(2023) 21:82
Nishi et al. BMC Medicine
https://doi.org/10.1186/s12916-023-02771-4

BMC Medicine

RESEARCH ARTICLE

Open Access

Water intake, hydration status and 2‑yearchanges in cognitive performance:a prospective cohort study
Stephanie K. Nishi1,2,3,4,5*, Nancy Babio1,2,3*, Indira Paz‑Graniel1,2,3, Lluís Serra‑Majem3,6, Jesús Vioque7,8,Montserrat Fitó3,9, Dolores Corella3,10, Xavier Pintó3,11,12, Aurora Bueno‑Cavanillas7,13, Josep A. Tur3,14,
Laura Diez‑Ricote15, J. Alfredo Martinez3,16,17, Carlos Gómez‑Martínez1,2,3, Andrés González‑Botella18,
Olga Castañer9, Andrea Alvarez‑Sala10, Cristina Montesdeoca‑Mendoza6, Marta Fanlo‑Maresma3,11,
Naomi Cano‑Ibáñez7,13,19, Cristina Bouzas3,14, Lidia Daimiel3,15,20, María Ángeles Zulet3,16,
John L. Sievenpiper4,5,21,22,23,24, Kelly L. Rodriguez25, Zenaida Vázquez‑Ruiz3,26 and Jordi Salas‑Salvadó1,2,3

Abstract
Background Water intake and hydration status have been suggested to impact cognition; however, longitudinal evi‑
dence is limited and often inconsistent. This study aimed to longitudinally assess the association between hydration
status and water intake based on current recommendations, with changes in cognition in an older Spanish popula‑tion at high cardiovascular disease risk.
Methods A prospective analysis was conducted of a cohort of 1957 adults (aged 55–75) with overweight/obesity(BMI between ≥ 27 and < 40 kg/m2) and metabolic syndrome from the PREDIMED-Plus study. Participants had com‑pleted bloodwork and validated, semiquantitative beverage and food frequency questionnaires at baseline, as wellas an extensive neuropsychological battery of 8 validated tests at baseline and 2 years of follow-up. Hydration status
was determined by serum osmolarity calculation and categorized as < 295 mmol/L (hydrated), 295–299.9 mmol/L(impending dehydration), and ≥ 300 mmol/L (dehydrated). Water intake was assessed as total drinking water intake
and total water intake from food and beverages and according to EFSA recommendations. Global cognitive function
was determined as a composite z-score summarizing individual participant results from all neuropsychological tests.
Multivariable linear regression models were fitted to assess the associations between baseline hydration status andfluid intake, continuously and categorically, with 2-year changes in cognitive performance.
Results The mean baseline daily total water intake was 2871 ± 676 mL/day (2889 ± 677 mL/day in men; 2854 ± 674
mL/day in women), and 80.2% of participants met the ESFA reference values for an adequate intake. Serum osmo‑
larity (mean 298 ± 24 mmol/L, range 263 to 347 mmol/L) indicated that 56% of participants were physiologicallydehydrated. Lower physiological hydration status (i.e., greater serum osmolarity) was associated with a greater decline
in global cognitive function z-score over a 2-year period (β: − 0.010; 95% CI − 0.017 to − 0.004, p-value = 0.002). No*Correspondence:
Stephanie K. Nishi
stephanie.nishi@urv.cat
Nancy Babio
nancy.babio@urv.cat
Full list of author information is available at the end of the article© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
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regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
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Nishi et al. BMC Medicine(2023) 21:82

Page 2 of 17

significant associations were observed between water intake from beverages and/or foods with 2-year changes in
global cognitive function.
Conclusions Reduced physiological hydration status was associated with greater reductions in global cognitive
function over a 2-year period in older adults with metabolic syndrome and overweight or obesity. Future researchassessing the impact of hydration on cognitive performance over a longer duration is needed.
Trial registration International Standard Randomized Controlled Trial Registry, ISRCTN89898870. Retrospectively
registered on 24 July 2014
Keywords Hydration, Water, Fluids, Serum osmolarity, Serum osmolality, Cognition, Cognitive function, Cognitive
performance, PREDIMED-plus

Background
Cognitive decline is an important public health concern given 55 million people have been diagnosed withdementia and almost 80 million people are projected tobe affected by 2030 [1]. Cognitive function is particularly
important, especially as the population ages, because it
determines the maintenance of our independence, theperformance of everyday activities, and the quality of life[1]. Cognitive decline and dementia have a diverse etiology, and effective treatment is still not available [2]. For
this reason, prevention strategies targeting modifiablefactors, such as nutritional habits and dietary intake toslow the development of cognitive impairment, remaina promising public health approach [2]. Evidence to dateassessing individual dietary factors and dietary patterns
in relation to cognitive health has been summarized
in the Lancet Neurology [3], while there is information
related to various macro- and micronutrients and patterns, evidence related to water appears limited.
Water intake is a nutritional habit that is often overlooked, yet it is considered essential for the optimal physiological function of the human body [4]. As the mostabundant component of the human body, water intake
and optimal hydration are considered key aspects for theproper functioning of organ systems, aiding, among others, in efficient digestion, elimination of toxins, energy
production, thermoregulation, and joint lubrication, as
well as a multitude of biochemical reactions [5, 6]. Furthermore, proper hydration is thought to be importantfor optimal cognitive functioning as it plays a vital role inneural conductivity [7].
Dehydration occurs when the body loses more water
than is taken in, whereas hypohydration refers to the stateof water deficit; these conditions result in less-than-optimal hydration and may elicit adverse physiological consequences [8]. For the purposes of this paper, dehydration
will be the term used to encompass the state of improper
hydration due to unbalanced water loss or water deficit.
In Europe, the percentage of the population reported tohave inadequate water intake is estimated to vary from 5
to 35% [9–11]. Understanding water intake and hydration

status is of particular importance in older adults as thispopulation tends to be less likely to meet recommendations on water intake and is at greater risk for dehydration due to blunted sensitivity to thirst signals, lower
body reserves due to reduced muscle mass, reduced ability to deal with heat stress, and use of medications or
laxatives with diuretic effects [12–15]. Multiple healthorganizations and dietary guidelines have acknowledgedthat adequate hydration status is associated with thepreservation of physical and mental functions and thatwater intake is the best way to achieve hydration [16–19].
To maintain adequate hydration status, several countries and public organizations have proposed water
intake recommendations for the public [20, 21]. These
recommendations have been determined based on datafrom population studies and accounted for water frombeverage and food sources [22]. For instance, the European Food Safety Authority (EFSA), based on data from
population studies from 13 European countries, proposed the dietary reference values (DRV) for the adequate intake (AI) of water which increases with age up to
2.5 L and 2.0 L of water daily for men and women (aged
14 to 70 years), respectively [20]. Similarly, the Instituteof Medicine (IOM) proposed increasing water intake levels with age up to 3.7 L/day and 2.7 L/day (aged 19 to 70
years) in men and women, respectively. Yet, the World
Health Organization and other guidelines related to cognitive health do not currently include recommendations
related to water intake or hydration status [23–25]. Further understanding of health behaviors, such as cognitive functioning, related to something as fundamental
as water intake can have a substantial impact on publichealth.
Inadequate water intake and dehydration have been associated with existing signs of cognitive impairment amongolder adults living in long-term care facilities [26–29].
Moreover, acute studies have shown that dehydration andwater supplementation affect mood and cognitive performance [30]. However, fluid and water intake has received
limited attention in epidemiological studies, and the literature scarcely examines water intake as a predictor of

Nishi et al. BMC Medicine(2023) 21:82

cognitive performance among older adults. The few studies
that have assessed hydration status as a potential predictor of cognitive function among community-dwelling older
adults have been inconclusive [31–34]. Furthermore, todate, few studies have prospectively captured and examined
the impact of water and hydration status on cognitive function over a multi-year period. Therefore, the objective of thepresent analyses was to prospectively investigate the relation
between hydration status, water intake, and 2-year changes
in cognitive performance in community-dwelling older
adults with metabolic syndrome and overweight or obesity.

Methods
Study design

This prospective cohort study is based on data collectedduring the first 2 years of the PREDIMED-Plus (PREvención con DIeta MEDiterránea Plus) study. Briefly, thePREDIMED-Plus study is an ongoing randomized, parallel-group, 6-year multicenter, controlled trial designed toassess the effect of lifestyle interventions on the primaryprevention of cardiovascular disease. The primary aim of
the trial is to assess the effects of an intensive weight loss
intervention based on an energy-reduced Mediterranean
diet (MedDiet), physical activity promotion, and behavioral support (intervention group) compared to usual careand dietary counseling only with an energy-unrestricted
MedDiet (control group) on the prevention of cardiovascular events. Details of the design and methods of PREDIMED-Plus have been previously described [35, 36] andare available at https://​www.​predi​medpl​us.​com/.
Ethics, consent, and permissions

The PREDIMED-Plus study protocol and procedures
were approved by the Research Ethics Committees fromeach of the participating centers, and the study was registered with the International Standard Randomized Controlled Trial Registry (ISRCTN; ISRCT​N8989​8870). All
participants provided written informed consent.
Study participants

PREDIMED-Plus participants were recruited from
23 centers across Spain between September 2013 andDecember 2016. A total of 6874 adults met the eligibility
criteria and were randomly allocated in a 1:1 ratio to either
the intervention or the control group. Couples sharing thesame household were randomized together, and the couple
was used as a unit of randomization. Eligible participants
were community-dwelling adults (aged 55 to 75 for men;
60 to 75 for women) with overweight or obesity (BMI: 27
to 40 kg/m2) who met at least three criteria for metabolic
syndrome (35), without previous cardiovascular events ordiagnosed neurodegenerative diseases at baseline. All participants provided written informed consent.

Page 3 of 17

The present longitudinal analysis involves a sub-study
conducted in 10 of the 23 PREDIMED-Plus recruiting
centers. Of the participants in the PREDIMED-Plus substudy who had a completed validated 32-item Spanish
fluid intake questionnaire, participants were excluded ifthey did not have a completed baseline Food Frequency
Questionnaire (FFQ) or who reported implausible totalenergy intakes based on those proposed by Willet (≤ 500
and ≤ 3500 kcal/day in women and ≤ 800 and ≤ 4000
kcal/day in men) [37]. For the water and fluid intake analyses, using the interquartile range method (using a 1.5
multiplier for the first and third quartiles), participants
with extreme intakes of fluid (daily fluid intakes for men< 188 mL or > 3862 mL and women < 263 mL or > 3539
mL) were excluded for the assessment of water and fluid
intakes. Similarly, participants without blood sample
values for urea, sodium, potassium, glucose, and serum
osmolarity values < 100 mmol/L were also excluded fromthe analyses of hydration status and cognition. Furthermore, associations were tested for those participants who
had completed the various cognitive tests.
Assessment of water and fluid intake

A validated, semi-quantitative 32-item Beverage Intake
Assessment Questionnaire (BIAQ) [10] and a 143-item
validated semi-quantitative FFQ (38) specifying usual portion sizes, were administered by trained dietitians to assess
habitual fluid and dietary intakes, respectively. These twoquestionnaires have been validated within populationsof older, Spanish individuals, which are analogous to thecurrent study population, and both have been found to
be reproducible with relative validity [10, 38]. The BIAQ
recorded the frequency of consumption of various beverage types during the month prior to the visit date. The
average daily fluid intake from beverages was estimatedfrom the servings of each type of beverage. The questionnaire items on beverages included: tap water, bottled water,
natural fruit juices, bottled fruit juices, natural vegetablejuices, bottled vegetable juices, whole milk, semi-skimmed
milk, skimmed milk, drinking yogurt, milkshakes, vegetable drinks, soups, jellies and sorbets, soda, light/zero soda,
espresso, coffee, tea, beer, non-alcoholic beer, wine, spirits,
mixed alcoholic drinks, energy drinks, sports drinks, meal
replacement shakes, and other beverages. The water andnutrient contents of the beverages were estimated mainly
using the CESNID Food Composition Tables [39], complemented with data from the BEDCA Spanish Database of
Food Composition [40].
The FFQ collected data on food intake based on the year
prior to the visit according to nine possible frequency categories, which ranged from “never or almost never” to “>
6 portions/day” and based on the dietary guidelines for
the Spanish population [41]. The information collectedNishi et al. BMC Medicine(2023) 21:82

was converted into grams per day, multiplying portion
sizes by consumption frequency and dividing the result
by the period assessed. Ten food groups composed ofvegetables, fruits, legumes, cereals, dairy beverages, meatand poultry, fats, nuts, fish/seafood, and other foods were
determined to assess the contribution of foods to total
water intake. Food groups and energy intake were estimated using Spanish food composition tables [42, 43].
Drinking water intake, water intake from all fluids, total
water intake, EFSA total fluid water intake (TFWI), and
EFSA total water intake (TWI) were computed (descriptions summarized in Additional file 1: Table S1). Drinking water intake was estimated based on tap and bottled
water intakes based on BIAQ responses. Water intake
from all fluids was computed from tap and bottled water,
plus water from other beverages based on responses tothe BIAQ. Total water intake encompassed water intake
from all fluids in addition to water present in food sources
based on responses to the FFQ. Water intake was further categorized based on established reference values.
The EFSA recommendations for total water intake (EFSA
TWI) for older adults (2.5 L/day and 2.0 L/day for men
and women, respectively) in conditions of moderate environmental temperature and moderate physical activity[20] were used as reference values. Further categorizations were determined based on total water intake fromfluids alone, based on EFSA recommendations (EFSA
TFWI), where recommended levels for older adults are
set to at least 2.0 L/day and 1.6 L/day for men and women,
respectively [20].
Assessment of hydration status

Hydration status was estimated based on calculated
serum osmolarity (SOSM), which is considered a morereliable biomarker of hydration status than urinary
markers in older adults [44]. Fasting serum glucose,urea, sodium, and potassium were measured by standard
methods. Blood urea nitrogen was determined from urea
values using the conversion factor of 0.357 and reported
in mmol/L. With all relevant serum analyte measurespresented in mmol/L, SOSM was estimated using thefollowing equation [45]:Page 4 of 17

Assessment of cognitive performance

A battery of 8 neuropsychological tests assessing different cognitive domains was administered at baseline
and 2-year follow-up by trained staff to assess cognitive performance. The following tests were assessed: theMini-Mental State Examination (MMSE), two Verbal
Fluency Tests (VFTs), two Digit Span Tests (DSTs) of
the Wechsler Adult Intelligence Scale-III (WAIS-III), theClock Drawing Test (CDT), and two Trail Making Tests(TMTs).
Briefly, a Spanish-validated version of the MMSE questionnaire, a commonly used cognitive screening test, wasused in the present analysis [47]. A higher MMSE scoreindicates better cognitive performance [48]. Verbal ability
and executive function were evaluated using the VFTs,
which consist of two parts: the semantic verbal fluency
task-animal category version (VFT-a) and the phonemic
verbal fluency task-letter “p” version (VFT-p) [49]. The
DST of the WAIS-III Spanish version assessed attention
and memory. The DST Forward Recall (DST-f ) is representative of attention and short-term memory capacity,and the DST Backward Recall (DST-b) is considered as atest of working memory capacity [50, 51]. The CDT-validated Spanish version was mainly used to evaluate visuospatial and visuo-constructive capacity [52–54]. Lastly,
the TMT, another tool often used to assess executivefunction, consists of two parts. Part A (TMT-A) assessed
attention and processing speed capacities, and part B(TMT-B) further examined cognitive flexibility [55]. All
instruments included in the cognitive battery have beenstandardized for the Spanish population in the age rangeof the study population.
To assess overall cognitive function, a global cognitive
function (GCF) score was determined as the main outcome measure, in addition to evaluating the individual
neuropsychological tests (supplementary analyses). Raw
scores at baseline and scores of changes at 2 years of follow-up for each individual cognitive assessment, as well
as GCF, were standardized using the mean and standard
deviation from the baseline measurements as normative data, creating z-scores [56]. GCF was calculated as
a composite z-score of all 8 assessments, adding or sub-SOSM = 1.86 ∗ (sodium + potassium) + 1.15 ∗ glucose + blood urea nitrogen + 14
Based on this equation, dehydration, impending dehydration, and hydrated statuses were defined as SOSM >300, 295–300, and < 295 mmol/L, respectively [28, 46].

tracting each individual test value based on whether ahigher score indicates higher or lower cognitive performance, respectively, using the formula:

)
)
) (
(
(
GCF = ZMMSE + ZCDT + ZVFT−a + ZVFT−p + −ZTMT−A + −ZTMT−B + ZDST−f + ZDST−b ∕8Nishi et al. BMC Medicine(2023) 21:82

Covariate assessment

The trained staff collected baseline socio-demographic(i.e., sex, age, education level, and civil status) and lifestyle (i.e., physical activity, total energy intake, alcohol
intake, caffeine consumption [57], sleeping habits, andsmoking status) related variables, as well as information
about medication use, in face-to-face interviews using
self-reported general questionnaires and a 143-item validated semi-quantitative FFQ for the dietary related variables [38], which were further estimated using Spanish
food composition tables [42, 43]. Leisure time physicalactivity was estimated using the validated MinnesotaREGICOR Short Physical Activity questionnaire [58].
These socio-demographic and lifestyle variables wereconsidered as possible covariates because of reports thatyounger adults, women, individuals with higher educational attainment, married, more active, greater consumers, and non-smokers tend to consume higher amountsof fluids from beverages and foods and hence more likely
to meet recommendations on water intake [59, 60]. Alcohol was accounted for as a potential covariate as it may
act as a diuretic at certain levels [61] as well as being
associated with an elevated risk of dementia when consumed regularly [62]. Similarly, caffeinated beverages
may have a mild diuretic effect [63], as well as may affectattention and alertness [64] and could be associated with
reduced cognitive decline and dementia risk [65]. Sleeping habits have also been associated with cognitive health[66]. Anthropometric measures, including weight andheight were measured by trained staff using calibrated
scales and wall-mounted stadiometers, respectively.
Body mass index (BMI), which may modify the relationship between water intake and hydration status [67],was calculated as weight in kilograms divided by heightin meters squared. History of chronic disease (i.e., type2 diabetes, hypertension, and hypercholesterolemia) was
self-reported or collected from patient medical records
and included as these conditions may cause fluid imbalance, cause dehydration, and have been associated withcognitive performance possibly leading to mild cognitive
impairment [68–70]. Depressive symptomatology wasevaluated using the Beck Depression Inventory-II (BDIII), given the association observed with cognitive health[71] where depressive symptomatology risk was established as a score ≥ 14 [72].
Statistical analyses

For the present analyses, a prospective cohort study was
conducted within the framework of the PREDIMED-Plusstudy using the database updated to December 22, 2020.
Participants were categorized into quantiles based on
baseline water intake (drinking water, water intake fromall fluids, total water intake), recommended categories ofPage 5 of 17

water intake (EFSA TWI, EFSA, TFWI), and hydration
status according to serum osmolarity. Baseline characteristics of participants for each category and quantileof water intake and hydration status were presented as
numbers and percentages using Pearson’s chi-square test
for categorical variables and means ± standard deviations or median (interquartile range [P25–P75]) using
one-way ANOVA or Kruskal Wallis test for continuous
variables, as appropriate.
Multivariable linear regression models were fitted toassess longitudinal associations comparing the 2-yearchange in cognitive function across baseline variables ofhydration status and water intake and for meeting the
EFSA recommendations for TWI and TFWI [20]. When
analyses were performed with categorical variables, p fortrend was calculated. The p for linear trend was calculated by assigning the median value of each category andmodeling it as a continuous variable.
Multivariable linear regression models were adjusted
for several potential confounders. Model 1 adjusted forage (years), sex (man or woman), intervention group,participating center size (< 100, 100 to < 150, 150 to <200, ≥ 200 participants for hydration status and < 100,
100 to < 200, 200 to < 300, ≥ 300 participants for fluidrelated analyses), respective baseline cognitive function
score, and corrected for clusters (to account for couples
living in the same household being randomized as a single unit). Model 2 was additionally adjusted for BMI (kg/m2), educational level (primary, secondary, or college),
civil status (single, divorced or separated, married, widower), smoking status (current, former, or never), andphysical activity (METs/min/day). Model 3 was additionally adjusted for sleeping habits (hours of nighttime
sleep), depressive symptomatology (yes/no), diabetes
prevalence (yes/no), hypertension (yes/no), hypercholesterolemia (yes/no), total energy intake (kJ per day),alcohol consumption in g/day (and adding the quadraticterm), and caffeine intake (g/day). To assess the linear
trend, the median value of each category of exposure
variables (hydration status and various assessments ofwater and fluid intake) was assigned to each participant and was modeled as continuous variables in linear
regression models. The Bonferroni correction was used
to correct for multiple comparisons and reduce the riskof a type 1 error.
Several stratified and sensitivity analyses were additionally performed to test the robustness of the findings.
First, sex-stratified regression approaches were employed
to examine the relationships between hydration status
and these water and fluid intake categories and 2-yearchanges in global cognitive function. Sensitivity analyses
were additionally performed, testing the addition of estimated glomerular filtration rate (eGFR), an indicator of

Nishi et al. BMC Medicine(2023) 21:82

renal function derived based on serum creatinine level,
age, and sex [73], or dietary intake covariates (amount[g/day] of vegetables, fruits, legumes, grains, non-fluid
dairy, meat, oils, fish, nuts, and pastries determined via
the validated 143-item semi-quantitative FFQ [19]) to
the multivariable models, but also after removal of participants with baseline MMSE < 24 (mild dementia andpoorer) [74], or the removal of participants with extreme
GCF z-scores at baseline (< 5% and > 95%).
The data were analyzed using the Stata 14 software
program (StataCorp LP, TX, USA), and the results wereoriginally considered statistically significant at a p-value(2-tailed) < 0.05, and after the Bonferroni correction, statistical significance was considered at a p-value (2-tailed)< 0.005.

Results
A total of 1957 participants (mean age 65.0 ± 4.9 years
and 50.5% women) were available for the assessmentof water and fluid intake and 1192 participants for theassessment of hydration status after excluding missing
values or implausible data (Additional file 1: Fig. S1).
Table 1 presents the baseline characteristics of the study
population according to sex, water (tap and bottle) intake
amount, and hydration status. The median (range) consumption of drinking water intake in men and women
was 900 (0 to 3100) and 900 (0 to 2700) mL/day, respectively. Compared with participants in the group with thelowest drinking water intake (< 500 mL/day), those withthe highest drinking water intake (1.8 to 3.1 L/day) weremore likely to be younger (p < 0.001), have a higher BMI(p = 0.001), and have a lower alcohol intake (p < 0.001).
Compared to participants considered to be hydrated
according to serum osmolarity status, participants considered to be dehydrated tended to be older (p = 0.008),
women (p = 0.008), have type 2 diabetes (p < 0.001), havedepressive symptoms (p = 0.025), and less likely to drink
alcohol (p = 0.002). Participants involved in the present
analyses did not differ from the rest of the participants
enrolled in the PREDIMED-Plus trial in terms of age,sex, BMI, and prevalence of obesity and type 2 diabetes(p > 0.05 for all comparisons). Furthermore, 80% of participants (69% of men and 90% of women) met the EFSA
fluid intake recommendations based on questionnaire
responses, yet serum osmolarity values indicated over
50% of participants were physiologically dehydrated with
only 10% of participants being considered physiologically
hydrated based on serum osmolarity levels.
Figures 1 and 2 summarize the multivariable-adjusted
β-coefficients (95% CIs) of water intake and hydration
status categorically and continuously, respectively, with
2-year changes in GCF z-scores (full details from allmodels are presented in Additional file 1: Tables S2-S4).

Page 6 of 17

Categorical analyses showed a non-significant trend
towards participants considered to have a dehydrated
status (β: − 0.11; 95% CI: − 0.24 to 0.02; p for trend =0.058) to have a greater decline in global cognitive function compared to those who were considered hydrated(Fig. 1). No significant associations were observed
between the various classifications of water intake (i.e.,
drinking water, all fluids, water from beverage and foodsources, and based on ESFA water and fluid recommendations) and 2-year changes in GCF in the multivariableadjusted models. The results of the continuous linear
regression analyses suggest the presence of a significant
association between hydration status and global cognitive
decline over a 2-year period (β: − 0.10; 95% CI: − 0.02 to− 0.004; p = 0.002) (Fig. 2).
Additional file 1: Tables S2-S4 show the unadjusted andmultivariable-adjusted β-coefficients (95% CIs) of waterand fluid intake (from both beverage and food sources,
assessed individually and combined), as well as hydration
status with changes in the global and individual assessments of cognitive function over a 2-year period. Wheneach neuropsychological test was investigated separately,
participants with the highest category of intake of drinking water (1.0 to 1.5 L/day) non-statistically significantly
presented with a 0.17-point increase (β: 0.17; 95% CI:
0.02 to 0.32; p for trend = 0.021) in DST-f z-score compared to those with the lowest water intake (< 0.5 L/day)over a 2-year period (Additional file 1: Table S2). Total
fluid intake showed similar findings where participantsin the highest category of intake of total fluid water (2.5
L, range 2.2 to 3.4 L/day) presented with a 0.12-point
increase (β: 0.12; 95% CI: 0 to 0.24; p for trend = 0.041)
in DST-f z-score compared to those with the lowest water
intake (1.1 L, range 0.4 to 1.4 L/day) over a 2-year period(Additional file 1: Table S2). No other associations in themultivariable-adjusted categorical or continuous analyses
were observed. Furthermore, post hoc analyses of available data showed no significant differences in changes
over time in the various fluid intake assessment variables(over a 2-year duration) or hydration status (over a 1-year
duration) (p > 0.005) among the participants.
Stratified analyses

When the analyses were stratified by sex, no changes insignificance were observed with the associations between
water and fluid intakes, in either categorical or continuous investigations, and global cognitive function. However,when hydration-related analyses were restricted to men or
women participants, findings were attenuated when women
only were assessed both categorically and continuously (p >0.005), whereas in men those with a SOSM ≥ 300 mmol/L,
indicating a dehydrated status, showed a higher cognitive
decline over the 2-year period compared to those hydrated

900(0–3100)

1957

MedianRange

Frequency, n

65. 0± 4.9

989 (50.5)545 (27.9)

389 (19.9)  Highschool  College

1513 (77.3)

202 (10.3)  Married  Widower

348.7 ± 334.4

Physical
activity (MET
min/day)817 (41.8)

236 (12.1)  Formersmoker  Current
smoker697 (35.6)

1096 (56.0)

164 (8.4)  ≤ 6 h  6 to ≤8h   > 8 h

Sleeping status, weekdays

904 (46.2)  NeversmokedSmoking status32.6 ± 3.5

Body massindex, kg/m2

Lifestyle factors

242 (12.4)  Single,
divorced or
separated

Civil status

1023 (52.3)  Primaryschool

Education level

Sex(women)Age (years)

Socio-demographic data

Total

Trait

91 (9.2)

73 (7.5)

579 (59.8)

316 (32.6)

163 (16.8)

605 (62.5)

200 (20.7)

414.8 ± 395.5

32.3 ± 3.4

33 (3.4)

829 (85.6)

106 (11.0)

264 (27.3)

310 (32.0)

394 (40.7)

63.70 ± 5.36

968 (49.5)

968(0–3100)

900

Men

0.003

< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001

p-value

34 (8.7)215 (55.1)

141 (36.2)

45 (11.5)

163 (41.8)

182 (46.7)

330.5 ± 322.4

32.3 ± 3.4

43 (11.0)300 (76.9)

47 (12.1)

91 (23.3)

111 (28.5)

188 (48.2)

188 (48.2)

65.0 ± 4.9

390(0–500)400 ml/day

68 (9.1)

412 (55.4)

264 (35.5)

105 (14.1)

305 (41.0)

334 (44.9)

345.2 ± 335.4

32.7 ± 3.5

88 (11.8)560 (75.3)

96 (12.9)

144 (19.4)

201 (27.0)

399 (53.6)

383 (51.5)

65.4 ± 4.8744(800–1000)

900 ml/day

40 (7.3)

315 (57.7)

191 (35.0)

61 (11.2)

222 (40.7)

263 (48.17)

364.1 ± 348.9

32.5 ± 3.5

52 (9.5)423 (79.1)

62 (11.4)

101 (18.5)

148 (27.1)

297 (54.4)

280 (51.3)

64.9 ± 5.0546(1071–1400)

1300 ml/day

22 (7.9)154 (55.6)

101 (36.5)

25 (9.0)127 (45.9)

125 (45.1)

352.6 ± 325.6

33.3 ± 3.5

19 (6.9)221 (79.8)

37 (13.4)

53 (19.1)

85 (30.7)

139 (50.2)

138 (49.8)

64.0 ± 4.7277(1800–3100)

1800 ml/day

Highestp-value

0.929

0.729

0.502

0.001

0.311

0.381

0.731

< 0.00113 (10.6)

65 (52.9)45 (36.6)

20 (16.3)

56 (45.5)

47 (38.2)

423.9 ± 338.2

32.3 ± 3.1

9 (7.3)

97 (78.9)

17 (13.8)

26 (21.1)

29 (25.6)

68 (55.3)

47 (38.2)

64.2 ± 5.0

123(263–294.9)

292 mmol/L

33 (8.3)

227 (56.8)

140 (35.0)

48 (12.0)

177 (44.3)

175 (43.8)

364.5 ± 337.9

32.5 ± 3.4

35 (8.8)319 (79.8)

46 (11.5)

77 (19.3)

112 (28.0)

211 (52.8)

190 (47.5)

64.3 ± 5.0

400(295–299.9)

298 mmol/L

Impendingdehydrated

66 (9.9)366 (54.7)

237 (35.4)

67 (10.0)269 (40.2)

333 (49.8)

344.2 ± 346.8

32.8 ± 3.5

72 (10.8)520 (77.7)

77 (11.5)

113 (16.9)

184 (27.5)

372 (55.6)

353 (52.8)

65.2 ± 4.8

669(300–346.5)

304 mmol/L

Dehydrated

Hydratedb

Highmoderate

LowestLowmoderate

Categories of estimated S­ OSMaCategories of water intake

0.863

0.057

0.057

0.245

0.645

0.636

0.008

0.008

p-value(2023) 21:82

517 (52.3)

381 (38.5)

73 (7.4)

212 (21.4)

704 (71.2)

284.0 ± 244.6

33.0 ± 3.6

169 (17.1)

684 (69.2)

136 (13.8)

125 (12.6)

235 (23.8)

629 (63.6)

66.2 ± 4.0

989 (50.5)989(0–2700)

900

Women

Table 1 Baseline characteristics of the participants according to sex, categories of water intake, and hydration status

Nishi et al. BMC Medicine
Page 7 of 17

Total1111 (56.9)

241 (12.4)  6 to ≤8h   > 8 h

1411 (72.1)

428 (21.9)

Hypercho‑
lesterolemia

Depressive
symptoms

297.6 ± 28.0

Blood urea 6.6 ± 1.8
nitrogen (BUN,
mmol/L)

SOSM(mmol/L)a

9.8 ± 13.3

2391.7 ±541.4

33.6 ± 31.8

Alcoholintake (g/day)

Energyintake (kcal/
day)

Caffeine
intake (mg/day)

Dietary intake

6.6 ± 1.7

6.3 ± 1.7

Glucose(mmol/L)

298.4 ± 24.4

6.3± 1.6

139.4 ± 14.9

Serumsodium(mmol/L)

37.6 ± 34.9

2548.6 ±543.8

2238.3 ±493.1

29.6 ± 27.9

15.7 ± 15.6

299.1 ± 20.2

6.7 ± 1.9

6.4 ± 1.8

139.9 ± 12.4

4.3 ± 0.4

114 (11.8)

650 (67.2)

806 (83.3)

4.0 ± 6.7

139.0 ± 17.1

4.3 ± 0.4

4.3 ± 0.4314 (31.8)

761 (77.0)

834 (84.3)

335 (34.6)

116 (12.0)

125 (12.7)

283 (28.6)

594 (61.5)

256 (26.5)

Men

517 (52.4)

344 (34.9)

Women

Serum
potassium(mmol/L)

Biochemical parameters

618 (31.6)

1640 (83.8)

Diabetes

Hyperten‑sion

Disease prevalence at recruitment

600 (30.7)  ≤ 6 hSleeping status, weekends

Trait

Table 1 (continued)< 0.001< 0.001< 0.001

0.298

0.791

0.056

0.219

0.355

< 0.001< 0.001

0.523

0.004

< 0.001

p-value
Highestp-value

38.3 ± 35.2

2422.1 ±559.5

12.5 ± 16.2

299.2 ± 21.6

6.6 ± 1.7

6.29 ± 1.56

140.1 ± 12.6

4.4 ± 0.4

94 (24.1)

274 (72.1)

326 (83.6)

118 (30.3)

54 (13.9)

207 (53.2)

128 (32.9)

31.6 ± 29.7

2391.2 ±530.4

9.8 ± 12.9

297.6 ± 26.6

6.7 ± 1.8

6.34 ± 1.75

139.1 ± 15.7

4.3 ± 0.4

154 (20.7)

544 (73.5)

626 (84.1)

236 (31.7)

94 (12.7)

431 (58.1)

217 (29.3)

32.6 ± 31.3

2374.7 ±549.0

8.7 ± 12.2

299.3 ± 19.1

6.7 ± 1.9

6.27 ± 1.64

139.6 ± 14.5

4.28 ± 0.37

116 (21.3)

380 (71.0)453 (83.0)

179 (32.8)

65 (11.9)

320 (58.6)

161 (29.5)< 0.001

0.580

0.980

0.582

0.492

0.003

0.551

0.742

0.902

0.851

0.437

34.0 ± 32.6

0.007

2384.1 ± 30.8 0.611

8.2 ± 11.1

296.9 ± 31.1

6.7 ± 1.8

6.43 ± 1.95

138.8 ± 16.6

4.3 ± 0.4

64 (23.1)

177 (70.8)

235 (84.8)

85 (30.7)

28 (10.2)

153 (55.6)

94 (34.2)

35.7 ± 35.1

2400.0 ±598.1

13.3 ± 16.5

292.3 ± 3.6

5.9 ± 1.5

5.7 ± 0.9

137.6 ± 2.1

4.2 ± 0.4

20 (16.3)

88 (71.5)

96 (78.1)

30 (24.4)

19 (15.5)

69 (56.1)

35 (28.5)

36.9 ± 35.3

2342.4 ±549.3

11.3 ± 15.3

297.9 ± 1.4

6.1 ± 1.4

6.0 ± 1.2

140.2 ± 1.4

4.3 ± 0.4

81 (20.3)

292 (73.0)

329 (82.3)

89 (22.3)

43 (10.8)233 (58.5)

122 (30.7)

Impendingdehydrated

35.8 ± 31.9

2365.7 ±554.4

9.1 ± 13.2

304.1 ± 3.8

7.0 ± 1.9

6.7 ± 2.0

142.4 ± 2.1

4.3 ± 0.4

171 (25.6)

487 (72.8)

568 (84.9)

241 (36.0)

98 (14.7)

362 (54.2)

208 (31.1)

Dehydrated

Hydratedb

Highmoderate

LowestLowmoderate

Categories of estimated S­ OSMaCategories of water intake

0.869

0.579

0.002

< 0.001< 0.001< 0.001< 0.001

0.005

0.025

0.950

0.136

< 0.001

0.371

p-value

Nishi et al. BMC Medicine(2023) 21:82
Page 8 of 17

1842.4 ±592.0

2871.1 ±676.21569 (80.2)

Total fluid
intake (ml/day)

Total water
intake (ml/day)

Meets EFSA
TWI recom‑
mendations

VFT-p8.13 ± 2.32

5.06 ± 2.20

DST-b

5.58 ± 2.28

9.19 ± 2.48

116.8 ± 68.3

48.94 ± 28.88

12.61 ± 4.36

16.82 ± 4.71

5.94 ± 1.23

0.22 ± 0.58

28.46 ± 1.60

671 (69.3)

2889.2 ±677.4

1888.7 ±601.1

1029.38.1 ± 2.6

Men

< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.0010.24340.001

0.8551< 0.001

p-value
Highestp-value

5.04 ± 2.25

8.61 ± 2.33

129.9 ± 75.4

53.73 ± 28.75

12.23 ± 4.47

16.32 ± 4.54

5.83 ± 1.37

0.10 ± 0.58

28.33 ± 1.84

186 (47.7)

2241.2 ±500.9

1232.1 ±454.2

352.9 ± 123.4

8.4 ± 2.6

5.15 ± 2.15

8.74 ± 2.48

138.0 ± 79.2

57.04 ± 33.21

11.93 ± 4.49

15.61 ± 4.98

5.77 ± 1.31

0.04 ± 0.64

28.04 ± 1.98

595 (80.0)

2709.8 ±493.7

1691.2 ±388.5

892.1 ± 30.1

8.3 ± 2.6

4.85 ± 2.20

8.50 ± 2.57

142.9 ± 81.4

55.77 ± 32.55

11.65 ± 4.41

15.49 ± 4.73

5.80 ± 1.27

− 0.03 ± 0.66

28.02 ± 2.02

512 (93.8)

3146.0 ±516.0

2103.5 ±406.7

1300.5 ± 17.3

8.6 ± 2.6

5.27 ± 2.19

8.86 ± 2.34

132.2 ± 72.6

51.02 ± 27.04

12.23 ± 3.98

16.03 ± 4.31

5.86 ± 1.31

0.11 ± 0.55

28.36 ± 1.74

276 (99.6)

3646.0 ±562.8

2593.5 ±413.5

1829.2 ±155.7

8.7 ± 2.6

0.049

0.23

0.06

4.89 ± 2.30

8.26 ± 2.62

153.2 ± 92.3

11.99 ± 4.11

5.11 ± 2.11

8.74 ± 2.44

139.6 ± 81.0

54.97 ± 30.33

12.19 ± 4.35

15.72 ± 4.58

5.90 ± 1.27

0.13 ± 0.56

28.20 ± 2.09

309 (77.3)

2869.6 ±743.4

1886.5 ±662.4

1034.7 ±466.6

8.6 ± 2.6

5.02 ± 2.16

8.73 ± 2.53

146.4 ± 82.4

57.56 ± 36.13

11.46 ± 4.32

15.62 ± 4.70

5.89 ± 1.20

0.01 ± 0.64

28.14 ± 2.04

521 (77.9)

2852.4 ±735.2

1874.4 ±646.0

1044.3 ±478.8

8.6 ± 2.5

Dehydrated

SOSM represents calculated serum osmolarity and was calculated using the formula SOSM = 1.86 × (Na+ + K+) + 1.15 × glucose + BUN + 14, where all analytes are in mmol/L

0.638

0.176

0.217

0.474

0.023

0.913

0.481

0.824

0.028

0.750

0.421

0.672

0.946

0.889

p-valuec

GCF was calculated using the formula GCF= ­(ZMMSE + ­ZCDT + ­ZVFT-a + ­ZVFT-p + (−ZTMT-A) + (−ZTMT-B) + ­ZDST-f + ­ZDST-b)/8

Hydrated includes participants considered to be hydrated (SOSM 285 to < 295 mmol/L) and overhydrated (SOSM < 285 mmol/L). These groups were combined due to the small number of participants (n = 3) considered
to be overhydrated. Assessment with the removal of these participants did not modify the findingsbaAbbreviations: CDT, Clock Drawing Test; DASH, Dietary Approaches to Stop Hypertension; DST-f, Digit Span Test Forward; DST-b, Digit Span Test Backward; GCF, global cognitive function; MMSE, Mini-Mental State
Examination; TMT-A, Trail Making Test Part A; TMT-B, Trail Making Test Part B; VFT-a, Verbal Fluency tasks semantical; VFT-p, Verbal Fluency Tasks Phonological

SOSM > 300 mmol/L represented a dehydrated state, 295 to 300 mmol/L represented a state of impending dehydration, and < 294 mmol/L represented a hydrated state

Chi-squared is used for categorical variables and one-way ANOVA for quantitative variables

56.72 ± 28.45

0.037

0.145

5.75 ± 1.50

15.77 ± 5.04

0.032

0.767

28.23 ± 1.91

0.008

0.02 ± 0.67

99 (80.5)<0.0010.018

2947.2 ±710.1

1931.1 ±643.3

1036.0 ±499.2

8.7 ± 2.7

< 0.001< 0.001< 0.001

0.0619Impendingdehydrated

Hydratedb

Highmoderate

LowestLowmoderate

Categories of estimated S­ OSMaCategories of water intake

Data are n (%) or mean ± SD for categorical and quantitative variables, respectively. The exception is the diet score is represented as mean (range)

4.53 ± 1.97

156.6 ± 82.5

8.66 ± 2.46

136.9 ± 78.4

DST-f

11.31 ± 4.34

61.62 ± 32.56

11.95 ± 4.40

14.75 ± 4.54

5.68 ± 1.38

− 0.15 ± 0.61

27.82 ± 2.18

898 (90.8)

2853.5 ±674.8

1797.2 ±579.7

55.17 ± 31.40

TMT-B

TMT-A

5.81 ± 1.31

15.78 ± 4.74

CDT

VFT-a

0.04 ± 0.62

28.14 ± 1.94

MMSE ­GCFcCognitive function tests

1031.2 ±465.2 1033.1 ±458.8

Drinking
water intake(ml/day)

8.8 ± 2.6

8.5 ± 2.6

Mediter‑ranean dietscore (17point)

Women

Total

Trait

Table 1 (continued)

Nishi et al. BMC Medicine(2023) 21:82
Page 9 of 17

Nishi et al. BMC Medicine(2023) 21:82

Page 10 of 17

Fig. 1 Hydration status, water and fluid intakes categorically with 2-year changes in global cognitive function (z-scores). Data are presented as
beta-coefficients and 95% CI. Multivariable linear regression models were adjusted for baseline covariates, including baseline GCF score, age(years), sex, intervention PREDIMED-Plus randomized group, and participating center (for hydration status: ≤ 100, 100 to < 150, 150 to < 200, >200 participants; for fluid-related exposures: ≤ 100, 100 to < 200, 200 to < 300, > 300 participants), body mass index (kg/m2), educational level(primary, secondary, or college), civil status (single, divorced or separated, married, widower), smoking habit (current, former, or never), physicalactivity (METs/min/day), sleep status (hours per day), depressive symptomatology (yes/no), diabetes prevalence (yes/no), hypertension (yes/no),hypercholesterolemia (yes/no), energy intake (kcal/day), alcohol consumption in g/day (and adding the quadratic term), and caffeine intake (mg/day). aHydration status refers to serum osmolarity, where dehydration, impending dehydration, and hydrated statuses were defined as SOSM > 300,
295–300, and < 295 mmol/L, respectively. bDrinking water refers to tap and bottled water intakes. cWater, all fluids refers to tap and bottled water,
plus water from other beverages and fluid food sources, such as soups and smoothies. dTotal water refers to water from all fluids in addition towater present in food sources. eEFSA TFWI refers to the recommended levels of total fluid water intake for older adults at 2.0 L/day and 1.6 L/day formen and women, respectively. fEFSA TWI refers to the recommended levels of total water intake, from fluids and foods, for older adults at 2.5 L/dayand 2.0 L/day for men and women, respectively. Abbreviations: EFSA, European Food Safety Authority; TFWI, total fluid water intake; TWI, total water
intake; SOSM, serum osmolarity(β: − 0.20, 95% CI: − 0.36 to − 0.04, p = 0.012 and p-trend= 0.002), and the significance remained in the continuous
analyses (β: − 0.013, 95% CI: − 0.022 to − 0.004, p = 0.004)(Additional file 1: Figs. S2-S3).
Sensitivity analyses

Regarding the sensitivity analyses, associations additionally adjusted for eGFR did not significantly modify
the findings (Additional file 1: Tables S5-S7). Moreover, associations additionally adjusted for intake of dietary variables, where applicable, including the amount(g/day) of vegetables, fruits, legumes, grains, nonfluid dairy, meat, oils, fish, nuts, and pastries did not

significantly modify the findings (Additional file 1:
Tables S5-S7). Furthermore, the main results did not
substantially change after the removal of extreme GCF
baseline z-scores (< 5% and > 95%). Whereas following
the removal of participants with a baseline MMSE score< 24 (n = 51), the categorical association of hydration
status with GCF became greater showing a more dehydrated status related to greater 2-year global cognitive
decline compared to hydrated participants (p-trend =0.046), and the association of the continuous variables
remained significant (p = 0.002). The findings related towater and fluid intake did not substantially change withthe removal of participants with a baseline MMSE score< 24 (Additional file 1: Tables S5-S7).

Nishi et al. BMC Medicine(2023) 21:82

Page 11 of 17

Fig. 2 Hydration status, water and fluid intakes continuously with 2-year changes in global cognitive function (z-scores). Data are presented
as beta-coefficients and 95% CI. Multivariable linear regression models were adjusted for baseline covariates, including baseline GCF score, age(years), sex, intervention PREDIMED-Plus randomized group, and participating center (≤ 100, 100 to < 150, 150 to < 200, > 200 participants), bodymass index (kg/m2), educational level (primary, secondary, or college), civil status (single, divorced or separated, married, widower), smoking habit(current, former, or never), physical activity (METs/min/day), sleep status (hours per day), depressive symptomatology (yes/no), diabetes prevalence(yes/no), hypertension (yes/no), hypercholesterolemia (yes/no), energy intake (kcal/day), alcohol consumption in grams/day (and adding thequadratic term), and caffeine intake (mg/day). Beta represents the changes in global cognitive function, expressed as z-scores, with each hydration
or fluid intake component continuously. Bonferroni correction analyses have been run to correct for multiple comparisons and indicate a p < 0.005
is statistically significant

Discussion
To the best of our knowledge, this is the first multi-year
prospective cohort study to assess the association between
water intake (from fluid and food sources) and hydration
status, with subsequent changes in cognitive performancein older Spanish adults with metabolic syndrome andoverweight or obesity. In this large sample of older Spanish
adults, poorer hydration status was associated with greater
global cognitive decline over a 2-year period, particularly
in men, whereas water intake, from fluid and/or foodsources, and meeting related EFSA recommendations, was
not associated with global cognitive function. Nonetheless,when results of cognitive function tests were considered
independently, water intake (> 1.5 L/day compared to <0.5 L/day from tap or bottled water and 2.2 to 4.4 L/daycompared to < 1.4 L/day of water from all fluid sources)
was related to better attention and short-term memory, asassessed by DST-f, over a 2-year period.
Despite the general acknowledgment that an appropriate level of fluid intake and hydration status is importantfor health, there have been limited investigations to dateassessing the relationship between fluid intake or hydration status and cognitive function. Existing evidence
suggests that good hydration status may be associatedwith better cognitive test results and that mild, induced
dehydration can impair cognitive abilities [75], but findings are not consistent and there are only a few studies
exploring the relationship of hydration status and hardly
any assessing amount of water intake, with cognitive performance in older community-dwelling adults.

Of the few relevant and recent studies that have been
conducted, one cross-sectional analysis of 2506 community-dwelling older American adults (aged ≥ 60 years)
from the Nutrition and Health Examination Survey(NHANES) 2011–2014 cycles assessed both hydration
status and water intake in relation to cognitive performance [34]. In comparison with the present findings, thisstudy found women (but not men) considered to behydrated, based on a SOSM level between 285 and 289
mmol/L, had better attention and processing speeds,
based on a Digit Symbol Substitution test (DSST), than
women not at optimal hydration [34]. These cross-sectional findings differ from the present observations where
global cognitive function, but not individual tests related
to attention and processing, was associated with hydration status. Whereas, correspondingly, water intake (butnot hydration status) was positively associated with DSTf, which is similar to the DSST in that it is an indicator of
attention as well as short-term memory capacity, and thiswas seen across all older adults (both men and women).
Additionally, in the NHANES study, cognitive test scores
were significantly lower among adults who failed to meet
EFSA recommendations on adequate intake (AI) of waterin bivariate analyses, yet this significance was attenuatedin the multivariable analyses among both women andmen. Yet, using the alternative AI of daily water intake
of 1500 mL or more, which is comparable to the highest
drinking water intake group in the present study, womenscored higher on the Animal Fluency Test, a measure ofverbal fluency and hence executive function, and DSSTNishi et al. BMC Medicine(2023) 21:82

than women with intake levels below this amount, andfindings among men trended in the same direction [34].
Similarly, hydration status has been associated with cognitive function in two cross-sectional studies of older community-dwelling adults by Suhr and colleagues [32, 33].
First, Suhr et al. showed that in 28 healthy communitydwelling older adults (aged 50 to 82 years), a lower hydration status, determined in this study via total body watermeasured using the bioelectrical impedance method, wasrelated to a decreased psychomotor processing speed,
poorer attention, and memory [33]. A second cross-sectional study by Suhr et al. [32] conducted in 21 postmenopausal women (aged 50 to 78 years) reported a positive
association between total body water, also measured by
the bioelectrical impedance method, and working memory
or memory skills.
Moreover, the possible relation between hydration andcognitive function shown by the present findings anddemonstrated by the abovementioned cross-sectional
studies in older adults align with acute heat- and exercise-induced dehydration studies in younger populations,
as illustrated by a meta-analysis of 33 trials (pre-post or
crossover design) of 413 adults free of disease (aged <45 years). It should be noted that these studies assessed
dehydration over a period of less than 72 h with 27 (81%)
of the studies including only men participants, and 25(76%) studies involving recreationally or highly athletic
individuals. The authors concluded that despite variability among the included studies, dehydration impaired
cognitive performance, particularly for tasks involving
attention, executive function, and motor coordination
when water deficits exceeded 2% body mass loss [76].
Conversely, a cross-sectional study conducted in
Poland among 60 community-dwelling older adults (aged
60 to 93 years) found no significant relationship between
cognitive performance, as assessed using the MMSE,
TMT, and the Babcock Story Recall Test, and hydration
status as assessed by urine specific gravity [31]. The discrepancy between the findings from this cross-sectional
study and the present PREDIMED-Plus analyses might
be because all participants in the cross-sectional study
from Poland were considered to be adequately hydrated
and hence the authors of that study could not assess theimpact of a dehydrated state on cognition.
A noteworthy consideration when interpreting theliterature and the main findings of the current study
for practical use and in the determination of potential
mechanisms of action is the distinction between water
intake and water balance (related to hydration status)
within the body. When homeostasis of fluids within the
body is disrupted, modifying water intake may impact
cognitive function, yet due to the dynamic complexity ofbody water regulation impacting hydration status may be

Page 12 of 17

dependent on individualized physiological water intake
needs [8]. Thus, while the biological mechanism by whichwater intake and a hydrated status may reduce the riskof cognitive decline is unclear, evidence suggests that
aspects related to hydration and fluid homeostasis or alack thereof, such as hormone regulation and changes inbrain structure, could be a key underlying factor.
Several mechanisms regulate water intake and output
to maintain serum osmolarity, and hence hydration status, within a narrow range. Elevated blood osmolarity
resulting in the secretion of antidiuretic hormone (ADH),
also known as vasopressin or arginine vasopressin, a
peptide hormone which acts primarily in the kidneys
to increase water reabsorption, is one such mechanismthat works to return osmolarity to baseline and preserve
fluid balance [77]. In addition to its role in mediating thephysiological functions related to water reabsorption andhomeostasis, evidence has suggested that ADH participates in cognitive functioning [78] and that the associated cognitive modulations may further interplay with
sex hormones [79]. Antidiuretic hormone may be influenced by the androgen sex hormone, which is generally
more abundant in the brains of males than in females[80]. As a result, the impact of ADH on cognition could
be greater in males [80].
Exercise- and heat-induced acute dehydration studies implicate possible modifications to the brain structure
as another potential mechanism of action for an association between water intake, hydration status, and cognitive
function. Evidence has proposed that acute dehydration
can lead to a reduction in brain volume and subtle regionalchanges in brain morphology such as ventricular expansion, effects that may be reversed following acute rehydration [81, 82]. Acute dehydration studies have further
implicated hydration status in affecting cerebral hemodynamics and metabolism resulting in declines in cerebral
blood flow and oxygen supply [83, 84]. A lower vascularand neuronal oxygenation could potentially compromise
the cerebral metabolic rate for oxygen, thereby contributing to reductions in cognitive performance [81, 85–88].
Nonetheless, other potential unknown mechanisms cannot be disregarded.
There are several limitations and strengths of the present analyses that need to be acknowledged. The first
notable limitation is that the results may not be generalizable to other populations since the participants areolder Spanish individuals with metabolic syndrome andoverweight or obesity. Second, measurement error andrecall bias are possibilities given the use of questionnaires
to estimate water and fluid intake and that these rely on
responders’ memory which is a component of cognitive
function. However, these questionnaires have been validated and determined as reliable methods of assessing

Nishi et al. BMC Medicine(2023) 21:82

long-term intake in the present study population [37,38]. Third, despite its longitudinal design, water and fluid
intake and hydration status were only considered at baseline; however, as the questionnaires measure habitual beverage and food intake, and older adults are considered tohave reasonably stable dietary habits [37, 38], this is notexpected to significantly impact the findings. Along theselines, the possible effect of seasonality on water intake
and osmolarity was not considered a concern in the present analyses as the validation of the fluid questionnairemeasurements included assessments at various points
throughout the year (baseline vs. 6 months vs 1 year) withno significant differences observed in fluid consumption
across the different time points assessed [10]. Hence, thefinding of no difference between 6-month intervals, suggests no significant differences between opposing seasons(e.g., winter vs. summer; spring vs. fall). Furthermore,
SOSM determination may not necessarily detect acutedehydration or rehydration immediately prior to the cognitive testing, and it is unknown whether observed elevated SOSMs were due to inadequate water intake, ADH
abnormality, or other factors. While it is possible thatthe hydration status of some individuals was misclassified because serum osmolarity was estimated as opposed
to being directly measured, the equation has been shown
to predict directly measured serum osmolarity well inolder adult men and women with and without diabetes
or renal issues with a good diagnostic accuracy of dehydration and has been considered a gold standard for theidentification of impending and current water-loss dehydration in older adults [44, 45, 89–91]. Lastly, a discrepancy was observed between the percentage of individuals
that were considered to have met EFSA fluid intake recommendations and those considered to be dehydrated
based on calculated osmolarity. This may have been due
to the fact that the EFSA fluid intake recommendations
are meant for individuals in good health [20]; whereas thepresent study population had overweight or obesity, andit has been shown that individuals with higher BMIs havehigher water needs related to metabolic rate, body surface area, body weight, and water turnover rates related
to higher energy requirements, greater food consumption, and higher metabolic production [92]. Strengths ofthe present analyses include the longitudinal, prospective
design, the large sample size, the use of an extensive cognitive test battery, the use of validated questionnaires, andthe robustness of the current findings due to the adjustment of relevant covariates.

Conclusions
Findings suggest that hydration status, specificallypoorer hydration status, may be associated with agreater decline in global cognitive function in older

Page 13 of 17

adults with metabolic syndrome and overweight orobesity, particularly in men. Further prospective cohort
studies and randomized clinical trials are required toconfirm these results and to better understand the link
between water and fluid intake, hydration status, andchanges in cognitive performance to provide guidancefor guidelines and public health.
Abbreviations
ADH	Antidiuretic hormone
AI	Adequate intake
BDI-II	Beck Depression Inventory-II
BEDCA	Base de Datos Española de Composición de Alimentos
BIAQ	Beverage Intake Assessment Questionnaire
BMI	Body mass index
CESNID	El Centro de Enseñanza Superior de Nutrición y Dietética
CDT	Clock Drawing TestCI	Confidence interval
DRV	Dietary reference value
DSST	Digit Symbol Substitution Test
DST-b	DST Backward Recall
DST-f	DST Forward Recall
EFSA	European Food Safety Authority
eGFR	Estimated glomerular filtration rate
En	Energy
FFQ	Food Frequency Questionnaire
GCF	Global cognitive function
IOM	Institute of Medicine
ISRCTN	International Standard Randomized Controlled Trial Registry
MedDiet	Mediterranean diet
METs	Metabolic equivalents
MMSE	Mini-Mental State Examination
NHANES	Nutrition and Health Examination Survey
PREDIMED	PREvención con DIeta MEDiterránea
Q	Quartile
SOSM	Serum osmolarity
TFWI	Total fluid water intake
TMT-A	Trail Making Test Section A
TMT-B	Trail Making Test Section B
TWI	Total water intake
VFT-a	Verbal Fluency Test Animals Category
VFT-p	Verbal Fluency Test Letter “p” Category
WAIS-III	Wechsler Adult Intelligence Scale-III

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​023-​02771-4.
Additional file 1: Table S1. Hydration and water intake definitions.
Table S2. Associations between cognitive assessments and water andfluid intake exposures. Table S3. Associations between cognitive assess‑ments and EFSA fluid intake related guidelines. Table S4. Associations
between cognitive assessments and hydration status. Table S5. Sensitivity
analysis in global cognitive function according to water and fluid intake
related exposures Table S6. Sensitivity analysis in global cognitive func‑tion according to EFSA fluid intake related guidelines. Table S7. Sensitivity
analysis in global cognitive function according to hydration status. Fig. S1.
Flow diagram of participants. Fig. S2. Continuous sensitivity analysis bysex. Fig. S3. Categorical sensitivity analysis by sex.
Acknowledgements
We thank all PREDIMED-Plus participants and investigators. CIBEROBN,CIBERESP, and CIBERDEM are initiatives of the Instituto de Salud Carlos III(ISCIII), Madrid, Spain. The Hojiblanca (Lucena, Spain) and Patrimonio Comunal
Olivarero (Madrid, Spain) food companies donated extra virgin olive oil.

Nishi et al. BMC Medicine(2023) 21:82

The Almond Board of California (Modesto, CA), American Pistachio Growers(Fresno, CA), and Paramount Farms (Wonderful Company, LLC, Los Angeles,CA) donated nuts for the PREDIMED-Plus pilot study.
Authors’ contributions
LSM, JV, MF, DC, XP, ABC, JAT, JAM, and JSS contributed to the study concept
and design and data extraction from the participants from the PREDIMEDPlus study which provides the framework for the present prospective cohort
analysis. SKN, NB, IPG, CGM, and JSS made substantial contributions to theconception of the present study. SKN performed the statistical analyses andinitial interpretation of the data. NB, IPG, CGM, and JSS contributed to thereview of the statistical analyses and interpretation of the data. SKN drafted
the manuscript. All authors substantively reviewed the manuscript for impor‑tant intellectual content and approved the final version to be published. All
authors had full access to all the data in the study and take responsibility forthe integrity of the data and the accuracy of the data analysis.
Funding
This work was supported by the official Spanish Institutions for funding scientificbiomedical research, CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN)
and Instituto de Salud Carlos III (ISCIII), through the Fondo de Investigación para
la Salud (FIS), which is co-funded by the European Regional Development Fund(six coordinated FIS projects leaded by JS-S and JVi, including the following pro‑jects: PI13/00673, PI13/00492, PI13/00272, PI13/01123, PI13/00462, PI13/00233,
PI13/02184, PI13/00728, PI13/01090, PI13/01056, PI14/01722, PI14/00636,
PI14/00618, PI14/00696, PI14/01206, PI14/01919, PI14/00853, PI14/01374,
PI14/00972, PI14/00728, PI14/01471, PI16/00473, PI16/00662, PI16/01873,
PI16/01094, PI16/00501, PI16/00533, PI16/00381, PI16/00366, PI16/01522,
PI16/01120, PI17/00764, PI17/01183, PI17/00855, PI17/01347, PI17/00525,
PI17/01827, PI17/00532, PI17/00215, PI17/01441, PI17/00508, PI17/01732,
PI17/00926, PI19/00957, PI19/00386, PI19/00309, PI19/01032, PI19/00576,
PI19/00017, PI19/01226, PI19/00781, PI19/01560, PI19/01332, PI20/01802,
PI20/00138, PI20/01532, PI20/00456, PI20/00339, PI20/00557, PI20/00886,
PI20/01158); the Especial Action Project entitled: Implementación y evaluaciónde una intervención intensiva sobre la actividad física Cohorte PREDIMEDPlus grant to JS-S; the European Research Council (Advanced Research Grant
2014–2019; agreement #340918) granted to MÁM-G.; the Recercaixa (number
2013ACUP00194) grant to JS-S; grants from the Consejería de Salud de la Junta
de Andalucía (PI0458/2013, PS0358/2016, PI0137/2018); the PROMETEO 17/2017
and PROMETEO 21/2021 grant from the Generalitat Valenciana; the SEMERGEN
grant; and the Juan de la Cierva-Incorporación research grant (IJC2019-042420-I)of the Spanish Ministry of Economy, Industry and Competitiveness and Euro‑pean Social Funds. This research was also partially funded by EU-H2020 Grants(Eat2beNICE/H2020-SFS-2016-2) and the Horizon 2020 PRIME study (Prevention
and Remediation of Insulin Multimorbidity in Europe; grant agreement #847879).
S.K.N. is supported by a postdoctoral fellowship from the Canadian Institutes of
Health Research (CIHR, MFE-171207). CG-M is supported by a predoctoral grantfrom the University of Rovira I Virgili (2020PMF-PIPF-37). IP-G is supported by a
predoctoral grant from Ministerio de Ciencia, Innovación y Universidades (FPU
17/01925). AA-S has received a post-doctoral grant (APOSTD/2020/164) fromthe Consellería de Innovación, Ciencia y Sociedad Digital, Generalitat Valenciana,
Fondo Social Europeo-FSE. CB is supported by a Juan de la Cierva postdoctoral
grant from Ministerio de Ciencia, Innovación y Universidades. JS-S, the seniorauthor of this paper, was partially supported by ICREA under the ICREA Academia
program. Publication of this work is supported by a conference award grant from
Hydration for Health presented by the Danone Global Research & Innovation
Center (#2022-08514) awarded to S.K.N. for being the conference attendee voted
recipient of the Early Career Researcher Award. None of the funding sources took
part in the design, collection, analysis, interpretation of the data; writing of thereport; or the decision to submit the manuscript for publication.
Availability of data and materials
The dataset supporting the conclusions of this article is available uponrequest pending application and approval of the PREDIMED-Plus Steering
Committee. There are restrictions on the availability of data for the PREDIMEDPlus trial, due to the signed consent agreements around data sharing, which
only allow access to external researchers for studies following the project
purposes. Requestors wishing to access the PREDIMED-Plus trial data used inthis study can make a request to the PREDIMED-Plus trial Steering Committee
chair: jordi.​salas@​urv.​cat. The request will then be passed to members of the
PREDIMED-Plus Steering Committee for deliberation.

Page 14 of 17

Declarations
Ethics approval and consent to participate
The PREDIMED-Plus study protocol and procedures were approved by the
Research Ethics Committees from each of the participating centers, and thestudy was registered with the International Standard Randomized Controlled
Trial registry (ISRCTN; ISRCT​N8989​8870). All participants provided written
informed consent.
Consent for publication
Not applicable.
Competing interests
S.K.N. is a volunteer member of the not-for-profit group Plant-Based Canada
and received an Early Career Researcher Award conference grant from Hydra‑tion for Health presented by Danone Global Research & Innovation Center.
D.C. reported receiving grants from Instituto de Salud Carlos III. R.E. reported
receiving grants from Instituto de Salud Carlos III, Uriach Laboratories, andGrand-Fountain Laboratories for clinical trial and personal fees from Brewersof Europe, Fundación Cerveza y Salud; Instituto Cervantes in Albuquerque,
Milano, and Tokyo; Fundación Bosch y Gimpera; non-financial support from
Wine and Culinary International Forum, ERAB (Belgium), and Sociedad Espa‑
ñola de Nutrición; and fees of educational conferences from Pernaud Richart(Mexico) and Fundación Dieta Mediterránea (Spain). R.C. reported receiving
fees of educational conferences from Fundación para la investigación delVino y la Nutrición (Spain). J.L.S. has received research support from the
Canadian Foundation for Innovation, Ontario Research Fund, Province ofOntario Ministry of Research and Innovation and Science, Canadian Institutesof health Research (CIHR), Diabetes Canada, American Society for Nutrition(ASN), International Nut and Dried Fruit Council (INC) Foundation, National
Honey Board (US Department of Agriculture [USDA] honey “Checkoff” pro‑gram), Institute for the Advancement of Food and Nutrition Sciences (IAFNS;
formerly ILSI North America), Pulse Canada, Quaker Oats Center of Excellence,
The United Soybean Board (USDA soy “Checkoff” program), The Tate and Lyle
Nutritional Research Fund at the University of Toronto, The Glycemic Control
and Cardiovascular Disease in Type 2 Diabetes Fund at the University ofToronto (a fund established by the Alberta Pulse Growers), The Plant Protein
Fund at the University of Toronto (a fund which has received contributions
from IFF), and The Nutrition Trialists Network Fund at the University of Toronto(a fund established by an inaugural donation from the Calorie Control Coun‑cil). He has received food donations to support randomized controlled trials
from the Almond Board of California, California Walnut Commission, Peanut
Institute, Barilla, Unilever/Upfield, Unico/Primo, Loblaw Companies, Quaker,
Kellogg Canada, WhiteWave Foods/Danone, Nutrartis, and Dairy Farmers
of Canada. He has received travel support, speaker fees, and/or honoraria
from ASN, Danone, Dairy Farmers of Canada, FoodMinds LLC, Nestlé, Abbott,
General Mills, Nutrition Communications, International Food Information
Council (IFIC), Calorie Control Council, International Sweeteners Association,and International Glutamate Technical Committee. He has or has had ad hoc
consulting arrangements with Perkins Coie LLP, Tate & Lyle, Phynova, andInquis Clinical Research. He is a former member of the European Fruit Juice
Association Scientific Expert Panel and a former member of the Soy Nutrition
Institute (SNI) Scientific Advisory Committee. He is on the Clinical Practice
Guidelines Expert Committees of Diabetes Canada, European Association
for the study of Diabetes (EASD), Canadian Cardiovascular Society (CCS), andObesity Canada/Canadian Association of Bariatric Physicians and Surgeons.
He serves or has served as an unpaid member of the Board of Trustees andan unpaid scientific advisor for the Carbohydrates Committee of IAFNS. He isa member of the International Carbohydrate Quality Consortium (ICQC), an
Executive Board Member of the Diabetes and Nutrition Study Group (DNSG)
of the EASD, and a Director of the Toronto 3D Knowledge Synthesis and Clini‑
cal Trials foundation. His spouse is an employee of AB InBev. J.S.-S. reported
receiving research support from the Instituto de Salud Carlos III, Ministeriode Educación y Ciencia, the European Commission, and the USA National
Institutes of Health; receiving consulting fees or travel expenses from Insti‑
tuto Danone and Abbott Laboratories; receiving nonfinancial support from
Patrimonio Comunal Olivarero, the Almond Board of California, and Pistachio
Growers and Borges S.A; serving on the board of and receiving grant support
through his institution from the International Nut and Dried Foundation andpersonal fees from Instituto Danone; and serving in the Board of Danone

Nishi et al. BMC Medicine(2023) 21:82

Institute International. The rest of the authors declared that they have nocompeting interests. The funders had no role in the design of the study; inthe collection, analyses, or interpretation of the data; in the writing of themanuscript; or in the decision to publish the results.
Author details
1
Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat
de Nutrició, Reus, Spain. 2 Institut d’Investigació Sanitària Pere Virgili (IISPV),
Reus, Spain. 3 Centro de Investigación Biomédica en Red Fisiopatología dela Obesidad y la Nutrición (CIBEROBN), Institute of Health Carlos III, Madrid,
Spain. 4 Toronto 3D (Diet, Digestive Tract and Disease) Knowledge Synthesis
and Clinical Trials Unit, Toronto, ON, Canada. 5 Clinical Nutrition and Risk Factor
Modification Centre, St. Michael’s Hospital, Unity Health Toronto, Toronto,
ON, Canada. 6 Research Institute of Biomedical and Health Sciences (IUIBS),
University of Las Palmas de Gran Canaria & Centro Hospitalario Universitario
Insular Materno Infantil (CHUIMI), Canarian Health Service, Las Palmas de Gran
Canaria, Spain. 7 CIBER de Epidemiología y Salud Pública (CIBERESP), Institutode Salud Carlos III (ISCIII), 28029 Madrid, Spain. 8 Instituto de Investigación
Sanitaria y Biomédica de Alicante. Universidad Miguel Hernández (ISABIALUMH), Alicante, Spain. 9 Unit of Cardiovascular Risk and Nutrition, Institut
Hospital del Mar de Investigaciones Médicas Municipal d’Investigació Médica
(IMIM), Barcelona, Spain. 10 Department of Preventive Medicine, Universityof Valencia, Valencia, Spain. 11 Lipids and Vascular Risk Unit, Internal Medicine,
Hospital Universitario de Bellvitge-IDIBELL, Hospitalet de Llobregat, Barcelona,
Spain. 12 School of Medicine, Universitat de Barcelona, 08907 Barcelona,
Spain. 13 Department of Preventive Medicine and Public Health, Universityof Granada, Granada, Spain. 14 Research Group on Community Nutrition &Oxidative Stress, University of Balearic Islands, 07122 Palma de Mallorca, Spain.
15
Nutritional Control of the Epigenome Group, Precision Nutrition and Obesity
Program, IMDEA Food, CEI UAM + CSIC, 28049 Madrid, Spain. 16 Departmentof Nutrition, Food Sciences, and Physiology, Center for Nutrition Research,
University of Navarra, IdiSNA, Pamplona, Spain. 17 Precision Nutrition and Car‑
diometabolic Health Program, IMDEA Food, CEI UAM + CSIC, Madrid, Spain.
18
Centro de Salud Raval de Elche, Alicante, Spain. 19 Instituto de Investigación
Biosanitaria Granada, IBS-Granada, Granada, Spain. 20 Departamento de Cien‑
cias Farmacéuticas y de la Salud, Facultad de Farmacia, Universidad San PabloCEU, CEU Universities, Urbanización Montepríncipe, Boadilla del Monte 28660,
Spain. 21 Department of Nutritional Sciences, Faculty of Medicine, Universityof Toronto, Toronto, ON, Canada. 22 Department of Medicine, Universityof Toronto, Toronto, ON, Canada. 23 Division of Endocrinology & Metabolism, St.
Michael’s Hospital, Toronto, ON, Canada. 24 Li Ka Shing Knowledge Institute, St.
Michael’s Hospital, Toronto, ON, Canada. 25 Departament of Occupational Risk
Prevention, Virgen de la Arrixaca’s Hospital (HCUVA), Murcia, Spain. 26 Depart‑
ment of Preventive Medicine and Public Health, Instituto de Investigación
Sanitaria de Navarra (IdiSNA), University of Navarra, Pamplona, Spain.
Received: 2 November 2022 Accepted: 6 February 2023

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