https://orcid.org/0000-0002-8344-818X
Figures
Abstract
Metabolomic profiles are increasingly being used to identify responders to dietary interventions. Advances using this approach are particularly needed to personalize and enhance the effectiveness of dietary weight loss interventions. Using obese Diversity Outbred (DO) mice that model genetic and phenotypic heterogeneity of human populations, we aimed to identify urinary metabolite signatures associated with responsiveness to calorie restriction (CR)-mediated weight loss. DO mice (150 males, 150 females) were fed a high-fat diet for 12 weeks to induce obesity, then urine was collected and an 8-week CR regimen (30% decrease in energy intake) initiated. At study completion, mice were rank-ordered according to their percent body weight change, with mice in the extreme quartiles deemed CR responders (n = 67) versus nonresponders (n = 67). Targeted semi-quantitative metabolomics identified elevated glutamic acid and hydroxyproline as key urinary metabolites that distinguish CR responders from CR nonresponders, independent of sex. Three urinary metabolites (glutamic acid, hydroxyproline, and putrescine) distinguished male CR responders from nonresponders. Six metabolites (glutamic acid, hydroxyproline, dopamine, histamine, lysine, and spermine) distinguished female CR responders from nonresponders. Multivariate receiver operating characteristic analyses integrated these metabolites to reveal potential sex specific and sex-independent associations of CR-mediated weight loss. Further, pathway analysis identified several metabolic pathways, including arginine and proline metabolism, and alanine, aspartate, and glutamate biosynthesis, that distinguished CR responders from nonresponders and could be indicative of metabolic reprogramming to enhance insulin sensitivity and energy metabolism.
Citation: Paules EM, Trujillo-Gonzalez I, VerHague M, Albright J, Stewart D, Sumner SJ, et al. (2025) Urinary signatures are associated with calorie restriction-mediated weight loss in obese Diversity Outbred mice. PLoS One 20(12): e0329422. https://doi.org/10.1371/journal.pone.0329422
Editor: Masahiro Sugimoto, Tokyo Medical University: Tokyo Ika Daigaku, JAPAN
Received: July 24, 2025; Accepted: November 21, 2025; Published: December 9, 2025
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Data Availability: Datasets analyzed for this study are available in DRYAD at: https://datadryad.org/dataset/doi:10.5061/dryad.8931zcs4n.
Funding: This study was supported by National Institute of Health Grants R35 CA197627 (SDH) and P30 DK056350 (SDH and JEF), the UNC-CH OVCR Creativity Hubs Pilot Award (PGL, JEF, SDH), and the UNC-CH NRI Metabolomics and Exposome Laboratory (SJS). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: Evan M. Paules, PhD. is a Balchem postdoctoral fellow. Balchem had no role in the study design, data collection, analysis, or preparation of the manuscript. The other authors declare no competing interests.
Abbreviations: DO, Diversity outbred; DIO, Diet-induced obesity; CR, Calorie restriction; ROC, Reciver operating characteristic; OPLS-DA, Orthogonal partial least squares discriminant analysis; VIP, Variable importance projection; AUC, Area under the curve
Background
Defined as a body mass index ≥30 kg/m², obesity results from complex interactions among genetic, environmental, and behavioral factors [1–3]. Obesity affects over 40% of U.S. adults [4,5] and significantly raises the risk for chronic diseases, including type 2 diabetes, cardiovascular conditions, and multiple cancers [6]. Efforts to reduce obesity prevalence are often hindered by individual variability in response to weight loss interventions [7,8]. Weight loss interventions tailored to an individual’s anthropometric data and psychosocial factors are more effective in supporting weight loss than generic interventions [9]. Predictive analytics encompassing metabolomic profiles are increasingly being used to forecast responders to treatments in cardiovascular and other chronic diseases [10]. Integrating metabolic predictors into weight loss interventions for obesity could enhance personalization and improve outcomes [11]; however, studies investigating metabolic targets, especially in noninvasively collected biospecimens like urine, for predicting responses to weight loss interventions remain limited [12].
Calorie restriction (CR) is a cost-effective, non-pharmacological weight loss strategy, but achieving and maintaining significant weight loss through CR remains difficult for many [13]. Biomarkers predictive of who will (or will not) favorably respond to CR-mediated weight loss are needed to facilitate more precise and sustainable anti-obesity treatment approaches [8]. In a recent preclinical proof-of-principle study, we demonstrated that serum metabolic biomarkers including leptin can distinguish between obese Diversity Outbred (DO) mice that do, versus do not, respond favorably to CR-mediated weight loss [14]. The DO mice are derived from the partially inbred Collaborative Cross Recombinant Lines, thus creating genetically unique individual mice and replicating the heterogeneity seen in human populations [15]. Moreover, the DO mice exhibit extensive genetic diversity (~45 million variants) and sex-specific metabolic traits, mirroring human responses to low-calorie diets [8,16]. Urine, as compared with serum, plasma and whole blood, offers several advantages as a biospecimen, including noninvasive sampling and ease of larger volume collection [17,18]. In this follow-up study, we investigated whether urinary metabolites could serve as biomarkers of CR responsiveness, hence extending our earlier serum-based findings [14] toward building a more comprehensive predictive signature for weight loss.
Materials and methods
Animal diets and urine collection
All protocols were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC protocol number: 21-003) at the North Carolina Research Campus in Kannapolis, NC. All animal care methods were previously described [14] and thus presented here in brief. Diversity Outbred (J:DO) mice from The Jackson Laboratory (stock #009376) were obtained at 8 weeks of age and were housed at 24oC with a 12-hour light/dark cycle and fed control diet (10 kcal% fat; Research Diets Catalog # D12450J) ad libitum for a 2 week acclimation period, then switched to a high-fat diet (60 kcal% fat; Research Diets #D12492) fed ad libitum for 12 weeks to elicit diet-induced obesity (DIO). Urine was collected using the Hatteras Chiller System (Grantsboro, NC, USA), with mice temporarily housed individually in cages with a wire mesh floor overnight following 12 weeks of the high-fat diet. Throughout the 24-hour collection period, urine samples were kept at 7°C, and after collection, samples were aliquoted and stored at −80°C until analysis. All mice were then housed in individual cages for the remainder of the study, fed the control diet ad libitum for 3 days, then administered a 30% CR diet (Catalog # F0078, Bio-Serv, Flemington, NJ) for 8 weeks. Individual housing ensured each mouse consumed 70% of ad libitum intake. To achieve the CR regimen, all mice received a daily aliquot of food providing 70% of the 3-day average energy intake, and 100% of the vitamins, minerals, and amino acids, consumed during the control diet regimen. Following the 8 weeks of CR, all mice were fasted for 4 hours, following the start of the light cycle, and subsequently euthanized using isoflurane at a dose of 3–4%. Once under deep anesthesia, confirmed by a toe pinch, followed by exanguination and cervical dislocation. Body weight was recorded weekly using a calibrated scale. Body composition, including fat mass and lean mass, was measured at baseline, after DIO, and following CR using an EchoMRI-100H analyzer (EchoMRI LLC, Houston, TX, USA). Sample size was calculated as previously described [14]. Humane endpoints throughout the study included any signs of significant distress (ruffled coat or hunched posture), disease, and weight loss that was greater than 20% within one week and animal health was monitored every day throughout the duration of the study. Nail trimming was implemented to reduce incidence of dermatitis due to excessive grooming. Nestlets, mouse huts/retreats, and gnawing bones were provided to reduce stress and increase enrichment. During the 23 weeks of dietary intervention, 29 mice died spontaneously, without meeting humane endpoint criteria or requiring euthanasia, before the study endpoint (20 males: 14 during DIO induction period, 6 during CR treatment period; 9 females: 6 during DIO induction period, 3 during CR treatment period). Mice that died spontaneously were censored from longitudinal analyses, as phenotypic measurements could not be obtained. However, we acknowledge that some morbidities (e.g., abnormal ambulation, rapid weight loss) could be associated with the phenotypes of interest.
Targeted semiquantified metabolomics analysis
Blinded urine samples were received by the UNC Metabolomics and Exposome Laboratory for inventory and randomized using SAS version 9.4 (SAS, Cary, NC) in which samples were assigned to analytical batches and plate positions using simple randomization to minimize systematic bias. Urine was analyzed using a Biocrates AbsoluteIDQ® p180 kit (Biocrates, Life Sciences AG, Innsbruck, Austria) following the manufacturer’s instructions [16]. In brief, internal standards, calibration standards (quantitation), quality control samples (interplate correlation and normalization), or 10 µL of experimental urine samples were placed at the center of each filter in the upper plate of a 96-well kit and dried with a nitrogen evaporator. Next, a 5% phenylisothiocyanate solution was added for derivatization of amino acids and biogenic amines. Following incubation, the filters were dried again with nitrogen. The metabolites were then extracted using a 5 mM ammonium acetate solution in methanol and transferred to a lower 96-deep well plate via centrifugation. Biocrates AbsoluteIDQ® p180 plates were analyzed using a AB Sciex API 4000 Triple Quad LC/MS (AB Sciex, Framingham, MA) equipped with an Agilent 1200 HPLC (Agilent, Santa Clara CA, USA). Amino acids and biogenic amines were determined in LC-MS mode. Quantification was carried out using internal standards and a calibration curve (Cal 1 to Cal 7). Data was managed on MetIDQ software (Biocrates, Life Sciences AG, Innsbruck, Austria), and all urinary metabolite data was normalized using creatinine [19].
Data analysis
Data preprocessing was completed using MetIDQ Carbon software for peak extraction, automatic checking of peak alignment, normalization, and background deduction of mass spectrometry raw data. This was a longitudinal study with repeated measurements per mouse. To establish CR responders and nonresponders, mice were rank-ordered based on their percent weight change in response to calorie restriction. Mice in the most extreme quartile were designated as CR nonresponders (quartile 1), and mice in the highest quartile were designated as CR responders (quartile 4) (both combined and separate by sex). These naming designators are relative only and not intended to reflect absolute changes (or a lack or presence thereof). Orthogonal partial least squares discriminant analysis (OPLS-DA), Two-Way ANOVA with interaction terms, receiver operating characteristic (ROC) curves, and pathway analyses were performed using MetaboAnalyst [20]. Preprocessed data was uploaded to the MetaboAnalyst web server followed by a data integrity check with missing value imputations. All data was subsequently normalized using median values of each metabolite followed by log transformation and auto scaled. Metabolites deemed important to the differentiation of study groups met the following thresholds determined using a fold-change analysis and t-test: fold change > 1.5 or < 0.66, a raw p < 0.05, and false discovery rate (FDR) = 20%. Metabolites corresponding to Variable Importance in Projection (VIP) values >1.50 (from the first principal component of the OPLS-DA analyses) were also deemed important to the differentiation of groups. Two-way ANOVA with interaction terms was performed to evaluate the effects of sex, responder status, and their interaction on individual metabolites (S1 Table). P-values from the ANOVA were subsequently corrected for multiple testing using the FDR method to control for type I error. Multivariate ROC curves were generated using OPLS-DA algorithm and predicted class probabilities were determined using 100 cross-validations. Model performance was evaluated using R²Y and Q² metrics. R²Y represents the proportion of variance in the response variable explained by the model, indicating the goodness-of-fit. Q² was calculated via cross-validation and reflects the model’s predictive ability on unseen data. The area under the curve (AUC) was calculated to assess model discrimination. To guard against overfitting, model complexity was optimized to minimize validation errors, and permutation testing (n = 100 permutations) was performed to confirm that the observed AUC was not due to chance. Results are reported as mean AUC values with confidence intervals based on cross-validation replicates. All metabolite concentrations were subject to pathway analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database in MetaboAnalyst. To identify pathways of interest, each pathway had to meet the following criteria: raw p-value <0.05 and an impact score of >0.3 [21].
Results
Body mass and composition of DO mice following high-fat diet and calorie restriction
Among 150 male and 150 female DO mice fed a high-fat diet for 12 weeks to induce obesity, then a 30% CR diet regimen for 8 weeks to induce weight loss (12; Fig 1), significant heterogeneity in body mass and composition occurred in response to each diet for all mice, with similar variability by sex (Table 1). For all mice after the high-fat diet treatment, body weights ranged from 19.8 g to 70.7 g (median: 44.9 g in males, 35.9 g in females), percent fat mass from 11.2% to 55.0% (median: 34.8% in males, 39.0% in females), and percent lean mass from 40.8% to 88.1% (median: 60.1% in males, 56.8% in females). For all mice after CR treatment, body weight ranged from 15.8 g to 60.8 g (median: 35.4 g for males; 26.4 g for females), percent fat mass from 0.1% to 47.1% (median: 23.8% for males, 22.4% for females), and percent lean mass from 47.6% to 94.0% (median: 71.8% for males, 73.0% for females).
Designation of CR responders and nonresponders by percent changes in body weight
We rank-ordered the mice (by both combined sex and separate sex) according to their CR-mediated percent weight change and then designated the mice in the extreme quartiles as CR nonresponders (quartile 1, n = 67 mice with the least weight loss, including 32 males and 35 females) and CR responders (quartile 4, n = 67 mice with the greatest weight loss, including 33 males and 34 females) (Table 2). Overall, the median percent change in body weight for CR responders was −38.6% (males: −38.0%; females −39.7%), compared to −8.7% for CR nonresponders (males: −7.4%, females: −12.1%).
Urinary metabolite signatures are associated with CR-mediated weight loss in obese mice
To identify urinary metabolites that distinguish between CR responders and nonresponders, we performed targeted semiquantitative metabolomics and subsequent downstream data analyses on urine samples collected in obese DO mice immediately prior to CR intervention. First, we conducted a supervised OPLS-DA of urinary metabolites (Fig 2A–C). Class separation, albeit often weak, was detected between CR responders and nonresponders in all mice (Q2 = 0.34, R2Y=0.48), males (Q2 = 0.07, R2Y=0.44), and females (Q2 = 0.43, R2Y=0.65).
Scores plots of (A) all CR responders (blue circles) and nonresponders (red circles), (B) male CR responders and CR nonresponders, and (C) female CR responders and CR nonresponders.
Next, using the OPLS-DA statistics, we identified urinary metabolites that had a VIP value >1.50. We also determined metabolites (comparing CR responders versus CR nonresponders) with a fold change >1.5 or <0.66 and with p < 0.05. The following metabolites met these criteria: hydroxyproline and glutamic acid, in both sexes; hydroxyproline, glutamic acid, and putrescine in males; and hydroxyproline, glutamic acid, dopamine, histamine, lysine, and spermine in females (Table 3). The metabolites were consistently elevated in CR responders versus nonresponders, with the exceptions of putrescine, histamine, and spermine, which were suppressed. In multivariate ROC analyses, each of these 3 metabolic signatures, when applied to their associated sex-combined and female group, was at least moderately good (based on area under the curve, AUC, > 0.70) at associating urinary metabolites with CR responsiveness (Fig 3A–C). Specifically, AUCs (95% CI) were 0.76 (0.69–0.86) in all mice combined, 0.71 (0.52–0.86) in males, and 0.85 (0.69–0.97) in females.
Receiver operating characteristic (ROC) curves of (A) all CR responders and nonresponders (AUC: 0.76, confidence interval (CI): 0.68-0.85), (B) male CR responders and CR nonresponders (AUC: 0.71, CI: 0.51-0.85), and (C) female CR responders and CR nonresponders (AUC: 0.84, CI: 0.69-0.96).
Pathway topology analysis of urinary metabolite infers differences in metabolic state between CR responders and nonresponders
We used pathway topology analysis of urinary metabolites to interrogate differences in the metabolic state of CR responders and nonresponders (Fig 4A–C). Top pathways with p < 0.05 and pathway score > 0.3 were deemed to have high impact, and these included arginine and proline metabolism; histidine metabolism; glycine, serine, and threonine metabolism; and alanine, aspartate, and glutamate biosynthesis in all mice, males and females (Table 4). Other high-impact pathways identified were tyrosine metabolism, as well as phenylalanine, tyrosine, and tryptophan biosynthesis, in all mice and female mice; and taurine and hypotaurine metabolism in females.
Bubble chart of KEGG pathway analysis for (A) all, (B) male, and (C) female CR responders vs CR nonresponders. Top pathways with p < 0.05 and pathway score > 0.3 are labeled accordingly. Pathway impact values are on the x-axis while -log(p) values are on the y-axis.
Discussion
Using targeted semiquantitative metabolomics and downstream data analyses of biobanked urine samples from obese DO mice (150 males, 150 females) collected immediately before CR intervention [14], we identified urinary metabolic signatures that distinguished CR-mediated weight loss responders from nonresponders (multivariate ROC curves, AUC > 0.70). The metabolic signatures included hydroxyproline and glutamic acid for all the mice; hydroxyproline, glutamic acid, and putrescine for the males; and hydroxyproline, glutamic acid, spermine, dopamine, histamine, and lysine for the females. Further, we discerned high-impact metabolic pathways, including arginine and proline metabolism and alanine, aspartate, and glutamate biosynthesis, that distinguished CR responders from nonresponders and could indicate metabolic reprogramming to enhance insulin sensitivity and energy metabolism. Hence, these preclinical data i) demonstrate that metabolites in the noninvasive biofluid urine can forecast CR-mediated weight loss, and ii) complement and extend the previously reported CR weight-loss predictive ability of metabolites in systemic circulation [14].
Of the urinary metabolites assessed, only hydroxyproline and glutamic acid distinguished CR responders from CR nonresponders in each combined- and separate-sex group. Each of these metabolites was also identified as being part of the arginine and proline metabolic pathway, as determined by KEGG pathway analysis. Arginine and proline metabolism primarily takes place in the intestinal-renal axis [22]. Independent of protein translation, arginine is an important intermediary metabolite in the urea cycle and polyamine biosynthesis, while proline is involved in collagen biosynthesis [23]. Glutamic acid concentrations impact the metabolism of arginine since glutamic acid produces α-ketoglutarate, which can be converted into aspartate and, subsequently, arginine. Putrescine (differentiating the male groups) and spermine (differentiating the female groups) are also involved in arginine and proline metabolism, likely due to arginine’s role in polyamine biosynthesis. Additionally, three distinct metabolic pathways—histidine metabolism; alanine, aspartate, and glutamate biosynthesis; and glycine, serine, and threonine metabolism—differentiated CR responders from CR nonresponders in all mice combined. Several urinary metabolites linked to CR response are part of these pathways. For example, histamine and glutamic acid are part of the histidine metabolism pathway, while lysine plays a role in glycine, serine, and threonine metabolism.
The literature supports both glutamic acid and hydroxyproline as indicators of metabolic distress [22–28]. Urinary glutamic acid levels are considered a reliable proxy for circulating levels [24]. Moreover, increased circulating glutamic acid in human studies directly correlate with obesity and indicate possible metabolic acidosis [25]. Elevated glutamic acid may also suggest reduced liver functionality in CR responders. As metabolic dysfunction-associated steatohepatitis progresses, there is a shift in glutamine extraction and catabolism to glutamic acid from the liver to the kidneys to compensate for the metabolic acidosis and dysregulated liver metabolism [26,27]. Hydroxyproline is one of the most prevalent amino acids in collagen [28]. In a healthy state, hydroxyproline is a substrate for extracellular matrix production, which helps to preserve the integrity and functionality of hepatocytes [28]. However, hepatic stellate cells activate upon chronic hepatocyte damage, leading to their increased proliferation, excessive production of extracellular matrix [28,29] and initiation of liver fibrosis [28]. In humans and rodents, circulating and urinary hydroxyproline levels are correlated with liver disease, specifically, the presence and severity of fibrosis [28], and may be a result of degradation of newly synthesized collagen, particularly in the beginning stages of fibrosis [30].
Spermine and putrescine are polyamines that play important roles in cellular metabolism and physiology [31]. High polyamine intake is linked to lower all-cause mortality in a prospective community-based cohort study [32], and elevated polyamine levels are linked to enhanced energy metabolism and increased autophagy [31]. Additionally, polyamines function as free radical scavengers to reduce the harmful effects of reactive oxygen species [33]. However, elevated excretion of polyamines has been shown to be associated with chronic disease and inflammation. Moreover, enhanced catabolism of cellular polyamines can also increase cellular oxidative stress [34]. In our study, spermine and putrescine, respectively, were reduced in female and male CR responders, compared to CR nonresponders, respectively. Putrescine can be converted into spermine as well as back-converted into putrescine through spermidine synthase. Spermidine synthase is regulated, in part, by androgens [35] and could possibly explain the sexual dimorphism identified in male and female CR responders.
Dopamine is a neurotransmitter involved in the regulation of energy balance via food intake [36]. In rodents, the blocking of dopamine receptors results in mitigating the reward stimulus of food, which suggests that altered dopaminergic signaling contributes to the feeding patterns in individuals with obesity [37]. In humans, women with obesity shown pictures of high-calorie foods had significantly greater activation in several regions of the brain that relate to reward processing including the amygdala and hippocampus, among others [38]. Dopamine crosses the blood brain barrier through monoamine transporters and is excreted in the urine [39]. We observed that urinary dopamine concentrations were significantly higher in female CR responders compared to female CR nonresponders, potentially related to how dopamine impacts eating behavior and reward responses. Moreover, dopamine was the top metabolite in our VIP analysis (Table 3). When food intake is restricted, CR responders may be unable to sustain dopamine-driven reward signaling, possibly altering hypothalamic circuits regulating satiety and energy expenditure. This is supported by evidence in humans, where higher urinary dopamine concentrations are associated with increased energy intake [40]. However, in our preclinical study, urinary dopamine concentrations only distinguished female (and not male) CR responders from CR nonresponders, possibly due to estrogen regulation of dopamine release [41].
Histamine is a biogenic amine that regulates inflammation and is produced by a variety of immune cells, such as mast cells, as well as the glomeruli of the kidneys [42,43]. Urinary and plasma histamine levels are commonly increased in response to high levels of cytokines and chemokines as often occurs in allergic reactions [42,44]. High histamine levels in the urine, as observed in the CR nonresponders relative to CR responders may also indicate the development of chronic kidney disease, especially with the infiltration of mast cells into the kidneys being correlated with the development of nephropathy [42]. However, the separation of CR responders and CR nonresponders by histamine levels was only identified in female mice and not male mice, possibly due in part to differential effects of testosterone versus estrogen on renal fibrosis [45].
Lysine is an essential amino acid obtained through metabolism of dietary proteins. The literature is mixed on lysine having a protective effect on kidney function in models of obesity and kidney disease or as a marker of obesity and diabetes. We identified that lysine, specifically as part of lysine degradation, was elevated in female CR responders compared to CR nonresponders. The lysine degradation pathways include the saccharopine pathway, predominately active in the kidney, and the pipecolate pathway, predominately active in the brain [46]. In the short- to mid-term, elevated lysine is associated with protective effects in kidney disease in rodent models, yet the mechanisms underpinning these effects are unknown [46]. Moreover, a lysine metabolite, α-aminoadipic acid, was found to be associated with diabetes risk and markers of obesity in humans [47].
Study limitations include a lack of 1) quantitation of physical activity, which is likely to be heterogeneous in these mice and may have impacted the amount of weight lost during CR; 2) data on estrous cycle in females, which may have influenced sex-steroid and other hormonal levels that can influence appetite and weight loss response. 3) Our multivariate ROC analyses achieved AUCs > 0.7 but with wide confidence intervals. The model for male mice exhibited a low Q² and moderate R²Y values, consistent with overfitting. Thus, while the models showed promising discriminatory potential, the wide confidence intervals and overfitting in our male model indicate that these findings could be imprecise and requires further validation with a larger sample size. Moreover, we did not track water intake or determine glomerular filtration rate, which would have identified differences in kidney function that may have impacted urinary concentrations of certain urine metabolites. Future studies that account for these possible covariates and include similar analyses using urine samples from weight loss studies in humans are needed to extend these initial findings and ensure a robust metabolic signature predicting responsiveness to CR-mediated weight loss.
Conclusions
Leveraging urine as a noninvasive biospecimen and using Diversity Outbred mice as a model of the phenotypic heterogeneity in human populations, we identified a urinary metabolite signature associated with favorable CR-induced weight loss in obese mice. This metabolite signature and associated metabolic pathways, including arginine and proline metabolism, and alanine, aspartate, and glutamate biosynthesis, indicated CR responders, relative to nonresponders, were predisposed to exhibit greater weight loss, increased insulin sensitivity, and enhanced metabolic reprogramming in response to CR. Future work should focus on validating and extending the metabolic predictors of weight loss reported herein as well as integrating them with other predictors of weight loss response, such as serum leptin levels [14], to further personalize weight loss regimens and increase their success.
Supporting information
S1 Table. Sex X responder interaction.
Two-Way ANOVA followed by FDR correction using MetaboAnalyst was performed to determine sex-specific urinary metabolites.
https://doi.org/10.1371/journal.pone.0329422.s001
(DOCX)
Acknowledgments
BioRender was used to create Fig 1. (Created in BioRender. Paules, E. (2025) https://BioRender.com/d33s798).
References
- 1. San-Cristobal R, Navas-Carretero S, Martínez-González MÁ, Ordovas JM, Martínez JA. Contribution of macronutrients to obesity: implications for precision nutrition. Nat Rev Endocrinol. 2020;16(6):305–20. pmid:32235875
- 2. Westerterp KR, Speakman JR. Physical activity energy expenditure has not declined since the 1980s and matches energy expenditures of wild mammals. Int J Obes (Lond). 2008;32(8):1256–63. pmid:18504442
- 3. San-Cristobal R, Navas-Carretero S, Celis-Morales C, Brennan L, Walsh M, Lovegrove JA, et al. Analysis of dietary pattern impact on weight status for personalised nutrition through on-line advice: The Food4Me Spanish Cohort. Nutrients. 2015;7(11):9523–37. pmid:26593942
- 4. Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of obesity among adults and youth: United States, 2015-2016. NCHS Data Brief. 2017(288):1–8.
- 5. Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of obesity and severe obesity among adults: United States, 2017-2018. NCHS Data Brief. 2020(360):1–8.
- 6. Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K. Body fatness and cancer--viewpoint of the IARC Working Group. N Engl J Med. 2016;375(8):794–8.
- 7. Drew JE. Challenges of the heterogeneous nutrition response: interpreting the group mean. Proc Nutr Soc. 2020;79(2):174–83. pmid:31239000
- 8. Gordon-Larsen P, French JE, Moustaid-Moussa N, Voruganti VS, Mayer-Davis EJ, Bizon CA, et al. Synergizing mouse and human studies to understand the heterogeneity of obesity. Adv Nutr. 2021;12(5):2023–34. pmid:33885739
- 9. Ryan K, Dockray S, Linehan C. A systematic review of tailored eHealth interventions for weight loss. Digit Health. 2019;5:2055207619826685. pmid:30783535
- 10. Vo DK, Trinh KTL. Emerging biomarkers in metabolomics: advancements in precision health and disease diagnosis. Int J Mol Sci. 2024;25(23).
- 11. Greenway FL. Physiological adaptations to weight loss and factors favouring weight regain. Int J Obes (Lond). 2015;39(8):1188–96. pmid:25896063
- 12. Zhan S, Zhou X, Fu J. Noninvasive urinary biomarkers for obesity-related metabolic diseases: diagnostic applications and future directions. Biomolecules. 2025;15(5):633. pmid:40427524
- 13. Hursting SD, Lavigne JA, Berrigan D, Perkins SN, Barrett JC. Calorie restriction, aging, and cancer prevention: mechanisms of action and applicability to humans. Annu Rev Med. 2003;54:131–52. pmid:12525670
- 14. Paules EM, VerHague M, Lulla AA, Meyer KA, Coleman MF, Albright J. Sex-specific systemic metabolic predictors of resistance to calorie restriction-induced weight loss in obese diversity outbred mice. Am J Physiol-Regulatory Integr Compar Physiol.
- 15. Churchill GA, Gatti DM, Munger SC, Svenson KL. The Diversity Outbred mouse population. Mamm Genome. 2012;23(9–10):713–8. pmid:22892839
- 16. Trouwborst I, Goossens GH, Astrup A, Saris WHM, Blaak EE. Sexual dimorphism in body weight loss, improvements in cardiometabolic risk factors and maintenance of beneficial effects 6 months after a low-calorie diet: results from the randomized controlled DiOGenes trial. Nutrients. 2021;13(5).
- 17. Good DM, Thongboonkerd V, Novak J, Bascands J-L, Schanstra JP, Coon JJ, et al. Body fluid proteomics for biomarker discovery: lessons from the past hold the key to success in the future. J Proteome Res. 2007;6(12):4549–55. pmid:17970587
- 18. Adachi J, Kumar C, Zhang Y, Olsen JV, Mann M. The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins. Genome Biol. 2006;7(9):R80. pmid:16948836
- 19. Waikar SS, Sabbisetti VS, Bonventre JV. Normalization of urinary biomarkers to creatinine during changes in glomerular filtration rate. Kidney Int. 2010;78(5):486–94. pmid:20555318
- 20. Xia J, Psychogios N, Young N, Wishart DS. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 2009;37(Web Server issue):W652–60. pmid:19429898
- 21. Zhao T, Liang Y, Zhen X, Wang H, Song L, Xing D, et al. Analysis of serum exosome metabolites identifies potential biomarkers for human hepatocellular carcinoma. Metabolites. 2024;14(8):462. pmid:39195558
- 22. Wu G, Bazer FW, Davis TA, Kim SW, Li P, Marc Rhoads J, et al. Arginine metabolism and nutrition in growth, health and disease. Amino Acids. 2009;37(1):153–68. pmid:19030957
- 23. Boyle J. Lehninger principles of biochemistry (4th ed.): Nelson, D., and Cox, M. Biochem Molecular Bio Educ. 2005;33(1):74–5.
- 24. Gamarra Y, Santiago FC, Molina-López J, Castaño J, Herrera-Quintana L, Domínguez Á, et al. Pyroglutamic acidosis by glutathione regeneration blockage in critical patients with septic shock. Crit Care. 2019;23(1):162. pmid:31064391
- 25. Payab M, Tayanloo-Beik A, Falahzadeh K, Mousavi M, Salehi S, Djalalinia S, et al. Metabolomics prospect of obesity and metabolic syndrome; a systematic review. J Diabetes Metab Disord. 2021;21(1):889–917. pmid:35673462
- 26. Taylor L, Curthoys NP. Glutamine metabolism: role in acid-base balance*. Biochem Mol Biol Educ. 2004;32(5):291–304. pmid:21706743
- 27. Simon J, Nuñez-García M, Fernández-Tussy P, Barbier-Torres L, Fernández-Ramos D, Gómez-Santos B, et al. Targeting hepatic glutaminase 1 ameliorates non-alcoholic steatohepatitis by restoring very-low-density lipoprotein triglyceride assembly. Cell Metab. 2020;31(3):605–622.e10. pmid:32084378
- 28.
Gabr SA, Alghadir AH, Sherif YE, Ghfar AA. Hydroxyproline as a biomarker in liver disease. In: Patel VB, Preedy VR, editors. Biomarkers in liver disease. Dordrecht: Springer Netherlands; 2017. pp. 471–91.
- 29. Kershenobich Stalnikowitz D, Weissbrod AB. Liver fibrosis and inflammation. A review. Ann Hepatol. 2003;2(4):159–63. pmid:15115954
- 30. Murawaki Y, Yamada S, Koda M, Hirayama C. Collagenase and collagenolytic cathepsin in normal and fibrotic rat liver. J Biochem. 1990;108(2):241–4. pmid:2172225
- 31. Pegg AE. The function of spermine. IUBMB Life. 2014;66(1):8–18. pmid:24395705
- 32. Kiechl S, Pechlaner R, Willeit P, Notdurfter M, Paulweber B, Willeit K, et al. Higher spermidine intake is linked to lower mortality: a prospective population-based study. Am J Clin Nutr. 2018;108(2):371–80. pmid:29955838
- 33. Ha HC, Sirisoma NS, Kuppusamy P, Zweier JL, Woster PM, Casero RA Jr. The natural polyamine spermine functions directly as a free radical scavenger. Proc Natl Acad Sci U S A. 1998;95(19):11140–5. pmid:9736703
- 34. Park MH, Igarashi K. Polyamines and their metabolites as diagnostic markers of human diseases. Biomol Ther (Seoul). 2013;21(1):1–9. pmid:24009852
- 35. Cyriac J, Haleem R, Cai X, Wang Z. Androgen regulation of spermidine synthase expression in the rat prostate. Prostate. 2002;50(4):252–61. pmid:11870804
- 36. Schwartz MW, Woods SC, Porte D Jr, Seeley RJ, Baskin DG. Central nervous system control of food intake. Nature. 2000;404(6778):661–71. pmid:10766253
- 37. Koob GF. Drugs of abuse: anatomy, pharmacology and function of reward pathways. Trends Pharmacol Sci. 1992;13(5):177–84. pmid:1604710
- 38. Stoeckel LE, Weller RE, Cook EW 3rd, Twieg DB, Knowlton RC, Cox JE. Widespread reward-system activation in obese women in response to pictures of high-calorie foods. Neuroimage. 2008;41(2):636–47. pmid:18413289
- 39. Marc DT, Ailts JW, Campeau DCA, Bull MJ, Olson KL. Neurotransmitters excreted in the urine as biomarkers of nervous system activity: validity and clinical applicability. Neurosci Biobehav Rev. 2011;35(3):635–44. pmid:20696183
- 40. Basolo A, Ando T, Hollstein T, Votruba SB, Krakoff J, Piaggi P. Higher urinary dopamine concentration is associated with greater Ad libitum energy intake in humans. Obesity (Silver Spring). 2020;28(5):953–61. pmid:32289874
- 41. Zachry JE, Nolan SO, Brady LJ, Kelly SJ, Siciliano CA, Calipari ES. Sex differences in dopamine release regulation in the striatum. Neuropsychopharmacology. 2021;46(3):491–9. pmid:33318634
- 42. Sudarikova AV, Fomin MV, Yankelevich IA, Ilatovskaya DV. The implications of histamine metabolism and signaling in renal function. Physiol Rep. 2021;9(8):e14845. pmid:33932106
- 43. Sedor JR, Abboud HE. Actions and metabolism of histamine in glomeruli and tubules of the human kidney. Kidney Int. 1984;26(2):144–52. pmid:6094908
- 44. Raithel M, Hagel A, Albrecht H, Zopf Y, Naegel A, Baenkler H-W, et al. Excretion of urinary histamine and N-tele methylhistamine in patients with gastrointestinal food allergy compared to non-allergic controls during an unrestricted diet and a hypoallergenic diet. BMC Gastroenterol. 2015;15:41. pmid:25888445
- 45. Mauvais-Jarvis F, Bairey Merz N, Barnes PJ, Brinton RD, Carrero J-J, DeMeo DL, et al. Sex and gender: modifiers of health, disease, and medicine. Lancet. 2020;396(10250):565–82. pmid:32828189
- 46. Tan Y, Chrysopoulou M, Rinschen MM. Integrative physiology of lysine metabolites. Physiol Genomics. 2023;55(12):579–86. pmid:37781739
- 47. Wang TJ, Ngo D, Psychogios N, Dejam A, Larson MG, Vasan RS, et al. 2-Aminoadipic acid is a biomarker for diabetes risk. J Clin Invest. 2013;123(10):4309–17. pmid:24091325