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Enregistrement W2177516223 · doi:10.1093/ajh/hpv076

Measuring Sodium Intake in Populations: Simple Is Best?

2015· letter· en· W2177516223 sur OpenAlexaff
Andrew Mente, Martin O’Donnell, Salim Yusuf

Notice bibliographique

RevueAmerican Journal of Hypertension · 2015
Typeletter
Langueen
DomaineNursing
ThématiqueSodium Intake and Health
Établissements canadiensHamilton Health SciencesMcMaster UniversityPopulation Health Research Institute
Organismes subventionnairesnon disponible
Mots-clésMedicineSimple (philosophy)SodiumInternal medicine

Résumé

récupéré en direct d'OpenAlex

Dietary sodium has long been proposed as a target for population-level interventions to reduce cardiovascular disease (CVD), given its association with blood pressure (BP).1 However, recent evidence from epidemiologic studies shows that low sodium intake (in the range recommended by current guidelines) compared to moderate (average) intake is not associated with lower risk of CVD events and mortality and may even be associated with an increased risk.2–4 In 2013, an Institute of Medicine (IOM) expert panel concluded that the optimum level of sodium intake to reduce CVD is uncertain and called for large epidemiologic studies and randomized controlled trials of sodium intake and clinical events.5 With research on sodium intake undergoing a shift from evaluating its short-term effects on surrogate markers to assess long-term clinical outcomes, there has been growing interest in developing efficient approaches to measuring sodium intake suitable for use in large studies.6–9 In this issue of the journal, Han et al.10 have reported that the Kawasaki formula is valid for estimating population mean levels of sodium excretion from a morning fasting urine. They showed that sodium excretion from a morning urine (collected before breakfast) showed the least bias (2.1 mmol/day) and most agreement with 24-hour measurements of urinary sodium excretion (intraclass correlation coefficient [ICC] = 0.64). These findings are consistent with previous data from our group, based on a study of 1,083 individuals from the general population in 11 countries which also reported good correlation (ICC = 0.71) and the least bias (8.8 mmol/day) with the Kawasaki formula (compared to other formulae).11 Conversely, for the Tanaka formula, Han et al.10 found greater bias (21.1–30.1 mmol/day) and less agreement (ICC of 0.26–0.38) for either morning fasting urine or late-afternoon/early evening predinner samples, which is in keeping with findings from our study11 and two other studies in Japanese cohorts.12,13 These findings highlight that the Kawasaki formula6 on a morning fasting urinary collection is a good estimate of actual 24-hour urinary excretion of sodium, which is the reference standard for estimating sodium intake. Large studies reduce random error, so the averaged estimates across a large number of people will provide reliable estimates of average sodium intake at a group level, although there may be some variability in estimates for single individuals (e.g., determining adherence to guidelines in an individual).14 The findings of the above two validation studies have important implications for surveys and epidemiologic studies of sodium intake. To describe the association of sodium to health outcomes or to obtain estimates of sodium intake in different populations, large studies are needed, e.g., the Prospective Urban Rural Epidemiology (PURE) study involving >100,000 healthy people from the general population.4,15 Such studies include representative populations, and accrue a few thousand deaths or CVD events within a relatively short time. Similarly monitoring sodium intake in populations over time and comparing intakes between populations require simple methods that are valid in order to study large numbers of people. For instance, in the Prospective Urban Rural Epidemiology (PURE) study, the range of mean sodium excretion (determined using the Kawasaki method) across the 17 different countries was consistent with the findings of a recent meta-analysis of cross-sectional studies from 187 countries, with most populations of the world consuming between 3 and 6g of sodium per day.16 Lastly, both repeated 24-hour urine measurements or estimates of sodium intake from multiple morning urines may be used to more precisely estimate “usual” sodium intake in groups of people during follow-up in randomized controlled trials. The latter method has the advantage of better adherence (fewer refusals or incomplete collections) and can be done more often than 24-hour urine collections—all of which can lead to more accurate assessments of sodium intake. A single measurement of urinary sodium by itself (whether this is a spot measure, a fasting measure, or a 24-hour urine) does not provide as good a measure of usual intake as multiple measures, because sodium intake varies from day-to-day, making repeated measurements desirable to estimate “usual” sodium intake. However, single measurement (as opposed to multiple measurements) introduces mainly random error and reduces statistical precision and most likely to bias estimates of association between sodium and health measures toward the null, but would not be expected to change the overall pattern (i.e., shape) of association.17 One practical way to account for this day-to-day variability in large population studies is by obtaining repeated measures in a subset of participants to estimate the correlation between measures obtained on two separate occasions (weeks or months apart) and then use the correlation to correct for the degree of regression-dilution bias. This has been widely used to describe the association between “usual” levels of exposures such as cholesterol or BP and CVD, and is widely accepted as being a valid approach.17 Since morning fasting urines are convenient, they have the added advantage of allowing easier repeat measures of sodium intake over time, to measure “usual” sodium intake. While conventional thinking has led people to assume that the 24-hour urine collection is the reference standard for measuring sodium intake, this assumption may have to be reconsidered. First, there is often a bias in volunteers to provide 24-hour urine collection, because it is inconvenient and intrusive, especially in people working outside their homes.9 This can lead to biases as to who agrees to participate in studies. Second, even in those who agree to obtain 24-hour urine collection, the rates of incomplete collection may be significant and even as high as 30%; and those who complete collections may differ from those who fail to complete collections.18,19 Third, participants are more reluctant to provide repeated 24-hour urine collections than simple morning urines. Therefore, while the 24-hour urine collection may be considered the reference standard for individual measurement, Kawasaki formula derived estimates from a single morning void may be a more appropriate reference standard for estimating sodium intake in populations. Recently, there have been advances in the area of sodium kinetics which question traditional concepts about sodium homeostasis and may have implications for measuring sodium intake.20 In a series of studies in space simulation flights, Rakova et al.20 found that aside from the extracellular space and a poorly exchangeable portion in bone, salt is stored largely in the skin interstitium and soft tissue, with immune system cells and lymph capillaries regulating sodium balance and BP. This suggests that sodium excretion on any given day may be only partly influenced by short-term variations in sodium intake but may be substantially influenced by the exchange of sodium across many different compartments. Therefore, on theoretical grounds, multiple “spot” collections of urine obtained some weeks or months apart may be a better indicator of sodium in the body than a single 24-hour collection. In conclusion, obtaining 24-hour urine collections may not be necessary and also may not be the optimal approach for large studies, as it is impractical and invariably results in the exclusion of a substantial proportion of the population. Instead a formulae-derived approach to estimate mean excretion of sodium from morning voids of urine is sufficiently robust and may even be the preferred method for large surveys and epidemiological studies. The authors declared no conflict of interest.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,017
score de la tête « metaresearch » (Gemma)0,074
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,047
Score d'incertitude au seuil0,088

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0170,074
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0060,005
Communication savante0,0040,009
Science ouverte0,0030,004
Intégrité de la recherche0,0470,048
Charge utile insuffisante (le modèle a refusé de juger)0,0060,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,161
Tête enseignante GPT0,318
Écart entre enseignants0,157 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations14
Publié2015
Routes d'admission1
Résumé présentnon

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Même revueAmerican Journal of Hypertension→Même sujetSodium Intake and Health→Travaux en français237 207→