High-Resolution Plasma Metabolomics and Thiamine Status in Critically Ill Adult Patients

Background and Aim: Thiamine (Vitamin B1) is an essential micronutrient and a co-factor for metabolic functions related to energy metabolism. We determined the association between whole blood thiamine pyrophosphate (TPP) concentrations and plasma metabolites using high resolution metabolomics in critically ill patients. Methods Cross-sectional study performed in Erciyes University Hospital, Kayseri, Turkey and Emory University, Atlanta, GA, USA. Participants were ≥ 18 years of age, with an expected length of ICU stay longer than 48 hours, receiving furosemide therapy for at least 6 months before ICU admission. Results Blood for TPP and metabolomics was obtained on the day of ICU admission. Whole blood TPP concentrations were measured using high-performance liquid chromatography (HPLC). Liquid chromatography/mass spectrometry was used for plasma high-resolution metabolomics. Data was analyzed using regression analysis of TPP levels against all plasma metabolomic features in metabolome-wide association studies. We also compared metabolomic features from patients in the highest TPP concentration tertile to patients in the lowest TPP tertile as a secondary analysis. We enrolled 76 participants with a median age of 69 (range, 62.5–79.5) years. Specific metabolic pathways associated with whole blood TPP levels, using both regression and tertile analysis, included pentose phosphate, fructose and mannose, branched chain amino acid, arginine and proline, linoleate, and butanoate pathways. Conclusions Plasma high-resolution metabolomics analysis showed that whole blood TPP concentrations are significantly associated with metabolites and metabolic pathways linked to the metabolism of energy, amino acids, lipids, and the gut microbiome in adult critically ill patients.


Introduction
Thiamine, an essential water-soluble vitamin, was the rst B vitamin to be discovered and thus was named vitamin B 1 (Whit eld, Bourassa et al. 2018).Thiamine pyrophosphate (TPP) is the active form of thiamine in the human body (Frank 2015).TPP is involved in a wide variety of biochemical pathways required to maintain normal tissue and organ function, including as a co-factor in glycolysis, TCA cycle and pentose phosphate pathway metabolism.For example, the pentose phosphate pathway has a fundamental role in glucose metabolism and is also a major route for the synthesis of many neurotransmitters, nucleic acids, lipids, amino acids, steroids, and glutathione (Polegato, Pereira et al. 2019).
Critical illnesses are severe, life-threatening disease states with heterogeneous clinical presentations (Ferrario, Cambiaghi et al. 2016).Critically ill individuals exhibit variable levels of in ammation due to infection, organ dysfunction, and other factors that increase counter-regulatory hormones and cytokines which contribute to a hypercatabolic state associated with insulin resistance and muscle wasting (Cyr, Zhong et  ).Several reports, including our previous study, have shown that thiamine depletion may be common in intensive care unit (ICU) patients, especially those receiving chronic diuretic therapy prior to ICU admission (Attaluri, Castillo et al. 2018, Gundogan, Akbudak et al. 2019).Since thiamine is involved in energy metabolism and the metabolic processing of other essential nutrients, low thiamine status may be associated with signi cant alterations in key metabolic pathways in ICU patients.
High-resolution metabolomics (HRM) analysis is a rapidly developing science that utilizes liquid chromatography and ultra-high-resolution mass spectrometry (LC-MS) to detect small molecules, including nutrient-related metabolites, in plasma, urine, tissue, and other biosamples ( Nutritional metabolomics using HRM thus represents a novel tool to explore nutrition-related physiology and pathophysiology in catabolic states such as critical illness. The aim of this pilot study was to determine metabolites and metabolic pathways linked to whole blood TPP concentrations in adult ICU patients on chronic diuretic therapy, who may be at particular risk for thiamine depletion.We compared patients with lower versus higher TPP concentrations using both targeted plasma HRM (pathways known to be dependent upon thiamine as a co-factor, such as the pentose phosphate pathway and the TCA cycle) and untargeted plasma HRM analytical approaches.

Materials and Methods
The current study was conducted in the Erciyes University Medical ICU center in Kayseri, Turkey and in the Clinical Biomarkers Laboratory at Emory University, Atlanta, GA, USA.
The present clinical research was performed in accordance with the ethical standards of the responsible committees on human experimentation and with the Helsinki Declaration of 1975 and approved by the Erciyes University Ethics Committee (Date:15.01.2020,Number: 2020/35).Written informed consent was obtained from all patients or their legal representatives.

Study Participants
Participants were included if they were at least 18 years of age, were deemed to require ≥ 48 hours of ICU treatment and received furosemide therapy for 6 months or longer before ICU admission.Patients receiving high-dose oral thiamine (≥ 50 mg/day within 14 days prior to ICU admission) were excluded.

Demographic and Clinical Data
At ICU admission Demographic characteristics (age, gender, body mass index (BMI)), reason for ICU admission, presence of comorbidity, and Acute Physiology and Chronic Health Evaluation II (APACHE II) score of the 76 study participants were recorded.The type, dose, and duration of diuretic use of all participants prior to admission to the Medical ICU were noted.The type of nutrition support, and the intake of carbohydrate, energy and insulin administration were documented on the ICU admission day.

Sample Preparation and Analysis
Whole blood thiamine concentrations Non-fasted whole blood samples were obtained on the day of ICU admission.TPP concentrations were measured using high-performance liquid chromatography (HPLC; Shimadzu, 8040, Immuchrom, Japan) at Erciyes University.The reference range for normal TPP values was 28-78 ng/Ml (Evliyaoglu, van Helden et al. 2019).

Plasma for metabolomics analysis
Blood samples obtained on the day of ICU admission were centrifuged at 1500 g with a cooled centrifuge for 10 minutes.EDTA plasma samples were separated and stored at − 80ºC until all participant samples were collected.Samples were shipped on dry ice to Atlanta, GA for metabolomics batch analysis in the Clinical Biomarkers Laboratory at Emory University, Atlanta.

High-resolution metabolomics
Plasma was analyzed by high-resolution mass spectrometry as previously described (Soltow, Strobel et al. 2013, Liu, Walker et al. 2016).Plasma samples (50 l) were mixed with 100 µl acetonitrile containing 1.25µl internal standard solution with eight stable isotopic chemicals representing multiple smallmolecule classes (Soltow, Strobel et al. 2013).Samples were then equilibrated on ice for 30 minutes, and centrifuged at 16,000 x g for 10 minutes to remove precipitated proteins.The supernatant was added to an autosampler vial and maintained at 4 • C until analysis.Liquid chromatography was performed on triplicates of each sample using both C18 (Higgins C18 stainless steel column, 2.1 × _50 mm) and hydrophilic interaction liquid chromatography (HILIC, Waters XBridge BEH Amide XP HILIC column, 2.1 × 50 mm) liquid chromatography columns followed by negative (C18) or positive (HILIC) electrospray ionization (ESI) and high-resolution mass spectrometry (Thermo Orbitrap Fusion Tribrid).
Mass spectral data were collected over a 5-minute run period at a resolution of 120,000 and mass-tocharge (m/z) scan range of 85 -1275.Each batch of 40 samples also included six triplicates of a pooled normal human plasma reference standard (Qstd3) (Go, Walker et al. 2015).The raw data les were extracted and aligned using apLCMS (Yu, Park et al. 2009) and xMSanalyzer (Uppal, Soltow et al. 2013).The resulting feature table consisted of unique metabolic features (metabolites) de ned by accurate mass m/z, retention time, and ion intensity.Data was ltered to exclude m/z features having a median coe cient of variation within technical replicates ≥ 75%, and only samples with Pearson correlation within technical replicates ≥ 0.7 were used for downstream analysis (Go, Walker et al. 2015).Triplicates were median summarized with the condition that at least two replicates had non-missing values.
Metabolite annotation was performed using xMSannotator, an R program that uses a clustering algorithm to provided tentative annotations of features using publicly available databases, including the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Human Metabolome Database (HMDB) (Uppal, Walker et al. 2017).

Statistical Analysis
Prior to the analysis, the feature table was ltered to retain the features having non-zero values in at least 50% of the samples overall and in at least 80% of the samples in a group.After ltering, the feature intensities were log2 transformed and quantile normalized.Multiple linear regression analysis was used to conduct metabolome-wide association analysis (MWAS) to determine the associations of metabolic features (metabolites) with TPP concentrations, adjusting for age, sex, BMI, and Apache II score as a priori covariates.
μ Given the relatively low number of participants with TPP levels below the lower limit of normal, we also performed a separate secondary regression analysis to compare participants with TPP concentrations in the lowest versus the highest tertile of whole blood TPP concentrations.This tertile analysis was also adjusted for age, sex, BMI, and APACHE II score.We adjusted for APACHE II score in HRM analysis given that in ammation during critical illness may alter blood concentrations of several micronutrients, with redistribution from blood to tissues (Berger, Shenkin et al. 2022).
Pathway enrichment analysis was performed using mummichog (v2.0.6), which uses permutation analysis to map features based on both m/z and retention time to speci c pre-identi ed human metabolic pathways (Li, Park et al. 2013).For the MWAS, all features associated with thiamine with a raw p-value < 0.05 were included in the pathway analysis.For the tertile analysis, all features that discriminated between tertiles with a raw p-value < 0.05 were included.Using a raw p-value < 0.05 (compared to the much more stringent false discovery rate correction) protects against type II error and prevents information loss when performing pathway analysis.The mummichog algorithm maps the signi cant features to pathways and determines the p-values for these enriched pathways after adjusting for the null distribution of pathway p-values established using 500 permutations, which protects against type I error.Thus, running mummichog using metabolomic features with p < 0.05 effectively provides a compromise to handle both type I and type II error (Uppal, Walker et al. 2016).As additional protection against type I error, only the pathways enriched with at least four overlapping metabolites and one Level 1 con rmed metabolite (see below) were considered as signi cantly relevant to thiamine status.
Metabolites within speci c pathways were identi ed and con rmed by comparison to an in-house library of metabolites previously validated using ion-dissociation tandem mass spectrometry (MS/MS) and coelution with authentic standards (Liu, Nellis et al. 2020).Identi cation scores were assigned for metabolites based on an adaptation of the criteria proposed by Schymanski et al (Schymanski, Jeon et al. 2014): Level 1: con rmed by matching to an in-house library of MS/MS validated metabolites established using authentic standards Level 2: con rmed by matching to validated adducts from our inhouse library and matches with online databases; Level 3: matches online databases and correlated with metabolites in pathways via mummichog; Level 4: computationally assigned annotation using the biostatistical program xMSannotator (medium or high con dence); Level 5: accurate mass (m/z) match only.

Results
The median age of study patients was 69 (range, 62.5-79.5)years and 65% were female.The median APACHE II score on ICU admission was 14 (range, [11][12][13][14][15][16][17][18][19][20].The most common reasons for ICU admission were acute respiratory failure (62.3%) and metabolic disorders, including renal and hepatic failure, severe hyperglycemia, and hepatic encephalopathy (63.6%).Some participants had more than one of these conditions upon ICU admission.Chronic liver cirrhosis (53.2%) and congestive heart failure (50.6%) were the most frequent co-morbid diseases of the study participants and were the primary reason for chronic diuretic use prior to ICU admission.Their median duration of ICU stay was 5 days (range, 3-8) and ICU mortality rate was 23.4%.The median TPP concentration was 41.4 (range, 32.2-57.5)with a mean value of 48.5 ± 24.8 (SD) ng/mL.The whole blood TPP normal reference value: 28-78 ng/mL.The proportion of patients with below normal TPP levels was 14.3%.Additional demographic and clinical characteristics of the participants are listed in Table 1

HRM Analysis
A total of 18,189 plasma metabolomic features were identi ed from the C18/ESI negative column after the LC/MS run, with 16,598 features used in downstream analysis after pre-processing.There were 21,737 total plasma metabolomic features identi ed from the HILIC/ESI positive column after LC/MS, with 19,576 features used in downstream analysis after pre-processing.
Type 1 (m/z) and Type 2 (retention time) Manhattan plots were used to visualize the metabolomic features associated with TPP status for both MWAS and tertile analysis, respectively (Fig. 1, tertile analysis not shown).In the C18/ESI-, data, there were 672 features (368 negatively, and 304 metabolites positively) associated with TPP levels at raw P < 0.05 based on MWAS analysis, and in the HILIC/ESI + data, 1033 features were associated with TPP levels (505 negatively associated, and 528 positively associated) (Supplemental Tables 1 and 2, respectively).When comparing the highest TPP tertile to the lowest tertile, there were 718 features in the C18/ESI-data and 1063 features in the HILIC/ESI + data that varied between tertiles at raw P < 0.05 (Supplemental Tables 3 and 4, respectively).
Features associated with whole blood TPP concentrations were plotted using one-way Hierarchical Cluster Analysis (HCA).Features that were increased or decreased in relation to TPP levels reveal distinct clustering and regulation (Fig. 2A).Metabolomic feature clustering was more distinct in the tertile analysis shown in Fig. 2B.
Metabolites that differed between the lowest and highest TPP tertiles were enriched in 20 metabolic pathways (Fig. 3, Panel B).Most of the signi cant pathways were related to amino acid/nitrogen metabolism (e.g., aspartate and asparagine, BCAA degradation, arginine and proline, lysine, and others), glucose metabolism (pentose phosphate, hexose phosphorylation, fructose, and mannose), and vitamin/cofactor metabolism (biotin, lipoate and vitamin C).
The relationship between whole blood TPP concentrations and selected signi cant metabolites from enriched pathways found using MWAS analysis are shown in Table 2.There is a signi cant negative correlation between TPP concentrations and plasma gluconate, deoxyuridine and linoleate levels (p = 0.043, p = 0.005 and p = 0.034 respectively).We also focused on the TCA cycle-related metabolites pyruvate, citrate/isocitrate, succinate, α-ketoglutarate, and malate, which we have previously validated using our HRM work ow (Liu, Nellis et al. 2020).None of these TCA cycle metabolites in plasma were linked to TPP levels.Selected metabolites that differentiate patients in the lowest TPP tertile from those in the highest TPP tertile are listed in Supplemental Table 5.   4C and 4D (p = 0.02 and p = 0.017, respectively).Both metabolites were positively associated with TPP (increased in the highest TPP tertile).Metabolites that were correlated with each other across multiple pathways can be seen in the network/module plot in Supplemental Fig. 1.The metabolites that were signi linked as a function of whole blood TPP levels were largely carbohydrates, but the module also included ascorbic acid and glycosylated nucleosides (e.g., uridine, cytidine).

Discussion
Thiamine plays an essential role in the energy metabolism in the human body (Frank 2015 Thiamine de ciency has been previously found to disrupt energy metabolism in rats by affecting glucose transport and fatty acid β-oxidation in mitochondria (Gralak, Dębski et al. 2019).In addition, the peroxisomal α-oxidation of 3-methyl fatty acids has been shown to be dependent on TPP (Casteels, Foulon et al. 2003).However, to our knowledge, no data to date has associated thiamine status with fatty acid oxidation or lipid metabolism in humans.In the current study, pathway analysis and targeted assessment of speci c metabolic features (see Table 2 and Supplemental Table 5, Figs. 3 and 4) identi ed fatty acid β-oxidation, linoleate metabolism, and squalene and cholesterol biosynthesis as lipidrelated metabolic pathways linked to thiamine status.These unexpected hypothesis-generating data suggest the possibility that thiamine nutriture may broadly in uence lipid metabolism in critically ill adults and should be further explored in larger, prospective cohorts.
Thiamine also plays an important role in amino acid metabolism.TPP is a critical coenzyme for branched-chain α-ketoacid dehydrogenase (BCKDH), which is essential for the catabolism of branchedchain amino acids (BCAA) and subsequent utilization in the TCA cycle, among other functions (Duran and Wadman 1985, Depeint, Bruce et al. 2006).Our study revealed signi cant associations of TPP levels with numerous amino acid metabolic pathways, including BCAA metabolism, arginine and proline metabolism, aspartate and asparagine metabolism methionine and cysteine metabolism, and the urea cycle (Fig. 3).As shown in Supplemental Table 5, the TPP concentrations were negatively associated with asparagine, lysine and valine.
Our data expand the metabolomic results of several studies in critically ill patients with or without sepsis where a variety of alterations in amino acids and amino acid pathways have been identi ed, but in whom thiamine status was not determined (

2021
).Therefore, it is possible that these disruptions may contribute to circulating TPP concentrations.
Another study found that hypoxia in human colonic epithelial cells inhibited colonic uptake of gut microbiota generated TPP (Sabui, Ramamoorthy et al. 2022).We did not study the gut microbiome directly in our participants, but we observed that two major gut microbiome-derived metabolites, hippurate and aminobutyrate, were each decreased in patients in the lowest TPP tertile, and the butanoate (butyrate) metabolic pathway was signi cantly affected by TPP status (Figs. 3 and 4C-D).Thus, it is possible that thiamine status may be related to gut microbiome dysregulation by currently unknown mechanisms.Future studies should further explore the possible link between TPP status and gut microbiome in humans.
Limitations of this pilot study are the relatively small sample size and the cross-sectional study design, which precludes cause and effect relationships between thiamine status and the metabolic associations observed.Our study population was derived from a single Turkish medical center and did not include noncritically ill control participants or a control group of critically ill patients not receiving furosemide treatment.However, our pilot data will inform future prospective studies on the speci c impact of furosemide treatment in ICU patients.We believe that our data, as presented, are valid to de ne the relationship of thiamine status on the plasma metabolome in our cohort.Future larger studies would ideally obtain fasted blood samples, but this is di cult in many ICU patients who are receiving continuous nutrition support.
Another limitation is that the sample size did not allow comparison between different primary reasons for ICU admission or nutritional status of participants.However, to our knowledge, this is the rst study in human critical illness to link thiamine nutritional status with systemic metabolism using metabolomics analysis.Our rigorous plasma HRM methods are state-of-the art and all samples are analyzed in triplicate with internal standards and are well validated in the literature(Jones 2016).Data were also adjusted for illness severity (APACHE II score) on the day plasma for HRM was obtained.We found that whole blood TPP levels are linked to metabolic pathways and metabolites (e.g., lipid metabolism, gut microbiome) not generally considered in thiamine metabolism and therefore are hypothesis-generating for subsequent con rmatory and translational mechanistic studies.

Conclusions
Using a hybrid approach of targeted and untargeted metabolomics, we found changes in numerous metabolites and metabolic pathways associated with energy metabolism, amino acid metabolism, lipid metabolism and gut microbiome metabolism as a function of whole blood TPP concentrations in critically ill adults.Plasma HRM as utilized in this study provides new understanding of how the status of a single micronutrient, TPP, may impact whole body metabolism.These results will hopefully stimulate larger prospective studies to determine the impact of thiamine status and thiamine repletion on metabolic homeostasis in individuals with critical illness.

Declarations
Con ict of interest: None Manhattan plots of signi cant metabolites related to TPP levels in the 76 critically ill participants on admission to the ICU using a metabolome wide association study (MWAS) of metabolites from the C18/ESI negative column (Panels A and B).
Panel A: Type 1 Manhattan plot where each dot corresponds to one metabolic feature plotted according to mass-to-charge (m/z) ratio on the x-axis and -log10 P-value on the y-axis.Panel B: Type 2 Manhattan plot where each dot corresponds to one metabolic feature plotted according to chromatographic MS retention time in seconds.Blue dots represent negative associations with whole blood TPP concentrations and red dots indicate a positive association with whole blood TPP levels, adjusted for age, APACHE II score, sex, and BMI.Black dots (below the P <0.05 dotted line) represent metabolites not signi cantly related to TPP levels in the participants.Of the total of 672 signi cant metabolites, 368 were negatively associated and 304 were positively associated with whole blood TPP levels at raw P < 0.05.When comparing the highest TPP tertile to the lowest tertile (not shown), there were 718 features that varied between tertiles at raw P < 0.05.
al. 2021, Rousseau, Prescott et al. 2021, Wendt, Castro-Pearson et al. 2021).Disturbed metabolic pathways associated with such hypercatabolic conditions in critically ill patients may contribute to and/or result in mitochondrial/energetic dysregulation, cellular damage, and organ failure (Puthucheary, Rawal et al. 2013, Christopher 2018, McClave, Wischmeyer et al. 2019, Gourd and Nikitas 2020, Siddiqui, Pandey et al. 2020, Flower, Page et al. 2021 Jones, Park et al. 2012, Jones 2016).Current HRM methods use advanced data extraction and bioinformatics methods to detect tens of thousands of metabolic features in plasma derived from endogenous and exogenous sources, including dietary nutrients, intermediates of macro-and micronutrient metabolism, the gut microbiome, environmental exposures, commercial products, and drugs(Jones, Park et al. 2012, Uppal, Walker et al. 2016).As examples, our recent studies have utilized plasma HRM to determine metabolites and linked metabolic pathways (e.g., amino acid metabolism, TCA cycle intermediates) associated with lean mass in working adults and in individuals with active pulmonary tuberculosis infection, respectively (Bellissimo, Jones et al. 2021, Collins, Jones et al. 2021).These and other studies have focused on a hybrid approach using targeted and untargeted (discovery)-based HRM detection of plasma metabolites and speci c metabolic pathways enriched in signi cant metabolites to provide new insight into human metabolism and disease-associated pathophysiology(Alvarez, Chong et al. 2017).High-resolution metabolomics can be important for providing personalized and precision nutrition support for critical illness(Christopher 2018, Siddiqui, Pandey et al. 2020).Metabolomics data can be useful for assessing the risk related to nutritional de ciencies(Lasky-Su, Dahlin et al. 2017), clarifying the metabolic mechanism behind nutritional treatments (Amrein, Lasky-Su et al. 2021), comparing the use of enteral and parenteral nutrition (Gonzalez-Granda, Seethaler et al. 2021), and discovering new targets for nutritional interventions (Viana, Becce et al. 2021, Stolarski, Young et al. 2022).
interaction liquid chromatography ID = identi cation score MWAS = metabolome-wide association study Octenoyl-CoA: Octenoyl-Coenzyme A OxoODE: Oxooctadecadienoic acid Plots in Fig. 4A and 4B show the relationship between TPP and two metabolites involved in the pentose phosphate pathway, gluconate (p = 0.043) and xylose/ribose (p = 0.047), which are both negatively associated with TPP levels.The association between gut microbiome metabolites hippurate and aminobutyrate and TPP are shown in Fig.

Figure 2 Panel
Figure 2

Figure 3 Panel
Figure 3

Table 1 .
. Study Subject Demographic and Clinical Characteristics * Some participants had multiple comorbidities and primary reasons for ICU admission.Abbreviations: APACHE II, The Acute Physiology and Chronic Health Evaluation; COPD, chronic obstructive pulmonary disease, CRP, C-reactive protein, ICU, intensive care unit; SOFA, Sequential Organ Failure Assessment, TPP, Thiamine pyrophosphate Whole blood TPP normal reference value: 28-78 ng/mL IQR, Interquartile range (25 th quartile-75 th quartile).

Table 2
C18/ESI-and HILIC/ESI + MWAS analysis in association with TPP concentrations (Frank 2015, Whit eld, Bourassa et al. 2018 a co-factor for pyruvate dehydrogenase in glycolysis, alpha-ketoglutarate dehydrogenase in the TCA cycle and transketolase in the pentose phosphate pathway, among its more important functions(Frank 2015, Whit eld, Bourassa et al. 2018).We found that thiamine status, as assessed by whole blood TPP concentrations were signi cantly related to two key metabolites involved in function of the pentose phosphate pathway, namely, the hydrogen adducts (M-H) of gluconate and xylose/ribose, which were both increased in individuals with lower TPP concentrations.The pentose phosphate pathway is critical for a variety of key biochemical functions, including maintenance of carbon homoeostasis, generation of precursors for nucleotide and amino acid biosynthesis, and protection from oxidative stress via production of NADPH(Krüger, Grüning etal.2011, Nalos, Parnell et al. 2016, Sigurdsson, Kobayashi et al. 2022) from the reduced form of nicotinamide adenine dinucleotide phosphate (NADP+).Thus, our data suggests the possibility that low TPP concentrations may contribute to changes in transketolase activity within the pentose phosphate pathway that, in turn, may disrupt energy generation via the tightly linked pathways of glycolysis and the TCA cycle (Frank 2015, Whit eld, Bourassa et al. 2018).
(Sabui, Romero et al. 2021)14, Chen, Liang et al. 2022,Ohlstrom, Sul et al. 2022).Given the critical role of amino acid-derived metabolites in the TCA cycle (e.g., via αketoglutarate, oxaloacetate, succinate, etc.), it is possible that the broad impact of TPP status on amino acid metabolism we observed may impact energy generation indirectly via the TCA cycle.Further translational studies are needed to con rm such an effect, particularly since we did not observe any direct impact of TPP status on the TCA cycle metabolites pyruvate, citrate/isocitrate, succinate, α-ketoglutarate or malate.It is also possible, though speculative, that depletion of TPP has adverse effects on skeletal muscle or other tissues which utilize amino acids or are highly involved in amino acid metabolism, a hypothesis that requires further study.It is known that the gut microbiota can generate TPP in mice, although the nutritional signi cance of this is unclear(Sabui, Romero et al. 2021).Studies have shown that the gut microbiome composition and diversity is disrupted in human critical illness(Lamarche, Johnstone et al. 2018, Haak, Argelaguet et al.