations between the BSGM phenotypes and the psychometrics. Having an abnormal rhythm stability (‘Dysrhythmic Phenotype’) significantly predicted higher depression ( p = .044) and stress scores ( p = .026), which was the only spectral phenotype associated with the psychometrics. Normal spectral metrics combined with continuous symptoms (‘Continuous Phenotype’) predicted higher depression ( p = .003), anxiety ( p = .045), and stress ( p = .008), and lower quality of life ( p < .001). Additionally, normal spectral metrics paired with sensorimotor symptoms (‘Sensorimotor Phenotype’) predicted higher anxiety ( p = .029) and lower quality of life ( p = .033). Lastly, normal spectral metrics combined with activity-relieved symptoms predicted higher stress ( p = .043) and post-gastric symptoms predicted lower depression ( p = .013). View this table: View inline View popup Table 2. Regression statistics showing associations between the body surface gastric mapping phenotypes based on spectral metrics and symptom profiling, and depression (PHQ-9), anxiety (GAD-7), stress (PSS-4), and quality of life (PAGI-QoL). Discussion This international BSGM consortium study examined the associations between psychological factors and emerging BSGM test phenotypes in patients with FD and CNVS. As anticipated, our patient cohort exhibited considerably elevated levels of depression, anxiety, and stress compared to those observed in the general population 38 – 40 ; with levels of clinical diagnoses over 40% higher than the general population 41 . These psychological variables were significantly associated with symptom severity, particularly in patients with overlapping FD and CNVS. This aligns with literature indicating that patients with overlapping DGBI diagnoses often experience worse mental health and gastrointestinal symptoms 42 , 43 . However, the observed psychological associations varied systematically according to BSGM-defined phenotypes, rather than reflecting a generic psychological overlay. The regression analyses performed here reveal distinct psychometric profiles across the BSGM disease phenotypes, supporting the utility of this emerging classification system for identifying clinically meaningful patient subgroups. Specifically, having an abnormal rhythm stability, indicated by a low GA-RI (‘Dysrhythmic Phenotype’), predicted higher levels of depression, potentially reflecting the debilitating impact of more severe gastric dysregulation on mental health. A similar observation was recently observed in a separate adolescent population with abnormal rhythms, who also showed more severe symptoms and reduced quality of life 24 . While the directionality of this relationship now warrants further investigation, it highlights the importance of measuring and addressing mental health, even in patients with significant gastric dysfunction. Within the current BSGM classification hierarchy, dysrhythmia warrants primary pathophysiological investigation and management, with psychological support serving as a complementary, though important consideration 27 . In contrast, abnormal Principal Gastric Frequency and BMI-Adjusted Amplitude did not exhibit any significant relationships with the measured psychological variables. This suggests that these abnormalities may be less indicative of gut-brain dysfunction and more likely to reflect underlying gastric motility disturbances, gastric outlet resistance, vagal nerve dysfunction, or gastric hypomotility, as supported by previous studies 14 , 17 , 18 , 44 . Conversely, multiple significant associations emerged between the psychometrics and symptom patterns in specific patient subgroups with normal spectral metrics. This aligns with prior research demonstrating that individuals with normal spectral metrics on BSGM displayed higher levels of depression and anxiety than those with identifiable gastric abnormalities on spectral analysis 14 , 45 . Notably, patients with normal spectral metrics and continuous symptom patterns (‘Continuous Phenotype’) predicted significantly higher depression, anxiety, and stress, and lower quality of life; the only group to have relationships with all four psychological variables measured. The lack of association between symptoms and gastric activity in this subgroup, coupled with high psychological comorbidities, suggests gut-brain dysregulation as a contributing symptom driver, potentially in tandem with other mediators such as chronic inflammation 46 . This particular BSGM pattern could serve as a proxy for a clinical diagnosis of the EPS subtype of FD 28 , suggesting that BSGM testing could offer a more robust detection tool than symptom-based criteria alone. Furthermore, normal spectral metrics combined with sensorimotor symptoms (‘Sensorimotor Phenotype’) were associated with higher anxiety and lower quality of life. We hypothesize that pathophysiology in this subgroup may be linked to visceral hypersensitivity, where normal digestive sensations are perceived as painful or symptomatic, potentially leading to food-related anxiety or altered interoception 47 , 48 . This finding aligns with hypotheses generated from previous research in patients with sensorimotor symptoms 19 , 23 , 27 , 49 . Consequently, we hypothesize that this group may benefit from interventions targeting visceral hypersensitivity and interoception, including neuromodulators, vagal stimulation, or psychological therapies aimed at modifying symptom perception 50 . This particular BSGM pattern could also serve as a proxy for a clinical diagnosis of the PDS subtype of FD 28 ; offering the additional advantage of ruling out neuromuscular dysfunction via a normal spectral phenotype. While other symptom profiles, such as activity-relieved and post-gastric, showed associations with depression and stress, these phenotypes remain uncommon in our database, and as such, these findings require cautious interpretation. Further work is required to define the clinical utility of these symptom patterns. Additionally, the most prevalent symptom tag, meal-induced, comprising 31% of the sample, exhibited no significant associations with any of the psychometric measures. This suggests that this particular symptom pattern may not be associated with neither gut-brain dysregulation nor gastric myoelectrical dysfunction, potentially implicating other known gastroduodenal pathophysiologies not measured in this study, such as immune activation 51 , 52 . Further investigation into this subgroup is warranted to elucidate the mechanisms underlying their meal-induced symptomatology. Overall, these findings support the value of integrating BSGM metrics, symptom profiles, and psychological assessments to enhance the precision of patient phenotyping, in gastroduodenal disorders. We anticipate that this approach could enable more tailored, mechanism-informed management strategies. Specifically, we hypothesize that patients with normal spectral metrics and continuous or sensorimotor symptoms may receive greater benefit from multidisciplinary approaches incorporating psychological interventions, such as CBT, hypnotherapy, or biofeedback, which have demonstrated efficacy in addressing both gastrointestinal symptoms and mental health 10 , 11 , 53 . Conversely, patients exhibiting spectral abnormalities may be more responsive to targeted medical approaches, including pharmacological interventions and surgical procedures 21 . Studies have indicated that this personalized approach, guided by BSGM profiling, can lead to improved healthcare utilization and cost reductions 20 , 54 . In addition, guiding patients towards appropriate psychological interventions is crucial given the limited access to these therapies 55 . Additional studies are ongoing to further assess these relationships. The current findings further support the recent integration of the Alimetry Gut-Brain Wellbeing (AGBW) Survey 56 into the standard Gastric Alimetry BSGM test protocol, in order to offer a more holistic assessment of patients with gastroduodenal disorders. This validated tool evaluates depression, anxiety, and stress in patients with chronic gastroduodenal symptoms. Future research is expected to leverage the AGBW Survey to further evaluate the associations between its scores and the BSGM metrics, symptom patterns, and phenotypes. This study has several limitations. Firstly, the observational design precludes causal inference between BSGM phenotypes and mental health outcomes. Longitudinal and interventional trials would be needed to confirm the proposed mechanisms and delineate the temporal relationship between symptoms and psychological comorbidity, though such studies are resource-intensive and logistically challenging. Secondly, the Western countries sampled, although international, may limit the global generalizability of our findings. Lastly, despite the relatively large sample size, certain phenotypes and symptom profiles were uncommon, potentially affecting the accuracy of the psychological profiling in these subgroups. Further studies with larger cohorts and targeted recruitment would be needed to ensure adequate representation of such patient subgroups. Alternatively, if these subgroups continue to offer marginal clinical benefit, they may be excluded from future classification systems to enhance clarity and practicality. In summary, this study shows substantial psychological comorbidity in chronic gastroduodenal disorders, which is not uniform but varies strongly by BSGM phenotypes. Psychological burden was highest in patients with normal gastric rhythms and continuous or sensorimotor symptoms, suggesting these groups are most likely to benefit from psychological interventions. These findings support the role of multimodal BSGM testing protocols in identifying specific phenotypes to guide more targeted, integrative care. Data Availability Deidentified data will be made available upon reasonable request to the corresponding author. Disclosures GOG and AG hold intellectual property and grants in gastric electrophysiology and are Directors of the University of Auckland spin-out company Alimetry Ltd. ML, GS, BW, CD, DF, CNA, and SC are members of Alimetry Ltd. The remaining authors have no relevant conflicts to declare. Grant Support This study is funded by a New Zealand Health Research Council Programme Grant 3715588. Data transparency statement Deidentified data will be made available upon reasonable request to the corresponding author. Footnotes ↵ * Joint Senior Authors References 1. ↵ Drossman DA . Functional gastrointestinal disorders: History, pathophysiology, clinical features and Rome IV . Gastroenterology 2016 ; 150 : 1262 – 1279 . Available at : doi: 10.1053/j.gastro.2016.02.032 . OpenUrl CrossRef PubMed 2. ↵ Stanghellini V , Chan FKL , Hasler WL , et al. Gastroduodenal disorders . Gastroenterology 2016 ; 150 : 1380 – 1392 . Available at : doi: 10.1053/j.gastro.2016.02.011 . OpenUrl CrossRef PubMed 3. ↵ Sebaratnam G , Law M , Broadbent E , et al. It’s a helluva journey: A qualitative study of patient and clinician experiences of nausea and vomiting syndromes . Front Psychol 2023 ; 14 : 1232871 . Available at : doi: 10.3389/fpsyg.2023.1232871 . OpenUrl CrossRef PubMed 4. ↵ Dudekula A , O’Connell M , Bielefeldt K. Hospitalizations and testing in gastroparesis . J Gastroenterol Hepatol 2011 ; 26 : 1275 – 1282 . Available at : doi: 10.1111/j.1440-1746.2011.06735.x . OpenUrl CrossRef PubMed 5. ↵ Luo Y , Keefer L . Role of psychological questionnaires in clinical practice and research within functional gastrointestinal disorders . Neurogastroenterol Motil 2021 ; 33 : e14297 . Available at : doi: 10.1111/nmo.14297 . OpenUrl CrossRef PubMed 6. ↵ Ballou S , Vasant DH , Guadagnoli L , et al. A primer for the gastroenterology provider on psychosocial assessment of patients with disorders of gut-brain interaction . Neurogastroenterol Motil 2024 : e14894 . Available at : doi: 10.1111/nmo.14894 . OpenUrl CrossRef 7. ↵ Van Oudenhove L , Crowell MD , Drossman DA , et al. Biopsychosocial aspects of functional gastrointestinal disorders . Gastroenterology 2016 ; 150 : 1355 – 1367 . Available at : doi: 10.1053/j.gastro.2016.02.027 . OpenUrl CrossRef 8. ↵ Knowles SR , Skvarc D , Ford AC , et al. Negative impact of disorders of gut-brain interaction on health-related quality of life: Results from the Rome Foundation Global Epidemiology Survey . Gastroenterology 2023 ; 164 : 655 – 668 .e10. Available at: https://www.ncbi.nlm.nih.gov/pubmed/36565940 . OpenUrl PubMed 9. ↵ Rodrigues DM , Motomura DI , Tripp DA , et al. Are psychological interventions effective in treating functional dyspepsia? A systematic review and meta-analysis . J Gastroenterol Hepatol 2021 ; 36 : 2047 – 2057 . Available at : doi: 10.1111/jgh.15566 . OpenUrl CrossRef PubMed 10. ↵ Keefer L , Ballou SK , Drossman DA , et al. A Rome working team report on brain-gut behavior therapies for disorders of gut-brain interaction . Gastroenterology 2022 ; 162 : 300 – 315 . Available at : doi: 10.1053/j.gastro.2021.09.015 . OpenUrl CrossRef PubMed 11. ↵ Law M , Pickering I , Bartlett E , et al. Cognitive behavioural therapy-based interventions for gastroduodenal disorders of gut-brain interaction: A systematic review . J Psychosom Res 2023 ; 175 : 111516 . Available at : doi: 10.1016/j.jpsychores.2023.111516 . OpenUrl CrossRef 12. ↵ Pasricha PJ , Talley NJ , Nguyen L , et al. Names Matter: A Call to Neurogastroenterologists to Examine How We Can Improve the Nomenclature We Use to Describe the Disorders Our Patients Experience . Gastroenterology 2025 ; 169 : 9 – 11 . Available at : doi: 10.1053/j.gastro.2024.12.040 . OpenUrl CrossRef PubMed 13. ↵ Ford AC , Mahadeva S , Carbone MF , et al. Functional dyspepsia . Lancet 2020 ; 396 : 1689 – 1702 . Available at : doi: 10.1016/S0140-6736(20)30469-4 . OpenUrl CrossRef PubMed 14. ↵ Gharibans AA , Calder S , Varghese C , et al. Gastric dysfunction in patients with chronic nausea and vomiting syndromes defined by a noninvasive gastric mapping device . Sci Transl Med 2022 ; 14 :eabq3544. Available at : doi: 10.1126/scitranslmed.abq3544 . OpenUrl CrossRef 15. ↵ Sebaratnam G , Karulkar N , Calder S , et al. Standardized system and app for continuous patient symptom logging in gastroduodenal disorders: Design, implementation, and validation . Neurogastroenterol Motil 2022 : e14331 . Available at : doi: 10.1111/nmo.14331 . OpenUrl CrossRef 16. ↵ Gharibans A , Hayes T , Carson D , et al. A novel scalable electrode array and system for non-invasively assessing gastric function using flexible electronics . Neurogastroenterol Motil 2023 ; 35 : e14418 . Available at : doi: 10.1111/nmo.14418 . OpenUrl CrossRef PubMed 17. ↵ Xu W , Gharibans AA , Calder S , et al. Defining and phenotyping gastric abnormalities in long-term type 1 diabetes using a novel body surface gastric mapping device . Gastro Hep Adv 2023 ; 2 : 1120 – 1132 . Available at : doi: 10.1016/j.gastha.2023.08.005 . OpenUrl CrossRef 18. ↵ O’Grady G , Varghese C , Schamberg G , et al. Principles and clinical methods of body surface gastric mapping: Technical review . Neurogastroenterol Motil 2023 ; 35 : e14556 . Available at : doi: 10.1111/nmo.14556 . OpenUrl CrossRef 19. ↵ Wang WJ , Foong D , Calder S , et al. Gastric Alimetry® expands patient phenotyping in gastroduodenal disorders compared to gastric emptying scintigraphy . Am J Gastroenterol 2023 ; 119 : 331 – 341 . Available at: http://journals.lww.com/ajg . OpenUrl PubMed 20. ↵ Xu W , Williams L , Sebaratnam G , et al. Gastric Alimetry® testing and healthcare economic analysis in nausea and vomiting syndromes . Dig Dis Sci 2024 ; 69 : 2304 – 2314 . Available at : doi: 10.1007/s10620-024-08455-0 . OpenUrl CrossRef 21. ↵ Varghese C , Daker C , Lim A , et al. Gastric Alimetry® in the management of chronic gastroduodenal disorders: Impact to diagnosis and healthcare utilization . Clin Transl Gastroenterol 2023 ; 14 : e00626 . Available at: http://journals.lww.com/ctg . OpenUrl 22. ↵ Varghese C , Van Hove S , Gabe S , et al. Predicting symptomatic response to prokinetic treatment using Gastric Alimetry . Neurogastroenterol Motil . 23. ↵ Varghese C , Schamberg G , Uren E , et al. Standardized mechanism-based digital profiling of gastroduodenal symptoms . SSRN 2023 . Available at : doi: 10.2139/ssrn.4517181 . OpenUrl CrossRef 24. ↵ Humphrey G , Keane C , Schamberg G , et al. Body Surface Gastric Mapping delineates specific patient phenotypes in adolescents with functional dyspepsia and gastroparesis . Neurogastroenterol Motil 2025 ; 37 : e70018 . Available at: https://onlinelibrary.wiley.com/doi/10.1111/nmo.70018 . OpenUrl PubMed 25. ↵ Varghese C , Schamberg G , Calder S , et al. Normative values for body surface gastric mapping evaluations of gastric motility using Gastric Alimetry: Spectral analysis . Am J Gastroenterol 2023 ; 118 : 1047 – 1057 . Available at : doi: 10.14309/ajg.0000000000002077 . OpenUrl CrossRef 26. ↵ O’Grady G , Varghese C , Schamberg G , et al. An initial phenotype set for the assessment of gastroduodenal disorders with Gastric Alimetry® . SSRN 2023 . Available at : doi: 10.2139/ssrn.4425865 . OpenUrl CrossRef 27. ↵ Varghese C , Schamberg G , Uren E , et al. A standardized classification scheme for gastroduodenal disorder evaluation using the Gastric Alimetry system: Prospective cohort study . Gastro Hep Adv 2024 ; 4 : 100547 . Available at: https://linkinghub.elsevier.com/retrieve/pii/S2772572324001419 . 28. ↵ The Rome Foundation . Appendix A: Rome IV Diagnostic Criteria for FGIDs . 2016 , January 16. Available at: https://theromefoundation.org/rome-iv/rome-iv-criteria/ . 29. ↵ Schamberg G , Varghese C , Calder S , et al. Revised spectral metrics for body surface measurements of gastric electrophysiology . Neurogastroenterol Motil 2022 . Available at: https://onlinelibrary.wiley.com/doi/10.1111/nmo.14491 . 30. ↵ Foong D , Calder S , Varghese C , et al. Gastric Alimetry® test interpretation in gastroduodenal disorders: Review and recommendations . J Clin Med Res 2023 ; 12 : 6436 . Available at : doi: 10.3390/jcm12206436 . OpenUrl CrossRef 31. ↵ Kroenke K , Spitzer RL , Williams JB . The PHQ-9: Validity of a brief depression severity measure . J Gen Intern Med 2001 ; 16 : 606 – 613 . Available at : doi: 10.1046/j.1525-1497.2001.016009606.x . OpenUrl CrossRef PubMed Web of Science 32. ↵ Spitzer RL , Kroenke K , Williams JBW , et al. A brief measure for assessing generalized anxiety disorder: The GAD-7 . Arch Intern Med 2006 ; 166 : 1092 – 1097 . Available at : doi: 10.1001/archinte.166.10.1092 . OpenUrl CrossRef PubMed Web of Science 33. ↵ Cohen S , Kamarck T , Mermelstein R . A global measure of perceived stress . J Health Soc Behav 1983 ; 24 : 385 – 396 . Available at: https://www.ncbi.nlm.nih.gov/pubmed/6668417 . OpenUrl CrossRef PubMed Web of Science 34. ↵ Loge C de la , Trudeau E , Marquis P , et al. Cross-cultural development and validation of a patient self-administered questionnaire to assess quality of life in upper gastrointestinal disorders: The PAGI-QOL . Qual Life Res 2005 ; 13 : 1751 – 1762 . Available at : doi: 10.1007/s11136-004-8751-3 . OpenUrl CrossRef 35. ↵ Bisschops R , Karamanolis G , Arts J , et al. Relationship between symptoms and ingestion of a meal in functional dyspepsia . Gut 2008 ; 57 : 1495 – 1503 . Available at : doi: 10.1136/gut.2007.137125 . OpenUrl Abstract / FREE Full Text 36. Vanheel H , Vanuytsel T , Van Oudenhove L , et al. Postprandial symptoms originating from the stomach in functional dyspepsia . Neurogastroenterol Motil 2013 ; 25 : 911 – e703 . Available at : doi: 10.1111/nmo.12227 . OpenUrl CrossRef PubMed 37. ↵ Carbone F , De Buysscher R , Van den Houte K , et al. Relationship between gastric emptying rate and simultaneously assessed symptoms in functional dyspepsia . Clin Gastroenterol Hepatol 2022 ; 20 : e429 – e437 . Available at: https://linkinghub.elsevier.com/retrieve/pii/S1542-3565(21)00333-5 . OpenUrl PubMed 38. ↵ Kocalevent R , Hinz A , Brähler E . Standardization of the depression screener Patient Health Questionnaire (PHQ-9) in the general population . Gen Hosp Psychiatry 2013 ; 35 : 551 – 555 . Available at : doi: 10.1016/j.genhosppsych.2013.04.006 . OpenUrl CrossRef PubMed Web of Science 39. Löwe B , Decker O , Müller S , et al. Validation and standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the general population . Med Care 2008 ; 46 : 266 – 274 . Available at: https://www.jstor.org/stable/40221654 . OpenUrl CrossRef PubMed Web of Science 40. ↵ Warttig SL , Forshaw MJ , South J , et al. New, normative, English-sample data for the Short Form Perceived Stress Scale (PSS-4) . J Health Psychol 2013 ; 18 : 1617 – 1628 . Available at : doi: 10.1177/1359105313508346 . OpenUrl CrossRef PubMed 41. ↵ Institute of Health Metrics and Evaluation . Global Health Data Exchange (GHDx) 2021 . Available at: https://vizhub.healthdata.org/gbd-results/ . 42. ↵ Sperber AD , Freud T , Aziz I , et al. Greater overlap of Rome IV disorders of gut-brain interactions leads to increased disease severity and poorer quality of life . Clin Gastroenterol Hepatol 2022 ; 20 : e945 – e956 . Available at: https://linkinghub.elsevier.com/retrieve/pii/S1542-3565(21)00580-2 . OpenUrl PubMed 43. ↵ Barbara G , Aziz I , Ballou S , et al. Rome Foundation Working Team Report on overlap in disorders of gut-brain interaction . Nat Rev Gastroenterol Hepatol 2025 . Available at : doi: 10.1038/s41575-024-01033-9 . OpenUrl CrossRef 44. ↵ Carson DA , Robertson S , Wang TH-H , et al. The impact and clinical implications of gastric surgery on the gastric conduction system . Foregut 2023 ; 3 : 29 – 44 . Available at : doi: 10.1177/26345161221130117 . OpenUrl CrossRef 45. ↵ Xu W , Wang T , Foong D , et al. Characterization of gastric dysfunction after fundoplication using body surface gastric mapping . J Gastrointest Surg 2024 ; 28 : 236 – 245 . Available at: https://www.sciencedirect.com/science/article/pii/S1091255X23087528 . OpenUrl PubMed 46. ↵ Abell TL , Kedar A , Stocker A , et al. Pathophysiology of gastroparesis syndromes includes anatomic and physiologic abnormalities . Dig Dis Sci 2021 ; 66 : 1127 – 1141 . Available at : doi: 10.1007/s10620-020-06259-6 . OpenUrl CrossRef PubMed 47. ↵ Privitera R , Anand P. Visceral pain and hypersensitivity disorders . In: Hsieh S-T , Anand P , Gibbons CH , et al. , eds. Small Fiber Neuropathy and Related Syndromes: Pain and Neurodegeneration . Singapore : Springer ; 2019 : 131 – 139 . Available at: http://link.springer.com/10.1007/978-981-13-3546-4_12 . 48. ↵ Verdonk C , Ajijola OA , Khalsa SS . Toward a multidisciplinary neurobiology of interoception and mental health . Curr Opin Neurobiol 2025 ; 94 : 103084 . Available at : doi: 10.1016/j.conb.2025.103084 . OpenUrl CrossRef 49. ↵ O’Grady G , Carbone F , Tack J . Gastric sensorimotor function and its clinical measurement . Neurogastroenterol Motil 2022 : e14489 . Available at : doi: 10.1111/nmo.14489 . OpenUrl CrossRef 50. ↵ Alam MJ , Chen JDZ . Non-invasive neuromodulation: an emerging intervention for visceral pain in gastrointestinal disorders . Bioelectron Med 2023 ; 9 : 27 . Available at : doi: 10.1186/s42234-023-00130-5 . OpenUrl CrossRef 51. ↵ Talley NJ , Peterson KA , Genta RM , et al. High discovery rate of duodenal and gastric eosinophilia in patients with unexplained moderate-severe abdominal symptoms: A prospective US multisite study . Gastroenterology 2023 ; 164 : 680 – 683 .e2. Available at : doi: 10.1053/j.gastro.2022.12.015 . OpenUrl CrossRef PubMed 52. ↵ Huyghe P , Ceulemans M , Keita ÅV , et al. The duodenal microenvironment in functional dyspepsia . J Neurogastroenterol Motil 2025 ; 31 : 186 – 198 . Available at : doi: 10.5056/jnm24176 . OpenUrl CrossRef 53. ↵ Pereira AG , Fu L , Xu W , et al. The effects of heart rate variability biofeedback on functional gastrointestinal disorders: A scoping review . Front Physiol 2025 ; 16 : 1511391 . Available at: https://www.frontiersin.org/articles/10.3389/fphys.2025.1511391/full . 54. ↵ Varghese C , Daker C , Lim A , et al. Gastric Alimetry in the management of chronic gastroduodenal disorders: Impact to diagnosis and health care utilization . Clin Transl Gastroenterol 2023 ; 14 : e00626 . Available at : doi: 10.14309/ctg.0000000000000626 . OpenUrl CrossRef 55. ↵ Law M , Bartlett E , Sebaratnam G , et al. One more tool in the tool belt: A qualitative interview study investigating patient and clinician opinions on the integration of psychometrics into routine testing for disorders of gut-brain interaction . Cureus 2024 . Available at : doi: 10.7759/cureus.67155 . OpenUrl CrossRef 56. ↵ Law M , Pickering I , Humphrey G , et al. Development and validation of the Alimetry Gut-Brain Wellbeing Survey: A novel patient-reported mental health scale for patients with chronic gastroduodenal symptoms . Front Psychol 2024 ; 15 : 1389671 . Available at: https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1389671/full . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted July 29, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. 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