Nicolas A Bonfils
email: nicolas.bonfils@aphp.fr

Henri-Jean Aubin

Amine Benyamina

Frédéric Limosin

Amandine Luquiens

Quality of life instruments used in problem gambling studies: A systematic review and a meta-analysis

Keywords: review, meta-analysis, gambling, quality of life, harm

The purpose of this systematic review was to identify the instruments used in original articles to measure quality of life (QOL) or health-related QOL (HRQOL) in gambling-disorder patients and to assess their suitability.

The systematic literature search to identify QOL/HRQOL instruments used among gamblingdisorder patients was performed in PubMed, Embase and PsycINFO databases up to November 2018. A meta-analysis was performed to study the effect size of the QOL/HRQOL instruments and gambling outcomes after an intervention.

Thirty-five studies were included. Seven types of instruments aiming at measuring QOL/HRQOL were identified. These instruments explored twenty-six domains. The instruments used were not properly validated in the studies. Most of the clinical trials reported a significant difference in QOL/HRQOL between pre-and post-intervention. These results were concordant with gambling outcomes but had a smaller effect size than gambling outcomes.

The currently used general instruments are efficient to measure a significant change after an intervention but might not evaluate specific areas of health related QOL impacted by gambling disorders

INTRODUCTION

Problem gambling is characterized by a persistent and recurrent problematic gambling behavior leading to clinically significant impairment or distress [START_REF] Browne | Gambling transitions among adult gamblers: A multi-state model using a Markovian approach applied to the JEU cohort[END_REF].

However treatment outcomes in this field remain poorly defined and are measured inconsistently across studies [START_REF] Pickering | Measuring treatment outcomes in gambling disorders: a systematic review[END_REF]. This is probably due to no consensual definition of recovery, as remission is only defined by no diagnostic operatory criterion during twelve months (American Psychiatric Association, 2013). Moreover, the course of the disease can vary deeply from one subject to another, as attests DSM-5 specification of the diagnosis "episodic" or "persistent". Gambling disorder does not follow a linear course over time [START_REF] Browne | Gambling transitions among adult gamblers: A multi-state model using a Markovian approach applied to the JEU cohort[END_REF][START_REF] Laplante | Stability and progression of disordered gambling: lessons from longitudinal studies[END_REF][START_REF] Slutske | The natural history of problem gambling from age 18 to 29[END_REF]. A wide range of outcome measures is used in gambling disorder research [START_REF] Walker | A framework for reporting outcomes in problem gambling treatment research: the Banff, Alberta Consensus[END_REF]. A recent systematic review identified sixty-three different outcome measures in gambling disorder studies [START_REF] Pickering | Measuring treatment outcomes in gambling disorders: a systematic review[END_REF]. Their authors recommend developing a multidimensional scale that could assess the efficacy of an intervention in various domains of functioning [START_REF] Pickering | Measuring treatment outcomes in gambling disorders: a systematic review[END_REF]. Earlier, the Banff Consensus provided a framework with the minimum features of reporting efficacy of intervention and came to support the relevance of quality of life assessment in gambling disorder, in complement with the reduction of gambling behavior and the reduction of problems caused by gambling [START_REF] Walker | A framework for reporting outcomes in problem gambling treatment research: the Banff, Alberta Consensus[END_REF].

In 1993, the World Health Organization defined quality of life (QOL) "as an individual's perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns. It is a broad ranging concept affected in a complex way by the person's physical health, psychological state, level of independence, social relationships, and their relationship to salient features of their environment" [START_REF] Szabo | The World Health Organization Quality of Life (WHOQOL) assessment instrument[END_REF]. Two types of QOL instruments are usually used: (i) general QOL instruments exploring overall QOL regardless of any health condition and (ii) health-related QOL (HRQOL) classically involving four areas: physical, physical well-being, psychological state and social relations [START_REF] Leplège | Les Mesures de la Qualité de Vie[END_REF]. On one hand, "quality of life" describes a subjective feeling of satisfaction with life in domains of importance to the subjects. On the other hand, "health-related quality of life" reports a subjective perception of the impact of the disease and its treatment(s) on daily life, physical, psychological and social functioning and well-being [START_REF] Leidy | Recommendations for evaluating the validity of quality of life claims for labeling and promotion[END_REF].

QOL has been discussed in the medical literature since the 1960s [START_REF] Elkinton | Medicine and the quality of life[END_REF]. It became increasingly important in heath care to measure outcomes beyond morbidity and biological functioning [START_REF] Karimi | Health, Health-Related Quality of Life, and Quality of Life: What is the Difference?[END_REF]. The practice of medicine is often based on the identification and management of symptoms, while patients' expectations go beyond management of symptoms: they are looking for optimal well-being [START_REF] Luquiens | Patient preferences and perspectives regarding reducing alcohol consumption: role of nalmefene[END_REF]. In addiction, QOL assessment matches with the treatment goal of enhanced client functioning and predict treatment adherence [START_REF] Laudet | The case for considering quality of life in addiction research and clinical practice[END_REF]. Moreover, participants with addiction at all stages of recovery expressed concerns about multiple areas of functioning [START_REF] Laudet | Don't wanna go through that madness no more: quality of life satisfaction as predictor of sustained remission from illicit drug misuse[END_REF]. QOL is often included as a secondary endpoint in clinical trials, reflecting patients' feelings and functioning and the impact of their health condition beyond simple symptom assessment [START_REF] Carr | Measuring quality of life: Is quality of life determined by expectations or experience?[END_REF]. In 2006, Walker et al. stressed that there was a lack of evaluated or validated psychometric instruments assessing QOL in gambling disorder [START_REF] Walker | A framework for reporting outcomes in problem gambling treatment research: the Banff, Alberta Consensus[END_REF] whereas evaluated or validated psychometric instruments assessing QOL do exist in substance use disorders [START_REF] Luquiens | Validation of a new patient-reported outcome instrument of health-related quality of life specific to patients with alcohol use disorder: the Alcohol Quality of Life Scale (AQoLS)[END_REF][START_REF] Neale | DEVELOPMENT AND VALIDATION OF "SURE": A PATIENT REPORTED OUTCOME MEASURE (PROM) FOR RECOVERY FROM DRUG AND ALCOHOL DEPENDENCE[END_REF].

The purpose of this review was (i) to identify the instruments used in original articles to measure QOL/HRQOL in subjects with a gambling disorder in all original articles and then (ii) to assess the suitability of these instruments (reliability, content validity, effect size) in the gambling disorder field.

MATERIAL AND METHODS

The systematic review was conducted independently using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines for systematic reviews [START_REF] Liberati | The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration[END_REF] . Our review project was registered in PROSPERO database.

Our project began in 2016. Since then, we have updated our review and implemented it with a meta-analysis. So we have updated our record in PROSPERO database (https://www.crd.york.ac.uk/PROSPERO/#recordDetails).

Selection criteria

Articles were included if they were (i) original articles aiming at assessing QOL/HRQOL in problem or pathological gambling/gambling disorder subjects and using an instrument designated to measure QOL/HRQOL by the authors as an outcome and (ii) if subjects were over 18 years old. We excluded (i) articles with subjects that did not fulfill problem or pathological gambling/gambling disorder criteria and (ii) articles with Parkinson disease subjects.

Data sources

The systematic literature searched to identify original studies using QOL instruments in problem gambling was performed in the following electronic databases: PsycInfo (Ovid), MEDLINE (using PubMed platform), and Embase (Ovid).

Literature search strategy

A systematic literature search was conducted among peer-reviewed journals from 1950 to November 2018 in these three electronic databases. The search strategy included a combination of the following relevant keywords: "quality of life", "pathological gambling", "gambling disorder" and "problem gambling". The search was conducted to identify original studies that reported the use of QOL/HRQOL instruments in gambling in any country.

Electronic database searches were limited to English-language publications.

Study selection

The study selection process was comprised of the following two phases. First, titles and abstracts of all potentially relevant publications were carefully screened and reviewed for eligibility according to inclusion and exclusion criteria by two authors (AL and NB). Secondly, the full texts of studies selected at the first step were obtained and reviewed for eligibility, using the same inclusion and exclusion criteria as in Level 1, by the same researchers. When necessary, we contacted authors of relevant studies to obtain additional information, article texts, and resolve questions about eligibility. If there were disagreements between the two authors in terms of selection of articles at the abstract level/ article level, discrepancies were resolved by consensus. Finally, with the same method, we systematically conducted any search of the reference list of the included studies

Data extraction

Data were extracted from the full text of articles by both the two investigators working independently. Data extracted included the following items: aim, research question, design and setting, sample characteristics, intervention, instrument used to assess QOL/HRQOL and result(s) on QOL/HRQOL. For epidemiological studies, the main outcome measure nature and results were extracted. For trials, we extracted whether QOL/HRQOL was a primary or secondary endpoint and the other endpoint nature and results. The data were summarized in Tables 2 (see in results section).

Psychometric properties of the instrument (reliability, content validity, effect size) in gambling population were also extracted. The QOL/HRQOL instruments are generally tested for two types of reliability: (i) the internal consistency measured by Cronbach's alpha and (ii) the test-retest reliability estimated by Pearson correlation coefficient. The content validity refers to the degree to which an instrument measures the concept it has to measure. We also studied the effect size of these instruments compared with the effect size of gambling behavioral measures (See below: Meta-analysis).

Finally, we analyzed currently used QOL/HRQOL concepts in gambling-disorder subjects. For this purpose, we identified the life domains of these instruments by extracting and examining item concepts, after reviewing all relevant instruments. We have included as many domains as possible but some may overlap.

Meta-analysis

To assess the ability to detect the change of QOL/HRQOL instruments, we also performed a meta-analysis from selected trials. Three trials were excluded due to a lack of data with which to perform the meta-analysis (no sample size or lack of documentation on outcome).

Each of the gambling and QOL/HRQOL pre/post intervention outcomes were analyzed by calculating the Cohen's d effect-size for each trial with the uncertainty of each result being expressed by their 95% confidence intervals (CI). An effect-size of 0.2 to 0.3 is considered to be a "small" effect, around 0.5 a "medium" effect, and above 0.8, a "large" effect [START_REF] Cohen | Statistical power analysis for the behavioral sciences[END_REF]. Consistent with convention, a conservative estimate of r = 0.70 was imputed as the pre-post correlation in the calculation if it was not reported [START_REF] Rosenthal | Meta-Analytic Procedures for Social Research[END_REF][START_REF] Wu | Cognitive Behavioral Therapy for Insomnia Comorbid With Psychiatric and Medical Conditions: A Meta-analysis[END_REF].

Then we calculated the summarized effect-size of gambling outcomes and QOL/HRQOL ones using a random-effects model. Heterogeneity was assessed by calculating the I 2 value. The random-effects model was chosen because of the between trial heterogeneity. The underlying assumption of a random-effects model is that the true effect could vary between studies based on characteristics of the study population or intervention. Indeed, a wide range of gambling and QOL/HRQOL outcomes were used throughout the different included studies. A random-effect model is more conservative because it produces wider confidence intervals for the effect estimates than a fixed-effects model.

Publication bias was assessed using a funnel plot. In case of asymmetry, Duval and Tweedie's Trim and Fill method was used to impute missing studies. A significance level of p<. 05 (twotailed) was used for all analyses [START_REF] Duval | Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis[END_REF].

Finally, we performed a quality analysis of the included randomized controlled trials following the "Cochrane Collaboration's tool for assessing risk of bias" [START_REF] Higgins | The Cochrane Collaboration's tool for assessing risk of bias in randomised trials[END_REF], which includes five risks of bias: 1-selection bias, assessing random sequence generation and allocation concealment; 2-performance bias, assessing blinding of participants and personnel; 3-detection bias, assessing blinding of outcome assessment; 4-attrition bias, exploring for incomplete outcome data; and 5-reporting bias, searching for selective reporting.

All analyses were conducted using the Comprehensive Meta-Analysis statistical program, version 3.0 (Englewood, NJ). HJA and NB extracted the data and performed this metaanalysis.

RESULTS

The search yielded 423 citations, published between 1997 and 2018. Of these, 221 were ineligible after review of the title and abstract. Thirty-five studies were included in this review. The PRISMA flow diagram of the study selection process (Figure 1) shows the reasons for excluding articles. The thirty-five included articles were published between 2002 and 2017. We identified six types of instruments aiming at measure QOL or HRQOL -all instruments self-administered:

 The Medical Outcomes Study Item Short-Form Health Survey (SF) with some different length options for the SF: SF-36 (McHorney et al., 1993;[START_REF] Ware | SF-36 health survey update[END_REF], SF-12 [START_REF] Ware | A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity[END_REF], SF-8 [START_REF] Ware | How to score and interpret single-item health status measures: a manual for users[END_REF] and SF-6D [START_REF] Brazier | The estimation of a preference-based measure of health from the SF-36[END_REF][START_REF] Brazier | Deriving a preference-based single index from the UK SF-36 Health Survey[END_REF],  The European Quality of Life Questionnaire (EQ-5D) and the EQ-Visual analogue scale (EQ-VAS) (EuroQol [START_REF] Group | EuroQol--a new facility for the measurement of health-related quality of life[END_REF][START_REF] Scott | Graphic representation of pain[END_REF],  The World Health Organization Quality of Life Assessment Instrument [START_REF] Szabo | The World Health Organization Quality of Life (WHOQOL) assessment instrument[END_REF] with some length options for the WHO-QOL-BREF and the EUROHIS-QOL 8 item index [START_REF] Schmidt | The EUROHIS-QOL 8-item index: psychometric results of a cross-cultural field study[END_REF],  The Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q) [START_REF] Endicott | Quality of Life Enjoyment and Satisfaction Questionnaire: a new measure[END_REF],  The Quality of Life Inventory (QOLI) [START_REF] Frisch | Clinical validation of the Quality of Life Inventory. A measure of life satisfaction for use in treatment planning and outcome assessment[END_REF],  The Personal Well Being Index -Adult (PWI-A) [START_REF] Cummins | Developing a National Index of Subjective Wellbeing: The Australian Unity Wellbeing Index[END_REF].

The instruments used most frequently were the SF (n=11), followed by the QOLI (n=10) and WHOQOL (n=6) (Table 1). In the last three years of the search (i.e. 2016, 2017, 2018), the four following instruments were used: the QOLI (n=4), the SF (n=3), the WHOQOL-BREF (n=1) and the Q-LES-Q (n=1) (Table 1). These instruments were initially designed for various purposes and referred to different concepts. The SF instruments were constructed to measure HRQOL. The EQ-5D is a standardized measure of health status, commonly used as a HRQOL instrument. The other instruments were developed to measure QOL. None of these instruments were specifically designed to gambling disorder.

The methods and the results of the thirty-five articles are presented in Tables 2.

Description of the instruments and their psychometric properties

The instruments differed widely in number of domains and items, scaling, scoring and psychometric properties. The complete description of these instruments is provided in Table 3.

The psychometric properties of the included scales have been poorly studied in the gambling population. We report those given in the included articles. The SF-12, SF-8 and SF-6D derive from the SF-36. In a sample of 77 pathological gamblers, internal consistency (Cronbach's alpha) of the SF-12 domains ranged from 0.76 to 0.77, indicating rather good reliability [START_REF] Mcintosh | Treating Problem Gambling Samples with Cognitive Behavioural Therapy and Mindfulness-Based Interventions: A Clinical Trial[END_REF]. No more psychometric properties have been reported in the gambling population for SF scales. In a sample of 139 individuals diagnosed with gambling disordered, internal consistency of the WHOQOL-BREF domains measured ranged from 0.66 to 0.84, indicating rather good reliability [START_REF] Oei | Cognitive and Psychosocial Variables Predicting Gambling Behavior in a Clinical Sample[END_REF]. In a sample of 260 alcoholdependent, 282 drug-dependent, and 132 pathological gambling outpatients, internal consistency of the PWI was 0.87 [START_REF] Manning | An exploration of quality of life and its predictors in patients with addictive disorders: Gambling, alcohol and drugs[END_REF]. To the best of our knowledge, psychometric properties of EQ-5D, QOLI and Q-LES-Q have not been reported in the gambling-disordered population.

Analysis of the current concept of quality of life in gambling-disordered subjects

The twenty-six domains investigated by the QOL/HRQOL instruments of the included articles are presented in Table 4. We categorized these domains into the following broader categories: relationships with others, activities, physical state, psychological state, financial concerns, medical care and satisfaction with life. Some disparities can be noticed between the different instruments on the assessed areas. Most of the instruments assessed social relationships (8/9) whereas EQ-5D did not. Moreover, only QOLI assessed "problems with children" and only WHOQOL-BREF did not assess family relationships. Social activities form the most explored domain across the instruments. Finally, only 3 instruments out of 6 assess the financial concerns (QOLI, WHOQOL-BREF and Q-LES-Q).

Meta-analysis: effect size of QOL instruments

We studied the effect size of the QOL/HRQOL instruments and gambling outcomes after an intervention. To this end, we only selected trials and performed a meta-analysis on gambling outcomes and QOL outcomes. Three trials were excluded because of a lack of documentation on QOL or outcome results. When combining the seven trials, our metaanalysis showed these results: the global effect size for QOL/HRQOL was 0.54 (95%CI 0.34-0.73); concerning gambling outcome, the global effect size was 1,550 (95%CI 1.20-1.90) (Figure 2). Thus the measurement of QOL with non-gambling specific instruments showed a significant medium-range effect size, significantly smaller than the large-range gambling outcome effect size. The heterogeneity was quite high, rather similar for both outcomes: I 2 = 87,82 for gambling outcome and I2= 83,20 for QOL/HRQOL. A visual inspection of the funnel plot for open-label studies revealed asymmetry, indicating possible publication bias (Figure 3). Using Trim and Fill the imputed point estimate is barely modified: 0.54 (95%CI 0.34-0.73) for the QOL/HRQOL outcome and 1.53 (95%CI 1.17-1.89) for the gambling outcome.

DISCUSSION

To our knowledge, this is the first review that inventories and assesses the suitability of QOL/HRQOL instruments in the gambling problem. We reviewed randomized controlled trials, non-comparative trials and epidemiological studies in order to be as comprehensive as possible. We explored the suitability of these instruments through their psychometric properties and their content. In addition, we performed a meta-analysis to analyze their global effect size compared to the effect size of gambling behavioral outcomes.

First, this review found that a high degree of heterogeneity in QOL/HROL used instruments in the gambling studies: six different types of instruments were used in thirtyfive included articles. Among these thirty-five included articles, only three articles were randomized clinical trials and seven were non-comparative trials. We found twenty-five epidemiologic studies that completed the inventory. Among trials, QOL/HRQOL was a secondary outcome for eight [START_REF] Carlbring | Randomized trial of internet-delivered self-help with telephone support for pathological gamblers[END_REF][START_REF] Garcia-Caballero | The efficacy of Motivational Intervention and Cognitive-Behavioral Therapy for Pathological Gambling[END_REF][START_REF] Grant | Escitalopram treatment of pathological gambling with cooccurring anxiety: an open-label pilot study with double-blind discontinuation[END_REF][START_REF] Kim | Gender Differences Among Helpline Callers: Prospective Study of Gambling and Psychosocial Outcomes[END_REF][START_REF] Lahti | Treatment of pathological gambling with naltrexone pharmacotherapy and brief intervention: a pilot study[END_REF][START_REF] Mcintosh | Treating Problem Gambling Samples with Cognitive Behavioural Therapy and Mindfulness-Based Interventions: A Clinical Trial[END_REF][START_REF] Oei | Effectiveness of a Self Help Cognitive Behavioural Treatment Program for Problem Gamblers: A Randomised Controlled Trial[END_REF][START_REF] Zimmerman | An open-label study of citalopram in the treatment of pathological gambling[END_REF] and among epidemiological articles, only eight assessed QOL/HRQOL as a primary endpoint [START_REF] Black | Quality of life and family history in pathological gambling[END_REF][START_REF] Ekholm | A nationwide study of healthrelated quality of life, stress, pain or discomfort and the use of medicine among problem gamblers[END_REF][START_REF] Grant | Quality of life in kleptomania and pathological gambling[END_REF][START_REF] Kohler | A monetary valuation of the quality of life loss associated with pathological gambling: An application using a health utility index[END_REF][START_REF] Loo | Gambling, Drinking and Quality of Life: Evidence from Macao and Australia[END_REF][START_REF] Manning | An exploration of quality of life and its predictors in patients with addictive disorders: Gambling, alcohol and drugs[END_REF][START_REF] Mythily | Quality of life in pathological gamblers in a multiethnic Asian setting[END_REF][START_REF] Scherrer | Effect of genes, environment, and lifetime co-occurring disorders on health-related quality of life in problem and pathological gamblers[END_REF]. This finding shows that the relevance of QOL/HRQOL is not fully recognized in gambling studies. The addiction field has come late to the chronic disease perspective. These models address the impact of disease and services on the patient's overall well-being. That is why the concept of QOL/HRQOL in addiction is relatively undeveloped whereas it is highly relevant [START_REF] Laudet | The case for considering quality of life in addiction research and clinical practice[END_REF]). If we consider observational studies, only one study was longitudinal; the others were cross-sectional with no possibility of exploring a change over time. Most often, QOL/HRQOL has been studied according to whether one is a pathological gambler, a problem gambler or a non-gambler. Despite the use of heterogeneous and generalist QOL/HRQOL measurement instruments, observational studies found that pathological and problem gamblers were more impacted than controls in QOL [START_REF] Black | Pathological gambling: relationship to obesity, self-reported chronic medical conditions, poor lifestyle choices, and impaired quality of life[END_REF][START_REF] Chamberlain | White matter tract integrity in treatment-resistant gambling disorder[END_REF][START_REF] Ekholm | A nationwide study of healthrelated quality of life, stress, pain or discomfort and the use of medicine among problem gamblers[END_REF][START_REF] Grant | Quality of life in kleptomania and pathological gambling[END_REF][START_REF] Kohler | A monetary valuation of the quality of life loss associated with pathological gambling: An application using a health utility index[END_REF][START_REF] Medeiros | Anxiety, Gambling Activity, and Neurocognition: A Dimensional Approach to a Non-Treatment-Seeking Sample[END_REF][START_REF] Mythily | Quality of life in pathological gamblers in a multiethnic Asian setting[END_REF][START_REF] Scherrer | Effect of genes, environment, and lifetime co-occurring disorders on health-related quality of life in problem and pathological gamblers[END_REF]. The duration of illness was also correlated with QOL [START_REF] Medeiros | Gambling disorder: Association between duration of illness, clinical, and neurocognitive variables[END_REF]. Some sub-scores on the scales, particularly SF scale, were not significant: physical subscale, bodily pain and even social functioning for example [START_REF] Mason | Problem gambling risk factors and associated behaviours and health status: results from the 2002/03 New Zealand Health Survey[END_REF][START_REF] Morasco | Gambling problems and health functioning in individuals receiving disability[END_REF][START_REF] Najavits | Pathological Gambling and Posttraumatic Stress Disorder: A Study of the Co-Morbidity versus Each Alone[END_REF][START_REF] Scherrer | Effect of genes, environment, and lifetime co-occurring disorders on health-related quality of life in problem and pathological gamblers[END_REF]. This could be explained by the fact that these sub-dimensions seem to be poorly adapted to problem gambling. The instruments used were not completely or not at all validated in this population regarding the psychometric properties. It is an important limit regarding the use of these instruments. Moreover, we performed a metaanalysis that revealed a smaller effect size of QOL/HRQOL instruments than gambling outcomes. It was not surprising that QOL/HRQOL showed a smaller effect size than gambling specific outcomes, as it was a more general construct, and could be affected by factors unrelated to gambling. However, this difference seemed huge. We have two explanations for this result. The first is that current therapeutic interventions would improve the behavioral symptoms of addiction but would not consequently improve QOL/HRQOL. It would then be important to consider interventions that improve QOL/HRQOL beyond behavioral change, without excluding each other. The other explanation would be that change in QOL/HRQOL is not fully captured by the QOL/HRQOL instruments used. This could be explained by the fact that these instruments are not gambling specific and are less able to measure a change than the other specific gambling behavioral measures. It would be interesting to develop an instrument to measure QOL specific to gambling.

Secondly, a content analysis of the QOL/HRQOL instruments revealed that they explored twenty-six domains that we have categorized in seven broader categories: relationships with others, activities, physical state, psychological state, financial concerns, medical care and satisfaction with life. Nevertheless, these instruments are general (i.e. unspecific) QOL/HRQOL instruments, and might not evaluate the whole range of impacted QOL/HRQOL caused by gambling disorders. Only a few studies revealed the results of the scale subscores, while others gave an overall result. When we looked at the studies giving the results of the sub scores, only one study found a significant result on all the sub-scores including mental health, physical functioning, bodily pain, and general health [START_REF] Black | Quality of life and family history in pathological gambling[END_REF]. The other studies found mental sub-score significantly associated with gambling but no significant results regarding physical, bodily pain, vitality or general health sub-scores [START_REF] Black | Pathological gambling: relationship to obesity, self-reported chronic medical conditions, poor lifestyle choices, and impaired quality of life[END_REF][START_REF] Mason | Problem gambling risk factors and associated behaviours and health status: results from the 2002/03 New Zealand Health Survey[END_REF][START_REF] Mcintosh | Treating Problem Gambling Samples with Cognitive Behavioural Therapy and Mindfulness-Based Interventions: A Clinical Trial[END_REF][START_REF] Morasco | Gambling problems and health functioning in individuals receiving disability[END_REF][START_REF] Najavits | Pathological Gambling and Posttraumatic Stress Disorder: A Study of the Co-Morbidity versus Each Alone[END_REF][START_REF] Scherrer | Effect of genes, environment, and lifetime co-occurring disorders on health-related quality of life in problem and pathological gamblers[END_REF]. Previous studies focused on harms caused by gambling (Commission, 2010;[START_REF] Langham | Understanding gambling related harm: a proposed definition, conceptual framework, and taxonomy of harms[END_REF] or on HRQOL areas impacted by gambling [START_REF] Bonfils | Giving room to subjectivity in understanding and assessing problem gambling: A patient-centered approach focused on quality of life[END_REF]. [START_REF] Bonfils | Giving room to subjectivity in understanding and assessing problem gambling: A patient-centered approach focused on quality of life[END_REF] aimed to describe HRQOL impacted by problem gambling from a patients' perspective, through a qualitative study. Financial pressure, relationships deterioration, negative emotions, loneliness, feeling of incomprehension, preoccupation with gambling and avoidance of helping relationship were found to be HRQOL domains impacted by gambling [START_REF] Bonfils | Giving room to subjectivity in understanding and assessing problem gambling: A patient-centered approach focused on quality of life[END_REF]. Loneliness, feeling of incomprehension, preoccupation with gambling and avoidance of helping relationship were not mentioned in HRQOL instruments previously used in problem gambling [START_REF] Bonfils | Giving room to subjectivity in understanding and assessing problem gambling: A patient-centered approach focused on quality of life[END_REF]. Moreover, Luquiens et al. [START_REF] Luquiens | Development of the Alcohol Quality of Life Scale (AQoLS): a new patient-reported outcome measure to assess health-related quality of life in alcohol use disorder[END_REF] studied the impact of alcohol use disorder on HRQOL through a qualitative analysis. To this end, they conducted focus groups with 38 alcohol use disorder subjects and found seven areas of impact: relationships, emotional impact, living conditions, control, activities, looking after self and sleep. Only the first three areas were common for both addictive disorders. Although substance use disorder and gambling disorder share common characteristics, people with gambling disorders also have specific characteristics [START_REF] Grant | Gambling disorder and its relationship with substance use disorders: implications for nosological revisions and treatment[END_REF]. It seems that general QOL/HRQOL instruments do not capture gambling specificities. It was for this reason that recent Burden of Disease assessments of gambling in New Zealand and Australia utilized time trade off and visual analogic scale elicitation methods to direct assess community and expert opinions, rather inferring QOL from generic instruments [START_REF] Browne | Measuring the Burden of Gambling Harm in New Zealand[END_REF]. The six different instruments listed in the included articles are very heterogeneous regarding the QOL/HRQOL domains they assess (Table 3). It would be interesting to standardize QOL/HRQOL instruments in this population in order to allow comparisons across different studies. However, there is currently no specific instrument to assess a change in QOL/HRQOL in subjects with a gambling disorder while there are instruments for specific diseases in many specialties (such as oncology [START_REF] Macquart-Moulin | High-dose sequential chemotherapy with recombinant granulocyte colony-stimulating factor and repeated stem-cell support for inflammatory breast cancer patients: does impact on quality of life jeopardize feasibility and acceptability of treatment?[END_REF] or neurology [START_REF] Fujishima-Hachiya | Development and validation of a disease-specific scale to assess psychosocial well-being of patients living with unruptured intracranial aneurysm[END_REF][START_REF] Tan | Validation of a short disease specific quality of life scale for hemifacial spasm: correlation with SF-36[END_REF] or gastroenterology [START_REF] Zingone | The Italian translation of the celiac disease-specific quality of life scale in celiac patients on gluten free diet[END_REF]), and in the addiction field in particular (as alcoholism [START_REF] Luquiens | Validation of a new patient-reported outcome instrument of health-related quality of life specific to patients with alcohol use disorder: the Alcohol Quality of Life Scale (AQoLS)[END_REF][START_REF] Luquiens | Development of the Alcohol Quality of Life Scale (AQoLS): a new patient-reported outcome measure to assess health-related quality of life in alcohol use disorder[END_REF][START_REF] Neale | DEVELOPMENT AND VALIDATION OF "SURE": A PATIENT REPORTED OUTCOME MEASURE (PROM) FOR RECOVERY FROM DRUG AND ALCOHOL DEPENDENCE[END_REF]).

However, it seems interesting to look at which general instruments of QOL/HRQOL seem most appropriate to use in this field of research, taking into account psychometric properties and content. SF, WHOQOL, PWI are the only instruments that are partially psychometrically validated with a correct internal consistency in gambling population. Only SF scales are HRQOL instruments; WHOQOL and PWI are QOL instruments. Nevertheless, SF scales fail to capture functioning in domains-especially financial concerns or self-esteem -that are important to gambling disorder-affected populations. The same is true for PWI, which also does not explore these domains or psychological well-being. The scale with both psychometric qualities and the most appropriate content for gambling is probably WHOQOL which offers a very promising alternative, yielding scores in psychological and social functioning; living environment; and an overall satisfaction score [START_REF] Laudet | The case for considering quality of life in addiction research and clinical practice[END_REF].

Finally, QOL instruments should be designed to reflect the impact of diseases and interventions in daily life from the subject's perspective [START_REF] Leplège | Les Mesures de la Qualité de Vie[END_REF]. However, most items of these instruments have been defined by experts to answer their own questions.

Therefore a tension may appear between the objective of the experts and the impact of the or treatment as perceived by subjects. Despite being self-administered and requesting subjects' subjective answers, the instruments are not necessarily a good reflection of subjects concerns. Moreover, if these general instruments can partially explore the occurrence of adverse consequences related to gambling disorder, it is important to evaluate the subjective value of these consequences for each subject. It is clear that two subjects can assign different weights to a single negative consequence related to his gambling addiction. While many studied areas are common across the six instruments, the wording of the question and thus the answers can differ widely. For instance, the wording of the question on the financial aspect varies between two questionnaires. The Q-LES-Q is interested in the satisfaction with the economic status during the past week. The WHOQOL-BREF asks if the subject had enough money to meet his (her) needs in the last two weeks.

The different wordings and the different periods explored could induce dissimilar answers.

The Food and Drug Administration (FDA) encourages specific Patients Reported Outcomes (PROs) instrument to support labeling claims of medical products [START_REF] Fda | Guidance for Industry Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims[END_REF]. PRO instruments insure the subjectivity of the outcome, but not its clinical pertinence. A pertinent PRO instrument should be patient-focused. One way to reach this goal would be to develop the instrument from the subjects themselves, via qualitative analysis of interviews with gambling-disorder subjects. This methodology would allow identification of the most impacted domains of life in this particular population and those aspects that are most sensitive to change, thereby promising real usefulness in assessing the efficacy of interventions among gambling disorder subjects. The development of a patient-focused rigorous PRO QOL instrument specific to subjects with gambling disorders would allow clinicians to have a relevant instrument to administer to their patients [START_REF] Arpinelli | The FDA guidance for industry on PROs: the point of view of a pharmaceutical company[END_REF].

There are several limitations to our literature review. First of all, we did not conduct an indepth search of the grey literature. However, we looked at the grey literature with the same keywords on the database OpenSIGLE, recommended by Cochrane, and did not find any results. Secondly, our review was limited to English-language articles. Some studies may not have been included due to this. With regard to meta-analysis, we can also highlight some limitations. First of all, the included articles had a great heterogeneity of judgment criteria.

Finally, we did not take into account the biases of the trials for inclusion in the metaanalysis.

Conclusion

A successful intervention can be indirectly measured by consequent increases in the QOL.

The currently used general instruments are efficient to measure a significant change after an intervention but might not evaluate specific areas of health related QOL impacted by gambling disorders. Moreover, they are not totally validated in the problem gambling population and are probably less sensitive than gambling behavioral criteria. Despite being self-administered and requesting subjective answers, these instruments do not necessarily explore the entire spectrum of patients' concerns on the impact on QOL of gambling disorder and do not take into account the patient's subjectivity. Patients should contribute to define the content of impact on QoL of gambling disorder. Such an approach could serve as the basis for the future development of a tool specifically designed for gambling disorders in order to assess health-related QoL. References

  Figure and Tables Figure 1. PRISMA flow diagram of the study selection process Legend: 'Wrong populations' designed subject without gambling disorder or, in other term, without

Figure

  Figure 2. Forest plot

  Wash out period between the 2 interventions ; b NODS : National Opinion Research Center DSM Screen for Gambling Problems SB: selection bias; PB: performance bias; DB: detection bias; AB: attrition bias; RB: reporting bias QOL : Quality of life

  : cognitive-behavioral therapy ; b : PG-YBOCS : Yale-Brown Obsessive Compulsive Scale Modified for Pathological Gambling QOL : Quality of life

  between older adults and younger adults 53 older adults with pathological gambling (≥ 60 years), and two comparison groups including 72 Gambling levels of Shaffer and Hall SF-36 No reported result No difference between the groups. lower QOL among pathological gamblers than among alcohol patients. But no significantly differences in personal relationships item 2/ Alcohol patients scored significantly higher on the PWI compared with drug patients scored healthier than PTSD and BOTH on the physical subscale, High rates of additional co-occuring disorders and suicidality in PTSD and BOTH 59% and 34% reported respectively a family history of substance use disorder and gambling problems

diagram of the study selection process

  Legend: 'Wrong populations' designed subject without gambling disorder or, in other term, without

	gambling problem/pathological gambling
		646
	647 Records identified from database search 648 (n=423)
			Records excluded with reasons
			(n=221)
			126 wrong populations
	Records after removal of duplicates (n=265)	650	45 no QoL measure clearly identified or no QoL instrument clearly identified
		651	50 not an article (book chapter or thesis or
			commentaries or congress abstract)
		653
	Records screened on title and abstract		2 articles in japoneese and no email contact
	(n=265)
		654
			1 article
			1 article in german
	Full-text articles assessed for eligibility
	(n=42)		Full-text articles excluded with reasons
			(n=7)
			2 wrong populations
	Studies included in qualitative synthesis	4 no QoL measure clearly identified
	(n=35)		2 no QoL instrument clearly identified
			1 methodological article

Table 1 . Instruments designated as measuring quality of life in included studies among gambling-disorder patients.

 1

		SF-36/SF-	QOLI WHO-QOL-BREF,	Q-LES-Q	EQ-5D	PWI
		12	EUROHIS-QOL 8-		EQ-VAS
		SF6 -D/SF-	item index
		8
	Ekholm et al. (2018) (Ekholm et	x
	al., 2018)
	Harries et al. (2018) (Harries et		x
	al., 2018)
	Oei et al. (2018) (Oei et al.,		x
	2018)
	Garcia-Caballero et al. (2018)			x
	(Garcia-Caballero et al., 2018)
	Black et al. (2017) (Black et al.,	x
	2017)
	Blum et al. (2017) (Blum et al.,		x
	2017)
	Chamberlain et al. (2017)		x
	(Chamberlain et al., 2017)
	Medeiros et al. (2017)		x
	(Medeiros et al., 2017)
	Chamberlain et al. (2016)		x
	(Chamberlain et al., 2016)
	Kim et al (2016) (Kim et al.,		x
	2016)
	Loo et al. (2016) (Loo et al.,		x
	2016)
	Madeiros et al. (2016)		x
	(Medeiros et al., 2016)
	McIntosh et al. (2016)	x
	(McIntosh et al., 2016)
	Manning et al. (2015) (Manning					x
	et al., 2015)
	Oei et al. (2015) (Oei and Raylu,		×
	2015)
	Subramanian et al (2015)				x
	(Subramaniam et al., 2015)
	Kohler (2014) (Kohler, 2014)	×			×
	Manning et al. (2014) (Manning
	et al., 2014)

Table 2b . Methods and results on quality of life of the non-comparative trials

 2b

	Lahti et al. (2010)	A non-comparative, single group	39 pathological gamblers	50mg naltrexone one hour before gambling or feeling urges	19 weeks	EQ-5D	Yes	2	PG-YBOCS b : Yes
	(Lahti et al., 2010)	study		to gamble associated with written instructions for practicing
				alternative behaviors.
		Finland
	Studies	Design and setting	Population	Intervention	Length of	Instrument	Results on QOL:	1st or 2ary	Primary outcome:
					the study		Significant difference	outcome	Significant difference
							between pre-and post-		between pre-and post-
							treatment		treatment
							(Yes/No)		(Yes/No)
	Garcia-Caballero	A non-comparative, single group	18 pathological gamblers	Motivational intervention and CBT therapy	6 months	Q-LES-Q	No	2	Yes
	et al. (2018)	study
	(Garcia-Caballero
	et al., 2018)	Spain
	Kim et al. (2016)	An uncontrolled trial	150 pathological gamblers	Standard helpline care	12 months EUROHIS-QOL 8-	Yes	2	Yes
	(Kim et al., 2016)					item index
		New-Zealand
	Manning et al.	A non-comparative, single group	284 pathological gamblers	A CBT-based therapy	12 weeks	PWI	Yes	1	Composite outcome
	(2014)	study							measure: Yes
	(Manning et al.,
	2014)	Singapore
	Carlbring et al.	A non-comparative, single group	284 pathological gamblers	An 8-week Internet-Based Treatment program	36 months	QOLI	Yes	1	Composite outcome
	(2012)	study							measure: Yes
	(Carlbring et al.,
	2012)	Sweden

Table 2c . Methods and results on quality of life of the epidemiological studies

 2c

	Studies	Design / Setting	Objective	Population	Main outcome(s) measure(s)	Instruments	Main Result(s) on QOL	Main Result(s)
	Ekholm et al.	Cross-sectional	To investigate the association between	14 924 individuals with current,	SF-12	SF-12	Both current and previous problem gambling were negatively associated with
	(2018)	study	problem gambling and health-related	past and never problem gambling			physical and mental-health problems and QOL.
	(Ekholm et		quality of life, stress, pain or
	al., 2018)		discomfort and the use of analgesics
		Denmark	and sleeping pills

Table 3 . Description of the used instruments

 3

	Instruments Domains (items) Domain description				Scaling & scoring
	Health-related quality of life instruments
	SF-36*	8 (36)	Physical functioning, physical role, limitation	Response choices
			emotional role limitation, bodily pain, mental	varied according to the
			health, social functioning, vitality and general	item.
			health perception				Lickert method of
								summated ratings.
								Total and subscale
								score = summing all
								item scores.
								Higher score indicates
								greater health.
								Two sub-scores: the
								mental and the physical
								sub-scores.
	EQ-5D*	5(15)	Mobility,	self-care,	usual	activities,	Descriptive system
			pain/discomfort and anxiety/depression		score (243 possible
								health states) and a
								visual analogue scale
								(score from 0 to 100)
								Higher score indicates
								greater health
	Quality of life instruments
	WHOQOL-	4 (100)	Physical health, mental health, social relationship	5-point Lickert scale
	100*		and environment				A total score is not
								possible. A score by
								domains is available
								according	to	the
								WHOQOL user manual.
								Higher score indicates
								greater health
	Q-LES-Q	2 (16)	Medication and life satisfaction and contentment 5-point Lickert scale
								A total score of items
								is	computed	and
								expressed	as	a
								percentage of the
								maximal possible score
								of 70.
								Higher score indicates
								greater health
	QOLI*	16 (32)	Health, self-regard, philosophy of life, standard of	A total score of items is
			living, work, recreation, learning, creativity, social	computed and
			service,	friendships,	love	relationships,	expressed as
			relationship with children, relationship with	aweighted satisfaction
			relatives, home, neighborhood, community		ratings range from -6 to
								6.
								Higher score indicates
								greater health
	PWI*	7 (7)	Standard of living, health, life achievements,	11-point Likert scale
			personal	relationships,	personal	safety,	The seven domain
			community connectedness, future security,		scores can be summed
								to yield an average
								score which represents

Table 4 . Domains investigated by quality of life instruments used in included studies.

 4

	Categories	Domains	SF-36	QOLI	WHOQOL-	Q-LES-Q	EQ-5D	PWI
			SF-12		BREF /		EQ-VAS
			SF6-D/SF-8		EURHOHIS-
					QOL 8-item
					index
	Relation to others	Family relationships	×	×		×	×	×
		Problems with children		×
		Social relations	×	×	×	×		×
	Activities	Work	×	×	×	×	×
		Leisure		×	×	×	×
		Social responsibilities/daily	×	×	×	×	×	×
		activities
	Physical state	Autonomy	×			×	×
		Self-care	×			×	×
		Mobility	×		×	×	×
		Physical activity	×			×
		Sexual activity			×	×
		Health state	×	×	×	×		×
		Physical appearance			×	×
		Physical pain	×	×	×	×	×

but not on the mental. Only mental health component score was significantly more impacted among disordered gamblers than among non-disordered gamblers.


H Boudra

P Noziere

G Cantalapied-Hijar

M Traikia

J-F Martin

M Petera

M Lagree

M Doreau

D P Morgavi
email: diego.morgavi@inrae.fr

Spot urine collection: A valid alternative to total urine collection for metabolomic studies in dairy cattle

Keywords: urine, lactating dairy cow, nitrogen diet, metabolomics, spot collection

Urine is a highly suitable biological matrix for metabolomics studies. Total collection for 24-h periods is the gold standard as it ensures the presence of all metabolites excreted throughout the day. However, in animal studies, it presents limitations related to animal welfare and also due to alterations of the metabolome originating from the use of acid for preventing microbial growth or microbial contamination. In this study, we investigated whether spot urine collection is a practical alternative to total collection for metabolomic studies in lactating cows. For this purpose, we collected urine samples from 4 lactating Holstein cows fed 4 diets in a 4 × 4 Latin square design. Urine was collected for 24 h using a collecting device (i.e., total collection) or collected once per day 4 h after the morning feeding (i.e., spot urine collection). Dietary treatments differed by the amount of nitrogen content (high vs. low) and by the nature of the energy (starch vs. fiber). Urine metabolome was analyzed by 2 untargeted complementary methods, nuclear magnetic resonance and hydrophilicinteraction liquid chromatography (HILIC) coupled to a time-of-flight mass spectrometer, and by 1 targeted method, HILIC-tandem mass spectrometry. Although sampling technique had an effect on the abundance of metabolites detected, spot urine samples were equally capable of showing differences in urine metabolome than samples from total collection. When considering nitrogen levels in the diet, the robustness and precision for discriminating high-and low-nitrogen diets was equally achieved with both sampling techniques. A total of 22 discriminant metabolites associated with the N level of diets were identified from untargeted HILIC coupled to a time-of-flight mass spectrometer (n = 9) and nuclear magnetic resonance (n = 11), and 2 from targeted HILIC-tandem mass spectrometry. Alternatively, starch or fiber in the diet induced less changes in the metabolome that were not clearly discriminated independently of the sampling technique. We concluded that spot urine collection can successfully reveal differences in the urine metabolome elicited by dietary N levels and be used as a substitute of total urinary 24-h collection for metabolomic studies.

INTRODUCTION

Metabolomics is a powerful tool for studying metabolic changes in mammals [START_REF] Sun | Metabolomics of four biofluids from dairy cows: potential biomarkers for milk production and quality[END_REF][START_REF] Contreras-Jodar | Heat stress modifies the lactational performances and the urinary metabolomic profile related to gastrointestinal microbiota of dairy goats[END_REF]. Urine is a suitable matrix when compared with other biofluids such as plasma, as it can be obtained noninvasively. In addition, unlike plasma and milk, urine samples do not require extraction with organic solvents that may affect the metabolome, making it the matrix of choice in many studies [START_REF] Dunn | Systems level studies of mammalian metabolomes: The roles of mass spectrometry and nuclear magnetic resonance spectroscopy[END_REF][START_REF] Vuckovic | Current trends and challenges in sample preparation for global metabolomics using liquid chromatography-mass spectrometry[END_REF].

Urine is collected in 2 ways, total collection (TC) performed over 1 or several pooled 24-h periods, and spot collection (SC) performed at different times and frequencies throughout the day. Total collection is considered the gold standard as it provides a representative sample that can be used for quantifying the daily metabolite excretion from individual animals, whereas SC is less precise as it is based on models and indirect measures to estimate daily urine output. Indeed, total urinary excretion of specific metabolites is currently used for estimating key physiological processes in ruminants such as the rumen microbial protein flow to the duodenum (i.e., from urinary purine derivatives excretion; [START_REF] Chen | Estimation of microbial protein supply to sheep and cattle based on urinary excretion of purine derivatives-An overview of the technical details[END_REF] or the myofibrillar protein degradation rate (i.e., from 3-methyl-histidine urinary excretion; [START_REF] Harris | The urinary excretion of N taumethyl histidine by cattle: Validation as an index of muscle protein breakdown[END_REF]. However, in research with animals, and ruminants in particular, there are many drawbacks linked to this type of collection. Total urine collection requires the animals be housed in tiestalls, reducing the number of individuals that can be used in experiments [START_REF] Wilson | Spot and cumulative urine samples are suitable replacements for 24-hour urine collections for objective measures of dietary exposure in adults using metabolite biomarkers[END_REF]. Also, tethering and tiestall systems pose additional welfare issues for cows [START_REF] Nalon | Protection of dairy cattle in the EU: State of play and directions for policymaking from a legal and Boudra et al[END_REF]. Additionally, it is not possible to perform TC under grazing conditions. It is also noted that TC is complicated as it requires the use of harnesses to hold collection devices or, in the case of females, invasive indwelling catheters. Moreover, TC using noninvasive devices is associated with a higher risk of macroscopically undetected fecal contamination that can modify the urinary metabolome by adding fecal metabolites or by degrading already-present metabolites through the action of contaminating microbes [START_REF] Patra | Ureases in the gastrointestinal tracts of ruminant and monogastric animals and their implication in urea-N/ammonia metabolism: A review[END_REF]. The negative effect of fecal contamination may be amplified by high temperature and exposure to light [START_REF] Boomsma | Optimal collection and storage conditions for catecholamine measurements in human plasma and urine[END_REF][START_REF] Fernández-Peralbo | Preparation of urine samples prior to targeted or untargeted metabolomics mass-spectrometry analysis[END_REF][START_REF] Kirwan | Preanalytical processing and biobanking procedures of biological samples for metabolomics research: A white paper, community perspective (for "precision medicine and pharmacometabolomics task group"-the metabolomics society initiative)[END_REF]. Addition of chemical preservatives such as sulfuric acid reduces the risk of microbial growth but can also alter the urine metabolome. Sulfuric acid does react with some compounds, such as alcohols, steroids, sugars, or proteins, causing degradation and modifications of metabolites into several end products. Metabolite degradation, adducts formation, and ion pairing created by the strong acid undermines sensitivity of MS [START_REF] Chowdhury | Origin and removal of adducts (molecular mass = 98 u) attached to peptide and protein ions in electrospray ionization mass spectra[END_REF][START_REF] Furey | Ion suppression; A critical review on causes, evaluation, prevention and applications[END_REF], diminishing the discovery potential of untargeted metabolomics. In contrast, SC is an easier and rapid way to collect urine samples that reduces contamination and avoids the use of preservatives. In addition, SC aligns with current welfare recommendation in dairy production [START_REF] Nalon | Protection of dairy cattle in the EU: State of play and directions for policymaking from a legal and Boudra et al[END_REF]. We hypothesized that SC could provide similar insight as TC on the urine metabolome of lactating cows. The objective of this study was to compare the potential of SC and TC to discriminate differences in the metabolome. To have a more complete image of the urine metabolome and a more robust comparison between sampling techniques, we used untargeted and targeted metabolomic methods. We used lactating dairy cows that were fed differing amounts of nitrogen and different types of energy sources. A secondary objective of this work was to identify urinary metabolites that reflected differences in diet composition.

MATERIALS AND METHODS

Animals, Diets, and Sample Preparation

The experiment was conducted at the animal experimental facilities of the HerbiPole at Marcenat, INRAE, in 2018. Low Mountain Ruminant Farming Systems Facility, DOI 10.15454/1.5572318050509348E12. Procedures with animals were conducted in accordance with the guidelines for animal research of the French Ministry of Agriculture and applicable European guidelines and regulations for experimentation with animals (https: / / eur -lex .europa .eu/ legal -content/ EN/ TXT/ PDF/ ?uri = CELEX: 32010L0063 & from = EN).

The experimental design was previously described [START_REF] Fanchone | Effects of nitrogen underfeeding and energy source on nitrogen ruminal metabolism, digestion, and nitrogen partitioning in dairy cows[END_REF]. Briefly, 4 multiparous (parity = 2.5 ± 0.6; mean ± SD) lactating Holstein dairy cows were fed diets containing 11% CP (low N) or 14% CP (high N) with high (30%) or low (15%) starch. Cows were 4.7 ± 0.9 yr old, weighed 662 ± 62 kg, and were 71 ± 10 DIM at the beginning of the study. Diets were assigned to the cows during 4 successive periods in a 4 × 4 Latin square design. Each experimental period lasted 28 d and consisted of 23 d of adaptation to the diet and 5 d of measurements. A transition period of 3 d was applied between each experimental period. Animals were fed twice daily at 0900 and 1700 h. Diets had similar net energy values and proportion of forage to favor similar DM intake and avoid confounding effects on digestibility between diets due to level of intake. Diet composition is shown in Supplemental Table S1 (https: / / data .inrae .fr/ dataset .xhtml ?persistentId = doi: 10 .15454/ FFFRL2, [START_REF] Morgavi | Supporting data for "Spot urine collection: A valid alternative to total urine collection for metabolomic studies in dairy cattle[END_REF]. Daily DMI and milk production throughout the study was, respectively, 20.2 ± 0.8 kg of DM and 23.7 ± 2.3 kg of milk per cow.

Urine samples were daily collected during the last week of treatment over 5 consecutive days. They were collected separately in 2 different ways. Total urine samples were collected during 24-h periods using caps attached to the vulva and connected to a 30-L collection carboy via a flexible tube. The carboy contained 500 mL of 10% (vol/vol) sulfuric acid as preservative. Every day before the morning feeding, a 100-mL aliquot was taken and the carboy was replaced with a clean one. Spot urine samples were taken between 4 and 6 h after the morning feeding (1300 to 1500 h). The perineal region was wiped with a clean paper towel to remove fecal residues, and urination was stimulated by massaging the vulva. After urination started, the first milliliters were discarded and urine was collected into a clean beaker without the use of any preservative. Out of 80 samples planned for each sampling technique, we collected 71 for TC (9 samples excluded due to fecal contamination) and 74 for SC (6 samples, obtained from cows in heat, were discarded as urine did not have the usual aspect). Samples from both types of collection were immediately centrifuged at 10,000 × g at 4°C for 10 min, filtered through 0.45-µm single-use syringe filter (Sartorius) to remove debris, aliquoted, and stored at -80°C until analysis. Storage time was 17 mo.

Metabolite Analysis

For a wide coverage of the metabolome and to deal with the highly diverse range of metabolites present in urine, different analytical techniques for both untargeted and targeted analysis are used [START_REF] Schrimpe-Rutledge | Untargeted metabolomics strategies -Challenges and emerging directions[END_REF]. We used 3 analytical platforms as follows: 2 complementary untargeted nuclear magnetic resonance (NMR) and hydrophilic-interaction liquid chromatography (HILIC) coupled to a time-of-flight mass spectrometer (HILIC-ToF/MS), and by 1 targeted method HILIC-tandem mass spectrometry (HILIC-MS/MS). For untargeted methods, daily collected urine samples (n = 71 for TC, n = 74 for SC) were analyzed. For the targeted method, we analyzed pooled samples per animal that were obtained by combining an equal volume of urine collected in each period and for each sampling technique (n = 16 × 2).

Metabolite profiling using a discovery-based, untargeted approach of global detection of all metabolites present in urine was performed on a Metabolic Profiler system that combined 2 techniques of detection, MS and NMR. The Metabolic Profiler consisted of an AVANCE III 500MHz NMR spectrometer equipped with a 3-mm FISEI z-gradient ( 1 H-13 C) probe and a 60-µL flow cell (Bruker Biospin GmbH) and a liquid chromatography coupled (LC) apparatus (Agilent 1100 HPLC system) coupled to a MicrOTOF (time-of-flight) mass spectrometer (Bruker Daltonics).

HILIC-ToF/MS Analysis. Urine samples (100 µL) were thawed at room temperature, and a 100-µL aliquot was transferred into a clean Eppendorf tube and mixed with 300 µL of acetonitrile. The samples were vortex-mixed and centrifuged at 10,000 × g for 10 min. Then, supernatants were transferred into an autosampler, and an aliquot of 10 µL was injected into the LC-MS system. To ensure performance of analytical instrumentation and that the data were of comparable high quality within and between series, quality control samples (QC) were inserted in duplicate every 5 urine samples. Two types of QC were prepared by pooling samples from SC and TC collections. Each pooled QC was obtained by mixing equal volume of urine from samples of all cows over the 4 experimental periods. These QC samples were processed as above. Separation was performed on a 150 × 2 mm i.d. HILIC column (Phenomenex) fitted with a same type 2 × 2 mm guard column, and using a water/acetonitrile (both containing 5 mM ammonium formate, pH 6.8) gradient at a flow rate of 0.3 mL/min. The gradient was started at 5% water, held for 2 min, and increased to 50% in 12 min; 50% water was maintained for 2 min before the gradient was returned to initial conditions and maintained for 7 min for re-equilibrating the column prior the next injection. The MS system was operated in positive ionization mode with a scan range of 50 to 800 m/z. The capillary was set to -4.5 kV, the nebulizer was operated at 2 bars, and the dry gas was set to 8 L/min at a temperature of 200°C. The capillary exit was set to 90 V with skimmer 1 set to 30 V. The time-of-flight spectrometer was calibrated by using lithium formate (ions at m/z 90 and 800). The MS source was regularly cleaned after 100 injections. For accurate mass acquisition, a formate-acetate solution was infused during the run at a flow rate 100 µL/min monitoring for positive ion mode.

1-Dimensional NMR Analysis. Urine samples were centrifuged at 10,000 × g for 10 min at 4°C. To 200 µL of supernatant of each sample, the robot of the Metabolic Profiler facility added 30 µL of a phosphate buffer (KH 2 PO 4 and K 2 HPO 4 , Fluka; pH 7.06) in Deuterium oxide (99.96% minimum, Eurisotop), containing 4.6 mM 3-(trimethylsilyl) propionic-2,2,3,3-tetra-d4 acid sodium salt (TSP-d4; 98% deuterated, Sigma-Aldrich), and 6.1 mM sodium azide (NaN3, 99.99% minimum, Sigma-Aldrich). The 1 H NMR experiments were carried out on a Bruker Metabolic Profiler equipped with an AVANCE III 500 MHz NMR spectrometer using a 3-mm FISEI z-gradient ( 1 H-13 C) probe with a 60-µL flow cell (Bruker Biospin). Cell probe was filled with sample using Gilson 215 robot incorporated into BEST (Bruker Efficient Sample Transfer) system. A standard monodimensional nuclear Overhauser effect spectroscopy sequence (noesygppr1d with water presaturation and gradients) was used with low power irradiation of the water resonance during the recycle delay of 4 s and the mixing time of 10 ms; 128 scans were collected with a 90° impulsion time of 9.3 µs, an acquisition time of 3.2 s, and a spectral window of 10,000 Hz; and 64K data points zero-filled to 128K before Fourier transformation with 0.3 Hz line broadening. All NMR spectra were recorded at 300 K. The robot maintained samples in well plates at 4°C during analyses.

HILIC-MS/MS Analysis. We used a targeted approach to do the absolute quantification of metabolites related to nitrogen status. Urine samples from the 2 sampling techniques were analyzed as previously described [START_REF] Boudra | Simultaneous analysis of the main markers of nitrogen status in dairy cow's urine using hydrophilic interaction chromatography and tandem mass spectrometry detection[END_REF]. Briefly, urine samples collected from each cow over 5 consecutive days were pooled. The mixture (100 µL) was diluted with acetonitrile, vortex-mixed and centrifuged, and 10 µL were injected into the LC-MS/MS system.

Data Processing

The MS data were converted to NetCDF format using 3.4 Datanalysis software (Bruker Biospin), and MS data from untargeted approach were processed using the open-source web-based interface Galaxy instance Workflow4Metabolomics running under R3.5.2 [START_REF] Giacomoni | Workflow4Metabolomics: A collaborative research infrastructure for computational metabolomics[END_REF]. Further pre-processing steps were also applied to remove noise and unwanted variation, including filtrations and normalization. The MS data were first filtered on retention time, and signals outside the range (lower than 0.4 min or higher than 22 min) were considered as noise and removed. Background noise was also removed by removing masses found in blank samples (solvents). Because we had 2 types of QC (1 per type of collection), MS data of each collection were then normalized within batch using a linear regression model (van der [START_REF] Van Der Kloet | Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping[END_REF]. A data matrix containing mass and retention time with associated signal intensities for all detected peaks was generated. An additional filtering test was used to select variables based on univariate analysis (Mann-Whitney-Wilcoxon test on N levels); the ions with P < 0.3 were retained for further multivariate analysis. After denoising and filtering tests, the number of features was reduced from 4,662 to 2,177 (47%).

The NMR data processing was performed in 2 steps. First, zero-and first-order phase correction was achieved manually on all spectra. Baseline correction was performed using a third-order polynomial. Chemical shifts in all spectra were referenced to the TSP-d4 signal at chemical shift (δ = 0 ppm). Spectra were treated with TopSpin version 2.1. Then, bucketing was performed using AMIX 3.9.10. The NMR spectra were subdivided in narrow spectral regions (0.02-ppm wide simple rectangular buckets). The spectral region from 10.50 to 0.50 ppm (spot urine) and 9.14 to 0.65 ppm (total urine) were used for bucketing. Spectral regions that either did not contain any resonances for SC samples (10.00-9.36 ppm and 0.65-0.50 ppm) or contained the residual water resonance (5.50-4.72 ppm for all samples; for TC samples: 5.60-4.72 ppm) were excluded from the bucketing procedure. Due to the chemical exchange with water, the urea peak is influenced by water pre-saturation, which requires the elimination of this peak [START_REF] Beckonert | Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts[END_REF]. Unfortunately, this excluded the region containing proton chemical shifts of allantoin and saccharides anomeric protons. As for MS data, a table was generated containing the buckets, their chemical shift, and the intensity.

Metabolite Annotation

Two-dimensional (2-D) NMR data were acquired on a Bruker AVANCE I spectrometer operating at 500 MHz and equipped with a 5-mm inverse-triple tuned (TXI) 1H/13C/15N with z-gradient coil probe (Bruker Biospin Wissenbourg). For 1-dimensional (1-D) 1 Hspectra, a standard 1-D spectroscopy sequence (zgpr) was used with low power irradiation of the water resonance during the recycle delay of 5 s. Then, 128 scans were collected with a 90° impulsion time of 10 µs, an acquisition time of 3.2 s, and a spectral window of 10.k Hz, and 64 K data points were zero-filled to 128 K before Fourier transformation with 0.3-Hz line broadening. Two-dimensional homonuclear (1H-1H COSY, 1H-1H TOCSY, 1H-1H Jres) and heteronuclear (1H-13C HSQC and HMBC) experiments were performed with quadrature phase detection in dimensions, using state-TPPI (time proportional phase incrementation) or QF (quadrature fixed) detection mode in the indirect experiment. For each of 512 increments in the indirect dimension, 2 K data points were collected, and 16 or 32 transients were accumulated in the direct dimension. The 13C decoupling (GARP) was performed during acquisition time for heteronuclear experiments. A π/2 shifted square sine-bell function was applied in the both dimensions before Fourier transformation. All spectra were recorded at 300 K and treated with TopSpin version 3.1 software.

For untargeted MS data, mass spectra were first manually annotated for each discriminant ion to identify the parent ion. The redundant m/z peaks corresponding to different ions (fragments and adducts) were used to help confirm the molecular ion, and then removed. For identification, an in-house database containing more than 1,000 metabolites analyzed under the same conditions was first queried based on both retention time and mass obtained from the time-of-flight-MS detector. This identification was based on both their retention time and mass. For the rest of unidentified metabolites, 4 online chemical databases were queried by accurate mass to search for hypotheses of chemical structures as follows: the Human Metabolome Database (HMDB, www .hmdb .ca), Kyoto Encyclopedia of Genes and Genomes (KEGG, http: / / www .genome .jp/ kegg/ ), Metlin (http: / / metlin .scripps .edu/ ), and the Bovine Metabolome Database (http: / / www .rumendb .ca/ ). To obtain exact masses, pooled urine samples were analyzed using a high resolution Thermo Scientific LTQ Orbitrap Velos hybrid mass spectrometer (Thermo) using the same LC conditions as the LC-MS acquisitions.

The NMR metabolites identification based on the 1-D and 2-D NMR spectra were performed by querying an in-house database, public databases HMDB [START_REF] Wishart | HMDB 4.0: the human metabolome database for 2018[END_REF], Biological Magnetic Resonance Data Bank [START_REF] Ulrich | BioMagResBank[END_REF], and relevant publications [START_REF] Fan | Metabolite profiling by one-and two-dimensional NMR analysis of complex mixtures[END_REF][START_REF] Beckonert | Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts[END_REF][START_REF] Bertram | Effect of dietary nitrogen content on the urine metabolite profile of dairy cows assessed by nuclear magnetic resonance (NMR)-based metabolomics[END_REF].

Eleven urinary metabolites that contributed to the separation of diets differing in N level were identified. They were confirmed by NMR 2-D in urine spiked by pure standard. The 2-D experiments were performed on pooled urine samples that were prepared as follows. In the initial step, 5 QC samples (see in the HILIC-MS experiments section the description of their preparation) were pooled together in an Eppendorf and centrifuged at 10,000 × g at 4°C for 30 min on a tabletop centrifuge (Sigma 112). The supernatant was transferred into a 15-mL polypropylene tube and lyophilized. The lyophilized sample was then dissolved into 1.4 mL of a 39 mM potassium phosphate buffer (pD* ~7.1; pH measured in deuterium oxide solvent) prepared in 100% Deuterium oxide containing 0.6 mM NaN 3 and 0.8 mM TSP-d4. Dilution of this pooled QC sample was necessary to facilitate tuning and matching of the NMR probe owing to the high salt content of the sample.

Calculations and Statistical Analyses

Milk N-use efficiency was calculated as the average daily milk N secretion divided by the average daily N intake over 6 consecutive days. This overall milk N-use efficiency was then partitioned into ruminal, digestive, and metabolic efficiencies as previously described [START_REF] Cantalapiedra-Hijar | Nitrogen isotopic fractionation as a biomarker for nitrogen use efficiency in ruminants: A meta-analysis[END_REF]. Briefly, rumen N-use efficiency was calculated as the microbial protein synthesis divided by rumen degradable protein. Digestive N-use efficiency was computed as the metabolizable protein supply divided by the dietary CP intake, whereas metabolic N-use efficiency was calculated as the milk N secretion divided by the metabolizable protein supply. All variables used in these calculations were measured and not estimated [START_REF] Fanchone | Effects of nitrogen underfeeding and energy source on nitrogen ruminal metabolism, digestion, and nitrogen partitioning in dairy cows[END_REF].

Combined univariate and multivariate analyses were performed to compare urine metabolites in both sampling techniques. Before statistical analyses, both NMR and MS data were processed and subsequently normalized to the total intensity to avoid bias due to normal variations in concentration between cows and due to the collection method. Data were then subjected to univariate scaling and analyzed using SIMCA-P software (Umetrics, v. 13.01). Unsupervised principal component analysis (PCA) was first used to visualize trends in metabolite profiles between TC and SC, followed by supervised orthogonal partial least squares (OPLS)-discriminant analysis (DA) to discriminate N diets. Validation of the OPLS models were performed by considering samples from each day collection independently. Discriminant variables were selected according to the variable's importance in projection (VIP; VIP ≥ 1.0). These discriminant variables were then subjected to annotation (Supplemental Methods; https: / / data .inrae .fr/ dataset .xhtml ?persistentId = doi: 10 .15454/ FFFRL2, [START_REF] Morgavi | Supporting data for "Spot urine collection: A valid alternative to total urine collection for metabolomic studies in dairy cattle[END_REF]. The characteristics of the OPLS models were assessed by the following: proportion of the dependent variables that is explained by the model (R 2 X), cumulative predictive ability parameter (Q 2 ), and the number of VIP ≥1, indicating the contribution of variables to the discrimination between N diets. All OPLS models were tested for overfitting with a 200-time permutation analysis [see Worley andPowers (2013, 2016) for further details and examples of the statistical analyses].

Further univariate analyses (2-way ANOVA, cow, and N diets) were performed to assess the variability of the 9 discriminant metabolites of N diets identified by the untargeted MS method. Data from targeted methods were normalized by creatinine before univariate analyses (2-way ANOVA, N diets, and sampling techniques with interaction) using XlStat-Biomed (v. 18.1.1, Addinsoft).

RESULTS AND DISCUSSION

Sampling Techniques Markedly Influenced Urinary Metabolite Profiles

To compare the number of MS features extracted in each sampling technique, we processed the MS data from TC and SC separately. We obtained similar number of unfiltered features, 3,582 for TC and 3,506 for SC, with 88% of them common to both sampling techniques (Figure 1). Notwithstanding, when TC and SC data sets were jointly processed to obtain a quantitative comparison of shared features, there were differences between the 2 sampling techniques. After filtration and variable selection, the number of variables was reduced from 4,662 unfiltered features to 2,177 filtered features with more than half (n = 1,331) of these features being significantly different between sampling techniques (paired parametric test, Benjamini-Hochberg false discovery rate corrected, P < 0.05; data not shown). Multivariate analyses showed clear separation between metabolite profiles obtained in both sampling techniques (Figure 2). For MS data, separation was observed between individual samples as well as the pooled QC of the 2 sampling techniques. A tight clustering of both QC samples in MS data indicated that the analytical conditions were stable over the time of measurements, and that the HILIC-MS system provided reproducible results. This variation in metabolite profiles could be explained by the difference in the concentration of metabolites between TC and SC as exemplified in the present study for allantoin and uric acid (Table 1) and also reported by others [START_REF] Boudra | Simultaneous analysis of the main markers of nitrogen status in dairy cow's urine using hydrophilic interaction chromatography and tandem mass spectrometry detection[END_REF][START_REF] Lee | Validating and optimizing spot sampling of urine to estimate urine output with creatinine as a marker in dairy cows[END_REF]. Because TC and SC samplings reflected different time spans (i.e., average excretion throughout the day vs. 4 h postprandial), the proportion of feed and endogenous metabolites may have differed between both conditions. In addition, it is likely that TC samples also contained modified metabolites induced by sulfuric acid.

Both Sampling Techniques Equally Discriminated Dietary Groups

Despite the difference in metabolite profiles (Figure 2), both sampling techniques were able to discriminate diets in terms of N content when samples were analyzed by HILIC-ToF/MS and 1 H NMR. The PCA models built from MS data showed better separation between diets in terms of N content than NMR in both sampling techniques (Figure 3). It is also noted that discriminant metabolites were almost the same between sampling techniques (see below) and that the same metabolic pathways were affected by diet independently of the data set analyzed (i.e., SC or TC; MetaboAnalyst pathway analysis, data not shown; http: / / www .metaboanalyst .ca).

Several studies reported variability throughout the day in the metabolic profile of urine obtained by SC compared with the average profile obtained from TC samples [START_REF] Lee | Validating and optimizing spot sampling of urine to estimate urine output with creatinine as a marker in dairy cows[END_REF]. As stated in the previous section, there were differences due to time spans considered in the collection that were mainly caused by feeding. This difference was also compounded by daily individual differences in feeding behavior, nutrient absorption, and use rate as well as by the time when the animal last urinated. To overcome this drawback, urine outputs from SC were normalized with creatinine as recommended [START_REF] Shingfield | Evaluation of the spot urine sampling technique to assess urinary purine derivative excretion in lactating dairy cows[END_REF][START_REF] Tebbe | Evaluation of creatinine as a urine marker and factors affecting urinary excretion of magnesium by dairy cows[END_REF]. Another normalization method is based on osmolarity [START_REF] Jacob | Evaluation of specific gravity as normalization strategy for cattle urinary metabolome analysis[END_REF]; whereas other authors recommend repeated sampling throughout 1 or more days [START_REF] Lee | Validating and optimizing spot sampling of urine to estimate urine output with creatinine as a marker in dairy cows[END_REF][START_REF] Wilson | Spot and cumulative urine samples are suitable replacements for 24-hour urine collections for objective measures of dietary exposure in adults using metabolite biomarkers[END_REF]. Also, we investigated whether sampling over several days improved the robustness and precision of the SC models. To do that, we compared the quality parameters of PCA and OPLS-DA models of TC built with data obtained in the HILIC-ToF/MS analytical platform from 1 d (TC-d1) with those built from 1 d of SC samples (SC-d1) or the average data of 2, 3, 4, or 5 SC samples taken once a day. Both sampling techniques were able to differentiate diets when using PCA models built with data of d 1. The separation of N diet levels was obtained in the first component, whereas separation of energy diets was obtained in the second component. Discrimination of N diets was achieved with 26.5% and 20.2% of variance explained on component 1 for TC-d1 and SC-d1, respectively (Figure 4, Table 2). A stronger separation [R 2 X(1) = 24%] between N diet levels was observed when combining data from the 2 first collection days. This value increased marginally when data from more days were averaged, up to 28% when 5 d were averaged. The OPLS-DA models showed high value of the predictive parameter (Q 2 ) for all the models with a slight increase from 2-d-average. Mean values within a column and within the same sampling technique with different superscript letters differ (P < 0.05).

1 HN = high nitrogen (14% CP); LN = low nitrogen (11% CP).

2 β aminobutyric acid.

Although good discrimination is obtained at d 1 with a Q 2 value similar to that of TC (SC-1d = 0.81; TC-1d = 0.73), we observed a better discriminative power from 2-d-average (SC-2d = 0.89) that remained stable over 5 d of collection (Table 2). The number of VIP ≥1, indicating the contribution of variables to the discrimination between N diets, was similar among models, with the exception of model built with data from d 1 that is smaller. These observations suggested that multiple-day collection improved the model quality, and that 2 consecutive days seemed enough to achieve stable results. This hypothesis is consistent with the Venn diagram (Supplemental Figure S1, https: / / data .inrae .fr/ dataset .xhtml ?persistentId = doi: 10 .15454/ FFFRL2, Morgavi, 2021) obtained by comparing VIP lists extracted from each OPLS-DA model (VIP ≥ 1). Indeed, when looking at the proportion of variables specific to each model, we observed that it was higher in the SC-1-d model and decreased with the successive addition of collection days except for SC averaged over To highlight discriminant metabolites associated with differences in diets in terms of N content, OPLS-DA models were built from samples collected over 5 d from both sampling techniques. We focused on the possible differences between high and low CP diets because of the importance that dietary N content has on animal production and N pollution [START_REF] Castillo | A review of efficiency of nitrogen utilisation in lactating dairy cows and its relationship with environmental pollution[END_REF]. However, because the nature of the energy supply also had a significant effect on the urine metabolome as showed in Figure 4, the OPLS models were performed separately for each energy source (starch and fiber). Within each energy source, a clear separation of diets differing in N content was obtained with both sampling techniques with a good predictive capacity (Q 2 ) for MS and NMR data (Supplemental Figure S2, https: / / data .inrae .fr/ dataset .xhtml ?persistentId = doi: 10 .15454/ FFFRL2, [START_REF] Morgavi | Supporting data for "Spot urine collection: A valid alternative to total urine collection for metabolomic studies in dairy cattle[END_REF]. Twenty metabolites discriminating N diets were identified as follows: 11 by NMR (Supplemental Table S2, https: / / data .inrae .fr/ dataset .xhtml ?persistentId = doi: 10 .15454/ FFFRL2, [START_REF] Morgavi | Supporting data for "Spot urine collection: A valid alternative to total urine collection for metabolomic studies in dairy cattle[END_REF] and confirmed by NMR 2-D in urine spiked with pure standards (Supplemental Figure S3, https: / / data .inrae .fr/ dataset .xhtml ?persistentId = doi: 10 .15454/ FFFRL2, [START_REF] Morgavi | Supporting data for "Spot urine collection: A valid alternative to total urine collection for metabolomic studies in dairy cattle[END_REF], and 9 by HILIC-ToF/MS (Supplemental Table S3, https: / / data .inrae .fr/ dataset .xhtml ?persistentId = doi: 10 .15454/ FFFRL2, [START_REF] Morgavi | Supporting data for "Spot urine collection: A valid alternative to total urine collection for metabolomic studies in dairy cattle[END_REF]. These discriminant metabolites were common to both sampling techniques, and followed the same pattern, (i.e., increase or decrease). Notwithstanding, in agreement with [START_REF] Lee | Validating and optimizing spot sampling of urine to estimate urine output with creatinine as a marker in dairy cows[END_REF], higher variability between cows was observed for some metabolites detected by MS in SC compared with TC samples (Supplemental Figure S4, https: / / data .inrae .fr/ dataset .xhtml ?persistentId = doi: 10 .15454/ FFFRL2, [START_REF] Morgavi | Supporting data for "Spot urine collection: A valid alternative to total urine collection for metabolomic studies in dairy cattle[END_REF].

We also compared both sampling techniques using a quantitative HILIC-MS/MS method targeting 9 markers of nitrogen status as follows: allantoin, uric acid, xanthine, hypoxanthine, β-aminobutyric acid, β-alanine, creatinine, creatine, and urea [START_REF] Boudra | Simultaneous analysis of the main markers of nitrogen status in dairy cow's urine using hydrophilic interaction chromatography and tandem mass spectrometry detection[END_REF]. Results showed no difference (P > 0.05) of the sampling technique in the concentration of these metabolites, except for allantoin and uric acid that were significantly (P < 0.05) higher in TC (Figure 5). These differences could be explained in particular by diurnal variation in purine derivative urinary excretion [START_REF] Tas | Urinary purine derivates excretion as an indicator of in vivo microbial N flow in cattle: A review[END_REF]. When considering N level, all metabolites followed similar patterns in both sampling techniques, and creatine and urea were significantly different (P < 0.05) between N diets (Table 1). Moreover, the interaction (diet × sampling technique) in our study was not significant, indicat-ing that detection of the diet effect was similar for both sampling techniques. Other studies reported no differences between TC and SC for estimating daily purine derivatives excretion in ruminants when urinary output was estimated from creatinine concentration in SC samples [START_REF] Valadares | Effect of replacing alfalfa silage with high moisture corn on ruminal protein synthesis estimated from excretion of total purine derivatives[END_REF][START_REF] Chizzotti | Determination of creatinine excretion and evaluation of spot urine sampling in Holstein cattle[END_REF]. Nonetheless, urine creatinine concentration does vary throughout the day [START_REF] Lee | Validating and optimizing spot sampling of urine to estimate urine output with creatinine as a marker in dairy cows[END_REF] and should be considered depending on the objective of the study as discussed below. Similar to untargeted results, quantification of all metabolites in individual cows followed similar pattern in both collections (Supplemental Figure S5, https: / / data .inrae .fr/ dataset .xhtml ?persistentId = doi: 10 .15454/ FFFRL2, Morgavi, 2021). Supporting these results, linear regression analyses showed a strong correlation between sampling techniques for metabolites analyzed by the targeted method [START_REF] Boudra | Simultaneous analysis of the main markers of nitrogen status in dairy cow's urine using hydrophilic interaction chromatography and tandem mass spectrometry detection[END_REF]. Human clinical studies showed that single urine SC samples collected in the morning and analyzed by the untargeted NMR method allowed the discrimination between healthy and sick groups [START_REF] Saude | Metabolomic profiling of asthma: Diagnostic utility of urine nuclear magnetic resonance spectroscopy[END_REF]. Alternatively, [START_REF] Wilson | Spot and cumulative urine samples are suitable replacements for 24-hour urine collections for objective measures of dietary exposure in adults using metabolite biomarkers[END_REF] showed that there was no significant loss of information when TC was substituted for SC in human studies. To our knowledge, no such studies were performed in ruminants. It is acknowledged, however, that metabolomics studies require a high number of samples, and these results should be validated using larger groups of animals. Also, a single urine SC as used in this work cannot capture variations throughout the day or be used for the 24-h quantitative estimation of metabolites excretion. A total of 22 metabolites discriminating the dietary N level were identified from the 3 analytical platforms as follows: 20 metabolites from untargeted methods (Supplemental Tables S2 andS3) and 2 metabolites from the targeted method (Table 1). Three common metabolites were found in at least 2 analytical platforms (p-cresol sulfate, phenyl-acetylglycine, and urea). P-cresol is a rumen metabolite of tyrosine, and approximately half of the tyrosine content of rumen-administered casein was excreted as p-cresol in sheep [START_REF] Martin | The origin of urinary aromatic compounds excreted by ruminants. 3. The metabolism of phenolic compounds to simple phenols[END_REF]. Interestingly, p-cresol and phenyl-acetylglycine are metabolically related. Indeed, the aromatic AA, phenylalanine and tyrosine, are first converted into phenyl acetate and p-cresol through the action of the gut microbiota [START_REF] Smith | Enumeration of human colonic bacteria producing phenolic and indolic compounds: Effects of pH, carbohydrate availability and retention time on dissimilatory aromatic amino acid metabolism[END_REF] and then conjugated with glycine and glucuronide, respectively, to form phenyl-acetylglycine and p-cresol glucuronide in the liver and gut mucosa [START_REF] De Pascali | 1)H NMR and MVA metabolomic profiles of urines from piglets fed with boluses contaminated with a mixture of five mycotoxins[END_REF]. This suggests that urinary p-cresol and phenyl-acetylglycine concentration may reflect the intake of dietary phenylalanine and tyrosine in ruminants, and thus be a proxy of the overall N intake. Concerning the urinary urea concentration as a biomarker of N intake, it is well established that the proportion of urea in urine increases as the N intake increases [START_REF] Gonda | Evaluation of dietary nitrogen utilization in dairy cows based on urea concentrations in blood, urine and milk, and on urinary concentration of purine derivatives[END_REF], and urea concentration in blood or milk have been proposed as a biomarker of the N-use efficiency and N intake in ruminants [START_REF] Kohn | Using blood urea nitrogen to predict nitrogen excretion and efficiency of nitrogen utilization in cattle, sheep, goats, horses, pigs, and rats[END_REF][START_REF] Huhtanen | Evaluation of between-cow variation in milk urea and rumen ammonia nitrogen concentrations and the association with nitrogen utilization and diet digestibility in lactating cows[END_REF].

Further regression models based on these 22 discriminant metabolites were constructed to investigate the relationships between these metabolites and the N-use efficiency at the digestive, metabolic, and rumen levels. There was no relationship except for the overall milk N-use efficiency (Supplemental Figure S6, ttps: / / data .inrae .fr/ dataset .xhtml ?persistentId = doi: 10 .15454/ FFFRL2, [START_REF] Morgavi | Supporting data for "Spot urine collection: A valid alternative to total urine collection for metabolomic studies in dairy cattle[END_REF], likely due to the inherent errors associated with digestive and metabolic measurements compared with the milk N-use efficiency.

This work showed that SC is as good as TC for obtaining robust metabolomics results in lactating cows. When applied to assess the effect of dietary N level, urine samples obtained from SC effectively discriminated diets with high or low N levels. Spot urine collection also avoids the practical limitations and animal welfare issues involved in TC, making it highly recommendable for metabolomics studies in lactating cows.

ACKNOWLEDGMENTS

This work was funded by the seventh EU framework project "Innovative and practical management ap-

Figure 1 .

 1 Figure 1. Venn diagram of detected features in untargeted method (hydrophilic-interaction liquid chromatography-time-of-flight-MS) from dairy cow urine collected during 24 h (total collection) or by spot collection and processed separately.

Figure 2 .

 2 Figure 2. Score plots of principal component analysis of untargeted MS (A) and nuclear magnetic resonance (B) urine metabolites from dairy cows showing grouping of samples depending on the urine collection technique (total 24 h or spot). Red labels in A designate quality control samples. A tight clustering of quality control samples of both sampling techniques indicates that the analytical conditions were stable over the time of measurements. R 2 X[1] and R 2 X[2] represent the explained variance in component 1 (T1) and 2 (T2), respectively.

Figure 3 .

 3 Figure 3. Scores plot of principal component analysis of untargeted MS (A) and nuclear magnetic resonance (B) urine metabolites from total (circle) and spot (triangle) collections from dairy cows fed the following 4 diets: high (red labels) or low (black labels) in nitrogen with starch (full label) or fiber (empty label) as energy source. Samples were collected over 5 d. R 2 X[1] and R 2 X[2] represent the explained variance in component 1 (T1) and 2 (T2), respectively.

  5 d. Again, these results agree with the suggestion that collection during 2 d (2-d average) would be necessary and potentially sufficient.

Figure 4 .

 4 Figure 4. Comparison of principal component analysis score plots of hydrophilic-interaction liquid chromatography-time-of-flight-MS urine data obtained from total collection at 1 d (TC-d1, circle) versus spot collection (triangle) built with data from 1 d (SC-d1) and the mean data of 2, 3, 4, and 5 d (SC-d1+2 to d1+2+3+4+5). Urine samples were obtained from dairy cows fed diets high (red labels) or low (black labels) in nitrogen with starch (full labels) and fiber (empty labels) as energy source. Nitrogen level in the diet was discriminant in the first component, whereas starch level was discriminant in the second component. R 2 X[1] and R 2 X[2] represent the explained variance in component 1 (T1) and 2 (T2), respectively.

3

  OPLS-DA = orthogonal partial least squares-discriminant analysis; PC = principal component; R 2 (X) = variance explained by the 2 components; Q 2 = predictive parameter of the model; VIP = ratio of specific variable importance in projection (VIP)/total VIP in the model.

Figure 5 .

 5 Figure5. The y-axis represents the concentration of 7 urine metabolites quantified by hydrophilic-interaction liquid chromatography-tandem MS from total (red) and spot (black) urine samples from lactating dairy cows. The concentration of allantoin and uric acid differed significantly (P < 0.05) between sampling collection technique. For each box, the central horizontal bar is the median; the lower and upper limits of the box are the first and third quartiles, respectively; and points above or below the maximum and minimum value may be considered as outliers.

Table 1 .

 1 Concentration of metabolites measured by targeted method (hydrophilic-interaction liquid chromatography-MS/MS) in spot and total urine samples from lactating dairy cows fed 2 dietary nitrogen levels

					Concentration (µmol/mmol creatinine per L)
	Item	Diet 1	Allantoin	Uric acid	Urea	Creatine	β ABA 2	β alanine	Glycine
	Total urine collection Spot urine collection	HN LN HN LN	8,343.0 9,106.6 5,990.6 7,397.4	1,068.3 1,010.4 725.8 786.1	57,552.2 a 20,734.9 b 58,898.3 a 22,975.3 b	2,115.6 a 1,575.8 b 2,024.4 a 1,511.0 b	3.6 3.8 4.2 4.0	8.7 5.9 7.5 5.0	218.3 170.5 476.6 249.6
	Diet effect		0.113	0.987	<0.001	0.005	0.925	0.062	0.109
	Sampling effect		0.005	0.001	0.733	0.657	0.574	0.461	0.052
	Interaction		0.632	0.419	0.932	0.940	0.774	0.908	0.289
	a,b

Table 2 .

 2 Boudra et al.: SPOT URINE COLLECTION IN METABOLOMICS Comparative ability of total and spot urine collection methods to detect differences in the metabolome obtained by untargeted method (hydrophilic-interaction liquid chromatography-time-of-flight-MS) of lactating dairy cows induced by diet

			Quality of multivariate model
			PCA 2			OPLS-DA 3
		Discrimination on C1			Model performance
	Data 1	All diets 4	N diets	R 2 (X)	PC	R 2 (X)	Q 2	VIP
	TC 1 d	Partial	Partial	26.5	1+0+0	0.20	0.73
	SC 1 d	Partial	Partial	20.2	1+1+0	0.29	0.81	0.05
	SC 2-d mean	Yes	Yes	24.1	1+1+0	0.29	0.89	0.03
	SC 3-d mean	Yes	Yes	26.0	1+0+0	0.26	0.88	0.01
	SC 4-d mean	Yes	Yes	26.7	1+1+0	0.36	0.88	0.01
	SC 5-d mean	Yes	Yes	28.1	1+0+0	0.28	0.89	0.03
	1 Mass spectrometry data obtained from total (TC) and spot collection (SC) samples obtained from one day (1 d) or averaged over 2 to 5 consecutive days of sampling for SC. 2 PCA = principal component analysis; C1
	= component 1.

Boudra et al.: SPOT URINE COLLECTION IN METABOLOMICS Journal of Dairy Science Vol. 105 No. 1, 2022

Four diets containing 14% or 11% CP with starch or fiber as energy source.

proaches to reduce nitrogen excretion by ruminants (REDNEX)," FP7, KBB-2007-1. We thank David Alvarez and Ulli Hohenester (UMRH, INRAE) for their technical assistance. We also thank the personnel of the experimental animal facilities for the care of animals. The authors have not stated any conflicts of interest.