Carbohydrate Quality & Mortality: A Prospective Study

i An update to this article is included at the end
Clinical Nutrition 40 (2021) 2364e2372
Contents lists available at ScienceDirect
Clinical Nutrition
journal homepage: http://www.elsevier.com/locate/clnu
Original article
Association of carbohydrate quality and all-cause mortality in the SUN
Project: A prospective cohort study
Cesar I. Fernandez-Lazaro a, b, Itziar Zazpe a, b, c, d, Susana Santiago b, c,
lez a, b, d, e, *
Martínez-Gonza
Estefanía Toledo a, b, d, María Barbería-Latasa a, Miguel Angel
a
University of Navarra, Department of Preventive Medicine and Public Health, School of Medicine, 31008, Pamplona, Spain
IdiSNA, Navarra Institute for Health Research, 31008, Pamplona, Spain
University of Navarra, Department of Nutrition and Food Sciences and Physiology, School of Pharmacy and Nutrition, 31008, Pamplona, Spain
d
n Biom
n (CIBEROBN), 28029, Madrid, Spain
Centro de Investigacio
edica en Red Area
de Fisiología de la Obesidad y la Nutricio
e
Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
b
c
a r t i c l e i n f o
s u m m a r y
Article history:
Received 11 June 2020
Accepted 17 October 2020
Background & aims: Emerging evidence supports shifting the focus from carbohydrate quantity to carbohydrate quality to obtain greater health benefits. We investigated the association of carbohydrate
quality with all-cause mortality using a single, multidimensional carbohydrate quality index (CQI)
designed to account for multiple characteristics of carbohydrate quality.
Methods: A prospective study was conducted among 19,083 participants in the Seguimiento Universidad
de Navarra (SUN) Project, a Mediterranean cohort of middle-aged university graduates. The CQI was
based on four dimensions: high total dietary fiber intake, low glycemic index, high whole-grain
carbohydrate: total grain carbohydrate ratio, and high solid carbohydrate: total carbohydrate ratio.
Results: During 12.2 years of median follow-up, 440 deaths were identified. We found an inverse
association between the CQI and all-cause mortality. The multivariable-adjusted hazard ratio (HR) for the
highest vs. the lowest tertile of the CQI was 0.70 (95% CI, 0.53e0.93; Ptrend ¼ 0.018). However, each individual dimension of the CQI was not independently associated with lower mortality risk, with HR (95%
CI) between extreme tertiles as follows: 0.77 (0.52e1.14; Ptrend ¼ 0.192) for high fiber intake; 0.81 (0.59
e1.12; Ptrend ¼ 0.211) for low glycemic index; 0.87 (0.69e1.11; Ptrend ¼ 0.272) for high whole-grain
carbohydrate: total-grain carbohydrate ratio; and 0.81 (0.61e1.07; Ptrend ¼ 0.139) for high solid carbohydrate: total carbohydrate ratio. Our analyses remained similar after using repeated measurements of
diet with updated nutritional exposures after a ten-year follow-up.
Conclusions: The CQI as a whole, but none of its individual dimensions, was associated with lower
mortality. The CQI seems to comprehensively capture the combined effects of quality domains.
© 2020 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
Keywords:
Carbohydrate
Mortality
Carbohydrate quality index
Dietary patterns
SUN cohort
Mediterranean diet
1. Introduction
Carbohydrates are the primary source of energy intake in the
human diet and contain about four calories per gram. Over the last
Abbreviations: CI, confidence interval; CQI, carbohydrate quality index; CVD,
cardiovascular disease; FFQ, food-frequency questionnaire; HR, hazard ratio; IQR,
n con Dieta Mediterra
nea; PURE, Prointerquartile range; PREDIMED, Prevencio
spective Urban Rural Epidemiology; RERI, relative excess risk due to interaction; SD,
standard deviation; SUN, Seguimiento Universidad de Navarra; WHO, World Health
Organization.
* Corresponding author. University of Navarra, Department of Preventive Medicine and Public Health, C/ Irunlarrea, 31008, Pamplona, Spain.
Martínez-Gonza
lez).
E-mail address: [email protected] (M.A.
decades, dietary recommendations, mainly based on decreasing fat
intake in exchange for higher carbohydrate intake [1], have resulted
in disappointing outcomes [2]. Furthermore, public health nutritional policies have focused on macronutrient quantity [3], setting
quantity-based goals for carbohydrate intake. Recent recommendations from the World Health Organization (WHO) have strongly
suggested decreasing free sugar intake to less than 10% of total
energy, with even greater benefits from an intake less than 5% of
total energy [4]. In this regard and within the context of the obesity
epidemic, carbohydrate-strict diets, such as the ketogenic diet, have
attracted much attention due to their apparent rapid weight-loss
results [5], although they are not exempt of potential adverse
effects [6].
https://doi.org/10.1016/j.clnu.2020.10.029
0261-5614/© 2020 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
C.I. Fernandez-Lazaro, I. Zazpe, S. Santiago et al.
Clinical Nutrition 40 (2021) 2364e2372
The participants’ dietary information collected in the FFQ was
used to calculate the CQI. Details about the construction of the CQI
have been described elsewhere [11,14e17]. Briefly, the CQI is
constructed upon four carbohydrate quality domains: total dietary
fiber intake (g/d), glycemic index, ratio of carbohydrates from
whole grains to carbohydrates from total grains (whole
grains þ refined grains or their products), and ratio of carbohydrates from solid foods to total carbohydrates (solid
carbohydrates þ liquid carbohydrates). Liquid carbohydrates were
calculated by summing up sugar-sweetened beverages, the consumption of fruit juices, and alcohol.
To calculate the CQI, we first categorized participants into
quintiles for each component. Each component was then assigned a
score ranging from 1 point (first quintile) to 5 points (fifth quintile)
for the fiber intake (sex-specific quintiles), whole-grain carbohydrate: total-grain carbohydrate ratio, and solid carbohydrate: total
carbohydrate ratio components. The glycemic index dimension was
reversely scored, i.e., we assigned 1 point for participants categorized in the fifth quintile and 5 points for those in the first quintile.
Lastly, the values of each domain were summed together to
calculate the CQI score, ranging from 4 (lowest carbohydrate
quality) to 20 points (highest carbohydrate quality).
To minimize any effect from within-person variation in diet, we
used repeated measurements using both updated data and cumulative average information of the CQI after ten years of follow-up.
From a public health perspective, current mainstream nutrition
research has suggested shifting the focus from carbohydrate
quantity to carbohydrate quality [3,5]. Studies that have evaluated
the relationship between carbohydrate quality and different health
outcomes, including chronic diseases, certain types of cancer, and
mortality risk, provide evidence for this viewpoint [7e9]. Importantly, a series of systematic reviews reported a strong inverse association between fiber intake and whole-grain consumption, and
the risk of mortality [10]. Similar inverse associations were found
for the incidence of several non-communicable diseases and their
risk factors. However, such evidence was not reported for glycemic
index and glycemic load [10].
Beyond analyses of individual elements of carbohydrate quality,
Zazpe et al. [11] integrated four carbohydrate quality dimensions
ddietary fiber, the proportion of whole-grain carbohydrates
(compared with total grain carbohydrates), low glycemic index, and
the proportion of solid carbohydrates (compared with liquid carbohydrates) d into a single dietary quality index under the hypothesis that additivity and synergy exist across components
[12,13]. This multidimensional score was named the carbohydrate
quality index (CQI). Inverse associations between the CQI and
micronutrient intake inadequacy [11], obesity [14], incidence of
cardiovascular disease [15], and breast cancer [16] have been previously reported. In the PREDIMED-Plus study, improvements in
the CQI were instrumental in attaining reductions in cardiovascular
risk factors after 1-year of a dietary intervention [17,18]. To date, no
studies have assessed the relationship between dietary quality of
carbohydrates and all-cause mortality using this multidimensional
index. Thus, we aimed to investigate the association of the CQI with
all-cause mortality in a cohort of Mediterranean middle-aged
adults.
2.3. Outcome assessment
All-cause mortality was our primary outcome. The SUN Project
keeps a close, active, and permanent follow-up that allows us to
continuously update participants' information and quickly identify
incident diseases or new cases of mortality. For this study, around
75% of deaths were reported by next-of-kin, work colleagues, or the
authorities' postal system, and were confirmed by medical records
after consent. The remaining cases were confirmed after checking
the Spanish National Death Index and the National Statistics Institute. Contact with the Spanish Governmental Institutions occurred
at least once a year in which the authorities provided participants’
information regarding mortality and cause of death.
2. Methods
2.1. Study design and population
The Seguimiento Universidad de Navarra (SUN) Project (www.
proyectosun.es) is a prospective, dynamic, multipurpose, and
permanently open cohort comprised of more than 22,000 Spanish
university graduates that began open enrollment in 1999. The main
objective was to evaluate the impact of diet and lifestyle on the
prevention of non-communicable diseases and mortality. Participants’ information on diet, lifestyle, risk factors, and medical conditions has been biennially gathered through mailed or online
questionnaires. More information about the design, objectives, and
methods of the study have been previously described [19]. By
December 2019, the SUN Project had enrolled a total of 22,894
participants. For the present study, we excluded participants
recruited after March 2017 (n ¼ 341) to ensure a minimum followup of two years, individuals without follow-up (n ¼ 1,478, retention
rate 90.6%), and those who showed an energy intake beyond predefined limits (n ¼ 1,992), set as <500 or >3500 kcal/d for women,
and <800 or >4000 kcal/d for men [20].
The Institutional Review Board of the University of Navarra
approved the study protocol before any data collection (approval
code 010830). The SUN project was conducted according to the
principles expressed in the Declaration of Helsinki. Informed consent to participate in the study was obtained according to the
protocol approved by the Institutional Review Board.
2.4. Assessment of other covariates
Socio-demographic characteristics, anthropometric measures,
lifestyle behavior (including smoking and alcohol intake), and
medical history were collected. A previously validated questionnaire was used to evaluate physical activity [24] and a subsample of
the SUN cohort was used to assess the accuracy of self-reported
weight and height [25]. Adherence to the Mediterranean Diet was
assessed using the score proposed by Trichopoulou et al. [26].
2.5. Statistical analysis
We used inverse probability weighting to adjust baseline variables for age and sex and present the corresponding means and
proportions according to tertiles of the CQI. We calculated hazard
ratios (HRs) and 95% confidence intervals (CIs) using crude and
multivariable Cox regressions models to examine the association of
the CQI and its components with all-cause mortality. All models
(except crude models) included age as the underlying time variable
(birth date as origin) and were stratified by recruitment period (five
categories) and deciles of age. We considered the lowest tertile as the
reference category for all scenarios. Time at entry was considered as
the date of completion of the baseline questionnaire, whereas exit
time was the date of death or the date when the last follow-up
questionnaire was completed. Follow-up for each participant was
2.2. Dietary assessment and carbohydrate quality index (CQI)
Dietary habits at baseline and after ten years of follow-up were
assessed with a semi-quantitative 136-item food-frequency questionnaire (FFQ) repeatedly validated in Spain [21e23].
2365
C.I. Fernandez-Lazaro, I. Zazpe, S. Santiago et al.
Clinical Nutrition 40 (2021) 2364e2372
3. Results
determined from the date when the baseline questionnaire was
answered to the date of death or the date when the last follow-up
questionnaire was returned, for whom death was not reported. To
reduce measurement error, we used repeated dietary measurements to obtain a more realistic assessment of long-term diet during
follow-up using updated data and cumulative averages after a 10year follow-up. Information on 10-year dietary measurements was
available for 11,833 (62.0%) participants. We examined total mortality according to quintiles of CQI and merged the three intermediate quintiles of CQI in one group, resulting in three categories
(quintile 1, quintiles 2e4, and quintile 5).
After crude analyses, we fitted several multivariate Cox
regression models with adjustment for potential confounders:
model 1 adjusted for age (underlying time variable), sex and
total energy intake (kcal/d, continuous); model 2 additionally
adjusted for alcohol intake (never, <5 women or <10 men g/d,
5e25 women or 10e50 men g/d, and >25 women or >50 men g/
d), body-mass index (kg/m2 , linear and quadratic terms),
following special diet at baseline (yes/no), educational level
(years of higher education, continuous), leisure-time physical
activity (metabolic equivalent-h/week, continuous), marital
status (single, married, widowed, and others), smoking (never,
current, and former smoker), cumulative smoking habit
(smoking packs-years, continuous), time spent sitting (hours/
week, continuous), and weight gain in the previous 5 years
before entering the cohort (<3 kg and 3 kg); model 3 further
adjusted for family history of cardiovascular disease (CVD) (yes/
no), and pre-existent (at baseline) medical diagnoses of cancer,
cardiovascular disease, depression, diabetes, hypertension, hypercholesterolemia and hypertriglyceridemia; and model 4
additionally adjusted for intakes (all continuous) of animal
protein (g/d), plant protein (g/d), monounsaturated fatty acids
(%E), polyunsaturated fatty acids (%E), saturated fatty acids (%E),
trans fatty acids (%E, continuous), and for snacking between
meals (yes/no). Confounders were identified a priori based on
prior knowledge according to existing scientific literature and
previous findings in the SUN cohort [27]. We calculated linear
trends across tertiles and quintiles of the CQI and its components by assigning the median value to each tertile (or quintile)
and considering the resulting variables as continuous.
Additionally, we tested for effect modification between carbohydrate quality (tertiles of CQI) and quantity of carbohydrates (two
categories of percentage of energy from carbohydrate intake, below
and above the median) on mortality by calculating both multiplicative and additive interactions. For multiplicative interaction, effect modification was examined by introducing interaction terms in
the multivariate-adjusted models. For additive interaction, we
calculated the relative excess risk due to interaction (RERI) [28]. The
findings were presented following the recommendations of Knol
and Vanderweele [29], by representing the proportion of participants within each subgroup and the joint effect of the six possible
combinations of tertiles of CQI and low and high exposure to carbohydrate intake (as percent of total energy intake) on mortality.
Stratification analyses were additionally conducted to test the effect modification by assessing each exposure stratified by the other
exposure.
Sensitivity analyses were performed to assert the robustness of
our findings by limiting our models to different subgroups and
diverse a priori assumptions.
All analyses were performed with STATA version 15 (STATA
Corp., TX, USA) with the SUN database updated in December 2019.
A 2-sided p-value <0.05 was deemed as statistically significant.
A total of 19,083 participants (11,429 [59.5%] female) were
included in the present analyses (Fig. 1). The mean age was 38.4 (SD
12.4) years and the median follow-up was 12.2 years (IQR
8.3e14.9). Overall, during 219,887 person-years of follow-up, 440
total deaths were documented, including 84 (19.1%) deaths from
cardiovascular disease, 226 (51.4%) deaths from cancer, and 119
(27.1%) from other causes. Only 11 deaths (2.5%) were reported with
unknown causes. Table 1 shows the characteristics of participants
at baseline according to age- and sex-adjusted tertiles of CQI.
The overall CQI was inversely associated with all-cause mortality in crude and all multivariable-adjusted models (Table 2).
Based on the fully adjusted model (model 4), participants in the
highest tertile of CQI had 30% relatively lower risk of all-cause
mortality as compared with participants in the lowest tertile HR
(95% CI) of 0.70 (0.53e0.93) with a significant inverse
doseeresponse relation (Ptrend ¼ 0.018). When repeated measurements after a 10-year follow-up were used to update dietary exposures, the results barely changed (Table 3). Risk reductions
remained significant for participants in the highest tertile of the
CQI, with a multivariable-adjusted HR (95% CI) of 0.70 (0.54e0.93;
Ptrend ¼ 0.016) when using updated dietary information and 0.70
(0.53e0.92; Ptrend ¼ 0.017) for the cumulative average, respectively.
We explored the association between quintiles of CQI and allcause mortality. Results were consistent with previous findings
with significant risk reductions for mortality when comparing the
highest vs. the lowest quintile of the CQI, with multivariableadjusted HRs (95% CI) of 0.67 (0.47e0.97; Ptrend ¼ 0.025) in the
main analyses; 0.69 (0.48e0.98; Ptrend ¼ 0.029) for updated dietary
information; and 0.66 (0.46e0.96; Ptrend ¼ 0.015) for cumulative
average dietary information (Supplemental Table 1 and
Fig. 1. Flow-chart of participants. The Seguimiento Universidad de Navarra (SUN)
Project, 1999e2019.
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C.I. Fernandez-Lazaro, I. Zazpe, S. Santiago et al.
Clinical Nutrition 40 (2021) 2364e2372
Table 1
Age and sex adjusteda baseline characteristics of participants according to tertiles of the carbohydrate quality index (CQI): the Seguimiento Universidad de Navarra (SUN)
cohort: 1999e2019b.
Characteristics
T1
T2
T3
n (frequency)
CQI range
Body mass index (kg/m2)
Physical activity (METs-h/week)
Time spent sitting (h/d)
Marital status
Single
Married
Widowed
Others
Cumulative smoking habit (packs-years)
Smoking (%)
Never smoker
Current smoker
Former smoker
Alcohol intake (g/d)
Never
<5 women/<10 men
5e25 women/10e50 men
>25 women/>50 men
Snacking between meals (% Yes)
Special diet (% Yes)
Years at university
Medically-diagnosed conditions at baseline
Prevalent diabetes (%)
Prevalent hypertension (%)
Prevalent dyslipemia (%)
Prevalent cardiovascular disease (%)c
Prevalent cancer (%)
Dietary variables
Trichopoulou's MedDiet scored
Total energy intake (kcal/d)
Carbohydrate intake, % E
Solid carbohydrate intake (g/d)
Liquid carbohydrate intake (g/d)
Carbohydrate from whole grains intake (g/d)
Carbohydrate from refined grains intake (g/d)
Fiber intake (g/d)
Glycemic index
8199
4e10
23.5 (3.6)
18.7 (20.1)
5.4 (2.1)
5978
11e13
23.6 (3.5)
22.1 (22.8)
5.2 (2.0)
4906
14e20
23.6 (3.5)
26.5 (26.8)
5.2 (2.1)
43.3
51.6
1.0
4.2
6.4 (10.4)
43.2
50.9
0.8
5.1
5.7 (9.5)
45.0
48.9
1.1
5.1
5.2 (8.9)
45.2
25.4
28.6
48.8
20.4
30.1
50.7
17.8
30.6
18.1
47.0
32.5
2.4
34.0
6.0
5.0 (1.5)
17.7
49.1
31.5
1.7
33.5
8.3
5.1 (1.5)
17.4
50.8
30.6
1.3
30.7
12.8
5.1 (1.6)
1.7
11.0
7.1
4.7
2.5
1.9
10.9
6.9
4.5
2.5
2.6
12.1
6.9
4.8
2.7
3.1 (1.5)
2216 (607)
42.5 (7.4)
201 (73.1)
35.8 (22.3)
0.5 (3.4)
90.6 (52.1)
20.5 (6.8)
53.0 (4.9)
3.9 (1.6)
2409 (624)
43.4 (7.3)
231 (79.7)
31.5 (20.3)
4.8 (11.2)
92.0 (55.7)
29.2 (9.8)
51.6 (4.5)
4.7 (1.5)
2480 (607)
44.5 (7.6)
249 (79.8)
27.7 (19.2)
18.4 (23.3)
77.3 (47.5)
38.7 (13.1)
50.7 (4.3)
Abbreviations: CQI, Carbohydrate Quality Index; MedDiet, Mediterranean diet; METs, metabolic equivalents; T, tertile.
a
Adjusted through inverse probability weighting.
b
Values are means ± SDs or numbers of participants (percentages) unless otherwise indicated.
c
Prevalent cardiovascular disease was considered as having at least one of the following events before entering the cohort: myocardial infarction, stroke, angina pectoris,
coronary bypass, tachycardia, atrial fibrillation, aneurysm, cardiac insufficiency, pulmonary embolism, deep vein thrombosis, or intermittent claudication.
d
Adherence to the Mediterranean Diet was assessed using the score proposed by Trichopoulou et al. [26].
mortality for the combination of high percentage of carbohydrate
intake and low quality of carbohydrate (T1) with HRs (95% CI) of
1.67 (1.08e2.59). Significant associations were observed between
medium (T2) and low (T1) quality of carbohydrate intake among
those participants with high percentage of energy from carbohydrate intake with HRs (95% CI) of 1.45 (1.01e2.08) and 1.54
(1.05e2.25), respectively. Neither interactions on the multiplicative
scale (p ¼ 0.852 and p ¼ 0.453) nor the relative risk due to interaction on the additive scale (RERI:0.08; 95%CI: 0.22 to 0.37;
p ¼ 0.601 and RERI:0.09; 95%CI: 0.27 to 0.44; p ¼ 0.641) were
statistically significant.
Sensitivity analyses were conducted to assess the robustness of
our results comparing the highest vs. the lowest tertile of CQI in
several scenarios (Fig. 3). Overall, results revealed a persistent association between higher CQI and lower mortality risk. Most of
these associations resulted significant under different assumptions
and within different subgroups.
Supplemental Table 2). When we assessed specific causes of
mortality (cardiovascular mortality, cancer mortality, and other
causes) all point estimates for the HRs comparing extreme tertiles
were below 1 (cardiovascular 0.62; cancer 0.79; other causes
0.56); however, because of the small number of deaths within each
category, the confidence intervals were very wide (data not
shown).
Fig. 2 shows the risk of all-cause mortality during follow-up
when comparing extreme tertiles of each of the four individual
elements contributing to the CQI. A non-significant inverse association was observed between each of the four dimensions with allcause mortality, with multivariable-adjusted HRs (95% CI) of 0.77
(0.52e1.14; Ptrend ¼ 0.192) for high fiber intake, 0.81 (0.59e1.12;
Ptrend ¼ 0.211) for low glycemic index, 0.87 (0.69e1.11;
Ptrend ¼ 0.272) for high whole-grain carbohydrate: total-grain carbohydrate ratio, and 0.81 (0.61e1.07; Ptrend ¼ 0.139) for high solid
carbohydrate: total carbohydrate ratio.
The HR for all-cause mortality according to the six possible effects created between tertiles of CQI and low and high percentage
of energy from total carbohydrate intake (below and above the
median, 43%), i.e., quality and quantity of carbohydrates, is shown
in Table 4. The combined effects showed the highest risk on
4. Discussion
We examined the association of dietary carbohydrate quality
and the risk of all-cause mortality in a Mediterranean cohort of
2367
C.I. Fernandez-Lazaro, I. Zazpe, S. Santiago et al.
Clinical Nutrition 40 (2021) 2364e2372
Table 2
Association between tertiles (T) of the Carbohydrate Quality Index (CQI) and all-cause mortality in the SUN cohort (n ¼ 19,083). Hazard ratios (HR) and 95% confidence intervals
(CI).
n (frequency)
CQI range
Deaths
Person-years
Mortality rate/1000 person years
Crude model
Model 1
Model 2
Model 3
Model 4
T1
T2
T3
p for trend
8199
4e10
193
96,905
1.99
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
5978
11e13
150
68,879
2.18
1.07 (0.86e1.32)
0.91 (0.73e1.14)
1.00 (0.80e1.25)
1.01 (0.81e1.26)
0.99 (0.78e1.25)
4906
14e20
97
54,103
1.79
0.87 (0.68e1.12)
0.64 (0.50e0.83)
0.72 (0.56e0.94)
0.71 (0.55e0.93)
0.70 (0.53e0.93)
0.383
0.001
0.022
0.019
0.018
Abbreviations: CQI, Carbohydrate Quality Index; Ref., referent value; T, tertile.
Model 1: adjusted for age (underlying variable), sex (dichotomous), and total energy intake (kcal/d, continuous) stratified by deciles of age and recruitment period (5 categories).
Model 2: additionally adjusted for alcohol intake (four categories), BMI (kg/m2, linear and quadratic terms, continuous), following special diet at baseline (dichotomous),
educational level (years of higher education, continuous), leisure-time physical activity (metabolic equivalent-h/week, continuous), marital status (four categories), smoking
(three categories), smoking habit (package/year, continuous), time spent sitting (hours/week, continuous), and weight gain in the previous 5 years before entering the cohort
(3 kg, dichotomous).
Model 3: additionally adjusted for family history of CVD (dichotomous), prevalent cancer (dichotomous), prevalent cardiovascular disease (dichotomous), prevalent
depression (dichotomous), prevalent diabetes (dichotomous), prevalent hypercholesterolemia (dichotomous), prevalent hypertension (dichotomous), and prevalent
hypertriglyceridemia (dichotomous).
Model 4: additionally adjusted for animal protein (g/d, continuous), plant protein (g/d, continuous), monounsaturated fatty acids (%E, continuous), polyunsaturated fatty acids
(%E, continuous), saturated fatty acids (%E, continuous), snacking (dichotomous), and trans fatty acids (%E, continuous).
when we updated dietary information after ten years of follow-up.
Yet, none of the single components of the CQI was significantly
associated with lower mortality risk.
Nowadays, the role of carbohydrate quality and quantity for
determining population health outcomes is a subject of debate in
nearly 20,000 adults, with 12 years of median follow-up. After
multivariable adjustment for well-known risk factors, we found
that higher quality of carbohydrate intake, assessed with a multidimensional CQI based on four domains, was associated with lower
mortality in the long term. This association remained significant
Table 3
Repeated nutritional measurements. Association between tertiles (T) of the Carbohydrate Quality Index (CQI) and all-cause mortality in the SUN cohort, using repeated dietary
measurements after 10 years, using each of both approaches: updated dieta and cumulative diet average of baseline and 10-year informationb. Hazard ratios (HR) and 95%
confidence intervals (CI).
Updated Dieta
T1
T2
T3
p for trend
CQI range
Deaths
Person-years
Mortality rate/1000 person years
Crude model
Model 1
Model 2
Model 3
Model 4
4e10
192
94,511
2.03
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
11e13
147
69,019
2.13
1.01 (0.82e1.25)
0.90 (0.72e1.12)
0.98 (0.78e1.22)
0.99 (0.79e1.24)
0.97 (0.77e1.22)
14e20
101
56,357
1.79
0.83 (0.65e1.05)
0.65 (0.50e0.83)
0.72 (0.56e0.94)
0.72 (0.56e0.93)
0.70 (0.54e0.93)
0.162
0.001
0.019
0.017
0.016
Cumulative Diet Averageb
T1
T2
T3
p for trend
CQI range
Deaths
Person-years
Mortality rate/1000 person years
Crude model
Model 1
Model 2
Model 3
Model 4
4e10
188
94,379
1.99
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
10.5e13
155
70,569
2.20
1.05 (0.85e1.30)
0.93 (0.75e1.16)
1.02 (0.81e1.27)
1.03 (0.83e1.29)
1.01 (0.80e1.28)
13.5e20
97
54,939
1.77
0.84 (0.66e1.08)
0.64 (0.49e0.82)
0.72 (0.55e0.93)
0.71 (0.55e0.92)
0.70 (0.53e0.92)
0.236
0.001
0.020
0.018
0.017
Abbreviations: CQI, Carbohydrate Quality Index; Ref., referent value; T, tertile.
Model 1: adjusted for age (underlying variable), sex (dichotomous), and total energy intake (kcal/d, continuous) stratified by deciles of age and recruitment period (5 categories).
Model 2: additionally adjusted for alcohol intake (four categories), BMI (kg/m2, linear and quadratic terms, continuous), following special diet at baseline (dichotomous),
educational level (years of higher education, continuous), leisure-time physical activity (metabolic equivalent-h/week, continuous), marital status (four categories), smoking
(three categories), smoking habit (package/year, continuous), time spent sitting (hours/week, continuous), and weight gain in the previous 5 years before entering the cohort
(dichotomous).
Model 3: additionally adjusted for family history of CVD (dichotomous), prevalent cancer (dichotomous), prevalent cardiovascular disease (dichotomous), prevalent
depression (dichotomous), prevalent diabetes (dichotomous), prevalent hypercholesterolemia (dichotomous), prevalent hypertension (dichotomous), and prevalent
hypertriglyceridemia (dichotomous).
Model 4: additionally adjusted for animal protein (g/d, continuous), plant protein (g/d, continuous), monounsaturated fatty acids (%E, continuous), polyunsaturated fatty acids
(%E, continuous), saturated fatty acids (%E, continuous), snacking (dichotomous), and trans fatty acids (%E, continuous).
a
Repeated measures: update information of CQI after 10 years of follow-up.
b
Repeated measures: cumulative average information of CQI (at baseline and after 10 years of follow-up).
2368
C.I. Fernandez-Lazaro, I. Zazpe, S. Santiago et al.
Clinical Nutrition 40 (2021) 2364e2372
Fig. 2. Association between the Carbohydrate Quality Index (CQI) and its components d dietary fiber intake, glycemic index, whole-grain carbohydrate: total-grain carbohydrate
ratio, and solid carbohydrate: total carbohydrate ratio d for the highest tertile (T3) compared with the lowest tertile (T1) and all-cause mortality in the SUN cohort. Hazard ratios
(HR) and 95% confidence intervals (CI). Model adjusted for baseline alcohol intake (four categories), animal protein (g/d, continuous), BMI (kg/m2, linear and quadratic terms),
following special diet at baseline (dichotomous), educational level (years of higher education, continuous), energy intake (kcal/d, continuous), family history of CVD (dichotomous),
leisure-time physical activity (metabolic equivalent-h/week, continuous), marital status (four categories), MUFA (%E, continuous), plant protein (g/d, continuous), prevalent cancer
(dichotomous), prevalent cardiovascular disease (dichotomous), prevalent depression (dichotomous), prevalent diabetes (dichotomous), prevalent hypercholesterolemia (dichotomous), prevalent hypertension (dichotomous), prevalent hypertriglyceridemia (dichotomous), polyunsaturated fatty acids (%E, continuous), sex (dichotomous), saturated fatty
acids (%E, continuous), smoking (three categories), smoking habit (package/year), trans fatty acids (%E, continuous), time spent sitting (hours/week), and weight gain in the previous
5 years before entering the cohort (3 kg, dichotomous), and stratified by deciles of age and recruitment period (5 categories). Additionally, when an individual component of the
CQI was the exposure of interest, the model was mutually adjusted for the other 3 components of the CQI (as continuous variables).
approach to assess overall carbohydrate quality. This concept is
most similar to other nutritional assessments which consider
food intake as a multidimensional exposure created by mutual
effects from different single components interacting with each
other [12,13]. The overall quality of carbohydrates should be
conceptualized as a whole food matrix taking into account all its
domains. Thus, the interaction of different dietary components
may lead to greater effects than the corresponding effects of
single components individually. Such premise is exemplified in
the meta-analyses conducted by Hardy et al. [33] who reported
that the positive effects of total fiber on type 2 diabetes were
nullified after adjustments were made for glycemic index and
load. These authors additionally pointed out that cereal fiber may
have stronger positive effects on type 2 diabetes as compared to
total dietary fiber, mainly because cereal fiber can produce a
longer delay of carbohydrate absorption, and consequently produce lower glycemic response [34]. Hence, our presumption of
synergism and additivity when measuring overall carbohydrate
quality in a multidimensional index may explain why we found
no significant associations between each of the four components
of the CQI and mortality, but did find a significant association for
the overall CQI.
Our findings were in accordance with previous observational
studies that identified unhealthy low-carbohydrate diets with
higher mortality [8]. The results of the joint classification of our
study revealed that higher carbohydrate intake and lowest CQI
were significantly associated with higher mortality when
compared with participants with the lowest carbohydrate intake
and highest CQI. Such results suggest a call for caution with the use
public health. On the one hand, existing evidence suggests that
high carbohydrate intake may have a negative impact on mortality
risk [30,31]. Accordingly, the authors of the large international
Prospective Urban Rural Epidemiology (PURE) study concluded that
high carbohydrate intake (defined as more than 60% of total energy
intake) was associated with a higher risk of total and noncardiovascular disease mortality [30]. However, the methodology
of this study has been questioned [31], primarily due to the lack of
control for any measure of carbohydrate quality. Additionally, it is
known that the main sources of this macronutrient in middle- and
low-income countries are generally refined carbohydrates with low
nutritional quality.
On the other hand, and contrary to previous evidence, some
authors have advocated for carbohydrate quality as a major
driver for human health, independently of the amount of carbohydrate intake, arguing that “not all carbohydrates are the
same” [3,5]. In this direction, Seidelmann et al. [7] showed that
the food sources of macronutrients strongly influenced the association between carbohydrate intake and mortality. Moreover,
Reynold al [10]. concluded that carbohydrate quality, rather than
quantity, determines major health outcomes. These authors
highlighted the utility of fiber and whole grains to assess carbohydrate quality, but doubted the utility of glycemic index/load.
A major strength of that systematic review and meta-analyses
was to consider several isolated markers of carbohydrate quality. However, the authors used unidimensional indicators, and it
is generally accepted that the overall quality of carbohydrates is
composed of various domains or dimensions [32]. Hence, the use
of individual indicators may be considered, a priori, a simplistic
2369
C.I. Fernandez-Lazaro, I. Zazpe, S. Santiago et al.
Clinical Nutrition 40 (2021) 2364e2372
Table 4
Percentage (%) of participants, joint effect hazard ratio (HR), stratification, multiplicative and additive interactions using relative excess risk due to interaction (RERI) of
carbohydrate quality (carbohydrate quality index) and carbohydrate quantity (percentage of energy from carbohydrates) on mortality.
Mortality
Carbohydrate Quality Index (CQI)
Tertile 3
Energy from
carbohydrate
intake
HRs (95%CI)3 for
energy from
carbohydrates
within strata
of CQI
Lowb
High
a
Participants
(%)
HR (95%CI)a
2,155
(11.3%)
2,751
(14.4%)
1.00 Ref.
HRs (95%CI)c for
T2 of CQI within
strata of energy
from
carbohydrate
intake
Tertile 2
Tertile 1
Participants HR (95%CI)a
(%)
Participants HR (95%CI)a
(%)
2,968
(15.6%)
1.20 (0.75 to 1.91); 3,010
p ¼ 0.453
(15.8%)
1.13 (0.74 to 1.71);
p ¼ 0.570
1.47 (0.98 to 2.21); 4,418
p ¼ 0.062
(23.2%)
1.64 (1.06 to 2.56); 3,781
p ¼ 0.028
(19.8%)
1.23 (0.76 to 2.01);
p ¼ 0.400
HRs (95%CI)c
for T1 of CQI
within strata
of energy from
carbohydrate
intake
1.46 (0.98 to 2.19); 1.33 (0.87 to 2.03); 1.29 (0.82 to 2.01);
p ¼ 0.063
P ¼ 0.181
P ¼ 0.272
1.67 (1.08 to 2.59); 1.45 (1.01 to 2.08); 1.54 (1.05 to 2.25);
p ¼ 0.022
p ¼ 0.043
p ¼ 0.027
1.42 (0.75 to 2.68);
p ¼ 0.283
Measure of interaction on additive scale: Synergy index RERI 0.08 (0.22 to 0.37); p ¼ 0.601 0.09 (-0.27 to 0.44); p ¼ 0.641
(95%CI)
Measure of interaction on multiplicative scale: Ratio of HR
1.05 (0.63e1.5); p ¼ 0.852
1.07 (0.63e1.83); p ¼ 0.453
(95%CI)
Abbreviations: CI, confidence intervals; HR, hazards ratio; RERI: relative excess risk due to interaction.
*Boldface indicates statistical significance.
a
High energy from carbohydrates intake: percentage of energy from carbohydrate intakes above the median.
b
Low energy from carbohydrates intake: percentage of energy from carbohydrate intakes below the median.
c
Multivariable Cox regressions models adjusted for baseline alcohol intake (four categories), animal protein (g/d, continuous), BMI (kg/m2, linear and quadratic terms,
continuous), following special diet at baseline (dichotomous), educational level (years of higher education, continuous), energy intake (kcal/d, continuous), family history of
CVD (dichotomous), leisure-time physical activity (metabolic equivalent-h/week, continuous), marital status (four categories), MUFA (%E, continuous), plant protein (g/d,
continuous), prevalent cancer (dichotomous), prevalent cardiovascular disease (dichotomous), prevalent depression (dichotomous), prevalent diabetes (dichotomous),
prevalent hypercholesterolemia (dichotomous), prevalent hypertension (dichotomous), prevalent hypertriglyceridemia (dichotomous), polyunsaturated fatty acids (%E,
continuous), sex (dichotomous), saturated fatty acids (%E, continuous), smoking (three categories), smoking habit (package/year, continuous), time (underlying variable), trans
fatty acids (% E, continuous), time spent sitting (hours/week, continuous), and weight gain in the previous 5 years before entering the cohort (dichotomous), and stratified by
deciles of age and recruitment period (5 categories).
potential confounding. Fourth, the characteristics of participants
in our cohort (relatively-young Mediterranean university graduates) might presumably have limited the number of observed
casualties, which may have attenuated the statistical power in
our findings; yet, our main results were statistically significant,
consistent with previous studies, and supported by sensitivity
analyses. Moreover, we believe that the replication of our analyses in other independent cohorts with older participants who
have more heterogeneous diets may strengthen our findings by
increasing the contrast between extreme tertiles of carbohydrate
quality. In turn, the high educational level and homogeneity of
our cohort leads to greater reliability of the self-reported information, reducing the potential confounding related to educational level and socioeconomic status, as well as increasing the
internal validity of these results.
Strengths of our study include the use of a large populationbased cohort, a high retention rate, long average follow-up of participants, and application of a CQI that has been previously reported
in the scientific literature that allows accounting for multiple carbohydrate quality dimensions and has demonstrated consistency in
associations with different health outcomes [11,14e17]. Other
strengths include the verification of cases of mortality by medical
records or consultation of the National Death Index, use of validated methods, the ability to adjust for multiple potential confounders due to the ample information collected, and utilization of
repeated dietary measurements (frequently absent in nutritional
epidemiology) and numerous sensitivity analyses to confirm the
robustness of our findings. To our knowledge, the novelty of our
of low-carbohydrate diets such as ketogenic, particularly when
carbohydrate intake is of poor-quality.
The results of our study should certainly be translated to the
general population. Recommendations to improve the quality,
rather than limit the amount or percentage of total energy from
carbohydrates, can in great part be accomplished by increasing
dietary fiber intake, consuming foods with low glycemic index,
reducing refined grains in favor of whole grains, and avoiding liquid
carbohydrates. The potential health impacts of such changes have
been demonstrated in the PREDIMED-Plus trial, in which changes
in carbohydrate quality intake, achieved through an intervention
fostering adherence to Mediterranean diet, were positively associated with reductions in a wide range of cardiovascular risk factors
[17,18]. The greatest benefits observed in regards to risk factor
changes after one year of intervention were observed for those
participants with the greatest concurrent changes in the CQI.
We acknowledge several limitations in this study. We are
aware that self-reported methods of nutritional assessment are
subject to some degree of measurement bias; however, the FFQ
used in this study has been repeatedly validated [21e23], and
participants with energy intakes outside predefined limits were
excluded [20]. Second, dietary intake information was collected
at baseline, and participants might have modified their diet
during follow-up. Nevertheless, we updated dietary intake after
10-years of follow-up with two different approaches. Third, the
absence of confounding in studies of associations between a
certain exposure and outcome cannot be assumed; yet, we
adjusted for several co-variates to minimize, as much as possible,
2370
C.I. Fernandez-Lazaro, I. Zazpe, S. Santiago et al.
Clinical Nutrition 40 (2021) 2364e2372
Fig. 3. Sensitivity analysis1 e association between the Carbohydrate Quality Index (CQI) and total mortality when comparing the highest tertile (T3) versus the lowest tertile (T1) of
the CQI in the SUN cohort. Hazard ratios (HR) and 95% confidence intervals (CI) under different assumptions.
study lies in being the first study to assess the association of a
multidimensional CQI with all-cause mortality.
In conclusion, we found a significant association between
overall CQI and lower all-cause mortality, but the association was
not significant for the individual dimensions of the CQI. These
findings emphasize the need to shift the focus of nutritional
epidemiology from carbohydrate quantity to carbohydrate quality.
In addition, the CQI appears to capture the combined and potentially synergistic effects of quality dimensions in a comprehensive
manner. A single dimension of carbohydrate quality may not be the
most appropriate approach to assess the effects of overall carbohydrate quality.
manuscript for important intellectual content and approved the
final version to be published.
Funding sources
This project was supported by the Instituto de Salud Carlos III
and European Regional Development Fund (FEDER) (RD 06/0045,
CIBEROBN, Grants PI10/02658, PI10/02293, PI13/00615, PI14/
01668, PI14/01798, PI14/01764, PI17/01795, G03/140), the Navarra
Regional Government (27/2011, 45/2011, 122/2014), and the University of Navarra. In addition, we would like to thank all participants of the SUN project for their continued cooperation and
participation. Special thanks go to all the members of the SUN
project for administrative, technical, and material support. We also
would like to thank the members of the Department of Nutrition of
the Harvard School of Public Health (Willet WC., Hu FB., and
Ascherio A.) who helped us to design the SUN study.
Authors' contributions
C. F-L., I.Z., S.S., E.T., M. M-G. were involved with study conception and design; C. F-L., I.Z., M. M-G. performed the data analysis
and interpretation; S.S., E.T., M.B-L. assisted in data interpretation;
C. F-L., I. Z, S.S., M.M-G. drafted the manuscript; E.T., M.B-L. provided critical edits to the manuscript. All authors have revised the
Conflicts of interest
The authors declare no conflict of interest.
1
All assumptions were adjusted for baseline alcohol intake (four categories),
animal protein (g/d, continuous), BMI (kg/m2, linear and quadratic terms), following
special diet at baseline (dichotomous), educational level (years of higher education,
continuous), energy intake (kcal/d, continuous), family history of CVD (dichotomous), leisure-time physical activity (metabolic equivalent-h/week, continuous),
marital status (four categories), MUFA (%E, continuous), plant protein (g/d,
continuous), prevalent cancer (dichotomous), prevalent cardiovascular disease
(dichotomous), prevalent depression (dichotomous), prevalent diabetes (dichotomous), prevalent hypercholesterolemia (dichotomous), prevalent hypertension
(dichotomous), prevalent hypertriglyceridemia (dichotomous), polyunsaturated
fatty acids (%E, continuous), sex (dichotomous) , saturated fatty acids (%E, continuous), smoking (three categories), smoking habit (package/year), trans fatty acids (%
E, continuous), time spent sitting (hours/week), and weight gain in the previous 5
years before entering the cohort (3 kg, dichotomous), and stratified by deciles of
age and recruitment period (5 categories).
Acknowledgments
We would like to thank all participants of the SUN project for
their continued cooperation and participation. Special thanks go to
all the members of the SUN project for administrative, technical,
and material support. We also would like to thank the members of
the Department of Nutrition of the Harvard School of Public Health
(Willet WC., Hu FB., and Ascherio A.) who helped us to design the
SUN study. We thank Maria Soledad Hershey, RD, for kindly editing
the English style of this manuscript.
2371
C.I. Fernandez-Lazaro, I. Zazpe, S. Santiago et al.
Clinical Nutrition 40 (2021) 2364e2372
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Update
Clinical Nutrition
Volume 41, Issue 6, June 2022, Page 1465
DOI: https://doi.org/10.1016/j.clnu.2022.02.003
Clinical Nutrition 41 (2022) 1465
Contents lists available at ScienceDirect
Clinical Nutrition
journal homepage: http://www.elsevier.com/locate/clnu
Corrigendum to “Association of carbohydrate quality and all-cause
mortality in the Sun Project: A prospective cohort study” [Clinical
Nutrition 40 (4) (2021) 2364e2372]
Cesar I. Fernandez-Lazaro a, b, Itziar Zazpe a, b, c, d, Susana Santiago b, c,
lez a, b, d, e, *
Martínez-Gonza
Estefanía Toledo a, b, d, María Barbería-Latasa a, Miguel Angel
a
University of Navarra, Department of Preventive Medicine and Public Health, School of Medicine, 31008, Pamplona, Spain
IdiSNA, Navarra Institute for Health Research, 31008, Pamplona, Spain
University of Navarra, Department of Nutrition and Food Sciences and Physiology, School of Pharmacy and Nutrition, 31008, Pamplona, Spain
d
n Biom
n (CIBEROBN), 28029, Madrid, Spain
Centro de Investigacio
edica en Red Area
de Fisiología de la Obesidad y la Nutricio
e
Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
b
c
The authors regret that an old version of the Fig. 2 was inadvertently published in this manuscript. The correct version of the Fig. 2
appears below.
The authors would like to apologise for any inconvenience caused.
DOI of original article: https://doi.org/10.1016/j.clnu.2020.10.029.
* Corresponding author. University of Navarra, Department of Preventive Medicine and Public Health, C/ Irunlarrea, 31008, Pamplona, Spain.
Martínez-Gonza
lez).
E-mail address: [email protected] (M.A.
https://doi.org/10.1016/j.clnu.2022.02.003
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