Substitution analyses of foods with varying fat quality and the associations with all-cause mortality and impact of the FADS-1 genotype in elderly men

Abstract

Purpose
To investigate associations between substitutions of foods varying in fat quality and all-cause mortality in elderly Swedish men and to examine effect measure modification by a gene involved in fatty acid desaturation (rs174550 FADS1).

Methods
Using Cox-regression models in the ULSAM cohort (n = 1133 men aged 71), we aimed to investigate; (1) Associations between the substitution of a nutrient or food for another on all-cause mortality (primary outcome) and CVD (secondary outcome) and (2) Associations between the addition of various fat-rich foods to the habitual diet and all-cause mortality and CVD. Subgroup analyses based on the rs174550 FADS1 genotype were conducted.

Results
Over a mean follow-up of 11.6–13.7 years, n = 774 died and n = 494 developed CVD, respectively. No clear associations were observed for the vast majority of substitution nor addition models. Adding saturated fatty acids (SFA) on top of the habitual diet was however associated with an increased risk of mortality in men with the CT/CC-genotype [HR (95% CI) 1.44 (1.05, 1.97)]. Post-hoc analyses showed an inverse association of substituting SFA with carbohydrates [HR (95% CI) 0.79 (0.65, 0.97)], which was somewhat stronger in men with the CT/CC-genotype compared to men carrying the TT-genotype.

Conclusions
Few associations were observed between diet and all-cause mortality and CVD in this population. However, substituting SFA with carbohydrates was associated with lower mortality in post-hoc analyses and adding SFA to the habitual diet increased mortality in men with the CT/CC-genotype. The latter observation is novel and warrants further investigation in larger cohort studies including women.

Full text

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European Journal of Nutrition (2024) 63:145–153
https://doi.org/10.1007/s00394-023-03249-y
ORIGINAL CONTRIBUTION
Substitution analyses offoods withvarying fat quality
andtheassociations withall‑cause mortality andimpact oftheFADS‑1
genotype inelderly men
MichaelFridén1· ErikaOlsson2· LarsLind3· FredrikRosqvist1· UlfRisérus1
Received: 24 March 2023 / Accepted: 1 September 2023 / Published online: 20 September 2023
© The Author(s) 2023
Abstract
Purpose To investigate associations between substitutions of foods varying in fat quality and all-cause mortality in elderly
Swedish men and to examine effect measure modification by a gene involved in fatty acid desaturation (rs174550 FADS1).
Methods Using Cox-regression models in the ULSAM cohort (n = 1133 men aged 71), we aimed to investigate; (1) Associa-
tions between the substitution of a nutrient or food for another on all-cause mortality (primary outcome) and CVD (secondary
outcome) and (2) Associations between the addition of various fat-rich foods to the habitual diet and all-cause mortality and
CVD. Subgroup analyses based on the rs174550 FADS1 genotype were conducted.
Results Over a mean follow-up of 11.6–13.7years, n = 774 died and n = 494 developed CVD, respectively. No clear associa-
tions were observed for the vast majority of substitution nor addition models. Adding saturated fatty acids (SFA) on top of
the habitual diet was however associated with an increased risk of mortality in men with the CT/CC-genotype [HR (95% CI)
1.44 (1.05, 1.97)]. Post-hoc analyses showed an inverse association of substituting SFA with carbohydrates [HR (95% CI) 0.79
(0.65, 0.97)], which was somewhat stronger in men with the CT/CC-genotype compared to men carrying the TT-genotype.
Conclusions Few associations were observed between diet and all-cause mortality and CVD in this population. However,
substituting SFA with carbohydrates was associated with lower mortality in post-hoc analyses and adding SFA to the habitual
diet increased mortality in men with the CT/CC-genotype. The latter observation is novel and warrants further investigation
in larger cohort studies including women.
Keywords Food substitutions· Dietary fat quality· FADS· Mortality· CVD
Background
Based on both randomized trials and prospective cohort stud-
ies, global dietary recommendations, including those from
the Nordic countries (Nordic Nutrition Recommendations
(NNR)), highlight the importance of replacing saturated fatty
acids (SFA) with mono (MUFA)- and polyunsaturated fatty
acids (PUFA) in the diet [1]. However, longitudinal data on
hard outcomes such as all-cause mortality and cardiovascular
disease (CVD) with clearly defined food substitutions whereby
one food item rich in SFA is replaced by another food item
rich in PUFA in an elderly Nordic population, are limited.
Whether single-food replacements differing in dietary fat
quality later in life may be sufficient to counteract other age-
related biological processes, is unclear. Several meta-analyses
of observational studies have been conducted to investigate the
association between intake of SFA and all-cause mortality and
CVD in primarily younger and middle-aged populations, some
of which have concluded that SFA is not associated with either
outcome [2–4]. However, methodological limitations includ-
ing adjustment for causal intermediates such as blood lipids
and blood pressure might have led to biased estimates, thereby
attenuating these associations. Furthermore, since many previ-
ous observational studies have adjusted for total energy intake
without specifying any isocaloric replacement nutrient or food,
* Ulf Risérus
ulf.riserus@pubcare.uu.se
1 Department ofPublic Health andCaring Sciences, Clinical
Nutrition andMetabolism, Uppsala University, Uppsala,
Sweden
2 Department ofSurgical Sciences, Medical Epidemiology,
Uppsala University, Uppsala, Sweden
3 Department ofMedical Sciences, Clinical Epidemiology,
Uppsala University, Uppsala, Sweden
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146 European Journal of Nutrition (2024) 63:145–153
1 3
an unspecified substitution effect of increasing SFA at the
expense of a weighted average of all other energy-providing
foods or nutrients is estimated [5–7]. The obtained risk esti-
mate is therefore strongly reliant on the population-specific
background distribution of the diet, making it somewhat diffi-
cult to compare findings between populations and even harder
to interpret pooled estimates from meta-analyses [5].
Randomized controlled trials (RCT) have generally shown
a benefit of consuming less compared with more SFA on
composite CVD events [8, 9]. However, these studies have
primarily been conducted in middle-aged individuals and are
limited by the fact that different comparator foods or diets have
been used and that they are underpowered to detect effects
on all-cause mortality [8]. Clearly specifying the replacement
nutrient(s) and/or food(s) in both observational and rand-
omized trials may help to overcome the issue of comparability
raised above [10, 11].
Accumulating data have shown that variation in the FADS
1–3 gene cluster can impact the metabolism of dietary PUFA,
and especially the FADS1 gene has been linked to several car-
diometabolic disease traits, and may be a potential modifier
of fatty acid intake and mortality risk [12]. Recent interven-
tion studies of PUFA intake on cardiometabolic risk markers
have demonstrated effect heterogeneity by a single nucleotide
polymorphism (SNP) in a gene responsible for desaturating
PUFA (rs174550 in the FADS1 gene) [13, 14]. Lankinen,
etal. showed that when individuals with the TT-genotype in
the rs174550 FADS1 gene consumed a PUFA-rich diet, they
responded with lower levels of plasma C-reactive protein but
higher levels of adipose tissue inflammatory markers, com-
pared to individuals with the CC-genotype [13–15]. Longitu-
dinal studies investigating gene-diet interactions of substituting
foods rich in SFA with foods rich in PUFA on hard outcomes
such as all-cause mortality and CVD are however limited, but
important to examine in an era focused on personalized medi-
cine/nutrition. Our primary aim of this study was therefore to
examine associations between isocaloric substitutions of SFA
and SFA-rich foods with PUFA and PUFA-rich foods on all-
cause mortality and CVD in an elderly Swedish population
as well as to investigate potential effect measure modification
by the rs174550 FADS1 genotype. Our secondary aim was to
examine associations between adding a certain food or nutrient
on top of the habitual diet on all-cause mortality and CVD in
the full population as well as between stratum of the rs174550
FADS1 genotype.
Methods
Study population
The Uppsala Longitudinal Study of Adult Men (ULSAM)
is a population-based clinical cohort study initiated in
1970, whereby n = 2322 50-year old men were recruited
from the general population to investigate risk factors for
CVD [16]. In 1991–1995, n = 1221 71-year old men from
the original cohort were included for a follow-up investi-
gation. Of these, n = 1138 had data on dietary intake. Die-
tary habits were assessed for the first time in 1991–1995.
After exclusion of energy misreporters (< 800kcal/day
and > 4200kcal/day), n = 1133 remained, constituting our
main study population. Furthermore, of these n = 1133,
n = 1084, had data on the rs174550 FADS1 genotype for
the stratified analyses. For the secondary outcome (CVD),
n = 162 with prevalent CVD at baseline were excluded
(Supplementary Fig.1, Online Resource 1). This study was
conducted in accordance with the guidelines laid down in
the Declaration of Helsinki and was ethically approved by
the Regional Ethical Review Board at Uppsala University
(Dnr 251/90 and Dnr 2013/350). All participants gave their
written informed consent prior to inclusion.
Exposure assessment (diet)
For the baseline measures in 1991–1995, habitual diet was
assessed using an optically readable form of a seven day
dietary record with the use of a pre-coded menu book from
the Swedish National Food Agency (SNFA, version 1990)
[17]. Intakes of different foods were reported in household
measures or in predefined portion sizes. Participants were
also able to report other foods not specified in the menu
book in free text. The menu book was validated against an
open ended seven day weighed food record in a subsam-
ple of the cohort [18]. The seven day dietary assessment
method has furthermore been validated against biomarkers
in middle-aged individuals [19]. Dietary intake was ana-
lyzed with the help of a food composition database from
the Swedish National Food Agency (SNFA, version 1990))
incorporated into a commercially available software.
Food items from the 7day dietary records were classi-
fied in the following 10 food categories: red and processed
red meat (hereafter termed meat), unprocessed fish (here-
after termed fish), butter and butter-based spreads (hereaf-
ter termed butter), margarine and vegetable oils (hereafter
termed vegetable oils), fruits and vegetables (including
legumes) (hereafter termed F&V), alcoholic beverages,
non-alcoholic beverages (excluding water, tea and cof-
fee), total dairy (hereafter termed dairy), fatty snacks and
pastries and other foods (e.g. cereals, mayonnaise and
white meat). All foods were expressed in 100kcal. Blank
responses were recorded as zero. Nutrients were classified
in the following six categories: total carbohydrates, total
protein, alcohol, SFA, MUFA and PUFA. All nutrients
were expressed in 100kcal.
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147European Journal of Nutrition (2024) 63:145–153
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Genotyping
FADS1 (rs174550) was genotyped after DNA extraction
from blood samples using the Illumina Human Omni2.5M.
Due to low number of participants homozygous for the CC-
genotype, CC and CT were combined, thereby creating two
strata (TT and CT/CC).
Substitution andaddition modelling
Substitution analyses were performed using the leave-one-
out method with all foods and nutrients expressed in kcal
[6, 20, 21]. Total energy intake was included as a composite
variable to account for confounding by common determi-
nants of dietary intake [5, 22]. Substitutions of interest were
meat with fish, butter with vegetable oils, SFA with PUFA
as well as meat with F&V, butter with F&V and SFA with
carbohydrates, the latter three being post-hoc substitutions.
The former a priori determined substitutions were included
due to two main reasons: (1) food and nutrient substitutions
had to reflect real-life everyday replacements and (2) foods
had to reflect a significant proportion of SFA and PUFA
in the Swedish diet. The latter is the main reason why red
and processed red meat was not substituted for white meat.
Due to low intakes of nuts and seeds in this population, pre-
specified substitutions such as nuts and seeds with dairy was
not feasible. The leave-one-out method has been shown to
perform as good as the energy partition method when the
statistical modelling involves substituting just one nutrient
or food for another [21]. The leave-one-out model mimics
an RCT by which the intervention group would receive x
amount of kcal from food 1 while the comparator group
would receive x amount of kcal from food 2, holding all
other foods constant between the groups, thereby making
them isocaloric. Examples from two statistical modelling
approaches (one for nutrients and one for foods) using this
method are provided below.
1. Log(h(t; x)) of replacing SFA with PUFA = log(h0(t))
+ β1PUFA + β2MUFA + β3Carbohydrates + β4Protein + β5Alcohol +
β6Totalenergyintake + β7Confounders
2. Log(h(t; x)) of replacing red meat with fish = log(h0(t)
) + β1Fish + β2Butter + β3Vegetableoils + β4Fattysnacksandpastries +
β5Dairy + β6Alcoholicbeverages + β7Non-alcoholicbeverages + β8F&V
+ β9Otherfoods + β10Totalenergyintake + β11Confounders
Statistically modelling adding a nutrient or food on top
of the habitual diet was performed using the all-compo-
nents model suggested by Tomova etal. [5]. Additions
of interest were all foods and nutrients included in the
primary analysis (except for carbohydrates and F&V). The
all-components model mimics an RCT by which the inter-
vention group would receive x amount of extra kcal from
food 1 while the comparator group would continue with
their habitual diet, thereby making them non-isocaloric.
Although mediated by an increase in energy intake, this
model is the only model that can estimate total effects
from diet, in line with RCTs such as the omega-3 supple-
mentation trials or a recent study investigating the effect
of adding one avocado per day to the background diet on
visceral adipose tissue [23, 24]. Due to the non-isocaloric
condition that is inherently imposed, this model was
ranked secondary to the leave-one-out model with regards
to real-life practical relevance. Examples from two statis-
tical modelling approaches (one for nutrients and one for
foods) using this method are provided below.
1. Log(h(t; x)) of adding PUFA = log(h0(t)) + β1PUFA +
β2MUFA + β3SFA + β4Carbohydrates + β5Protein + β6Alcohol +
β7Confounders
2. Log(h(t; x)) of adding fish = log(h0(t)) + β1Fish + β2Meat
+ β3Butter + β4Vegetableoils + β5Fattysnacksandpastries + β6Dairy
+ β7Alcoholicbeverages + β8Non-alcoholicbeverages + β9F&V +
β10Otherfoods + β11Totalenergyintake + β12Confounders
All substitutions and additions were performed with
100kcal as the unit of exposure, corresponding to about
6 E% in this population reporting a median total energy
intake of 1712 (IQR 584) kcal. The main reason for choosing
kcal instead of E% as the unit of exposure is the somewhat
obscure estimand obtained when including ratio variables
such as E% in regression models [5].
Confounders
Confounders were identified using directed acyclic graphs
(DAGs) with subject matter knowledge and the use of the
DAGitty tool (Dagitty.net) [25]. To estimate the joint effect
of substituting SFA-rich foods with PUFA-rich foods, a
minimally sufficient adjustment set of variables were iden-
tified: age (continuous), current smoking (yes/no), educa-
tion (elementary school/folk high school/upper secondary
school or equivalent/university or equivalent), family history
of diabetes (yes/no), family history of CVD (yes/no), physi-
cal activity (over/under 3h of moderate physical activity
per day), sleep (difficulties/no difficulties getting to sleep at
night), stress (have/have not experienced stress at home or
at work the past 5years), total energy intake (kcal) and all
foods/nutrients (kcal) except for the one to be substituted
(Supplementary Fig.2, Online Resource 1). All covariates
were self-reported. To estimate the total effect of adding
SFA/PUFA-rich foods on top of the habitual diet, a similar
adjustment set was identified (except for total energy intake
and the food/nutrient that was left out in the leave-one-out
model) (Supplementary Fig.2, Online Resource 1).
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148 European Journal of Nutrition (2024) 63:145–153
1 3
Outcome ascertainment (all‑cause mortality
andCVD)
Deaths from all causes were retrieved from the Cause of
Death Registry and was the primary outcome of the study.
CVD was composed of primary CVD (myocardial infarc-
tion (ICD-8: 410, ICD-9: 310, ICD-10: I20), ischemic stroke
(ICD-8: 431, 433–436, ICD-9: 431, 433–436, ICD-10: I63-
I66) and heart failure (ICD-8: 427.00, 427.10, 428.99, ICD-
9: 428, ICD-10: I50, I11.0) and was identified through link-
age with the Swedish National Patient and Cause of Death
Registries. The accuracy of myocardial infarction and stroke
have been deemed to be high in Swedish registries [26].
However, as heart failure has shown lower validity, addi-
tional chart review based validation was performed, as previ-
ously described [27]. Those with prevalent CVD at baseline
were excluded for the analyses of incident CVD. CVD was
determined the secondary outcome.
Statistical analyses
Our primary aim of this study was to investigate the associa-
tions between substituting SFA-rich foods with PUFA-rich
foods and all-cause mortality and CVD, respectively. Mul-
tivariable Cox proportional hazard regression models were
used to estimate hazard ratios (HR) with corresponding 95%
CI, with time since baseline as the underlying time scale.
Time at risk for all-cause mortality was calculated from the
date of first baseline visit (1991–1995) until the date of death
or administrative end of follow-up (December 31, 2011),
whichever occurred first. For CVD, time at risk was calcu-
lated from the date of first baseline visit (1991–1995) until
the date of CVD diagnosis, death or administrative end of
follow-up (December 31, 2011), whichever occurred first.
Missing data on confounders (2% for family history of T2D
and family history of CVD, 3% for physical activity, sleep
and stress, and 4% for smoking) were imputed using multiple
imputation (n = 5 imputations) to account for potential selec-
tion bias, assuming the data was missing at random (MAR).
Energy misreporters (n = 5) were excluded to account for
potential measurement bias. Similar models were used for
the secondary aim of this study of investigating associations
of adding SFA/PUFA-rich foods on all-cause mortality and
CVD. Effect measure modification by the rs174550 FADS1
genotype on the multiplicative scale was examined by strati-
fied Cox regression analyses. The assumption of propor-
tional hazards was checked using Schoenfeld residual plots.
Post-hoc analyses included examining the associations
between substituting SFA and SFA-rich foods with carbo-
hydrates and carbohydrate-rich foods and all-cause mortality
and CVD. These analyses were additionally performed since
dietary recommendations also highlight that saturated fat as
a whole should be limited, but without clearly specifying the
replacement nutrient or food. The carbohydrate-rich food
used in the food substitution models was F&V.
To investigate the robustness of our results, a couple of
sensitivity analyses were performed including (1) complete-
case analyses (to address the question of selection bias) and
(2) excluding the first 2years of follow-up (to address the
question of reverse causation). All analyses were carried out
in IBM SPSS Statistics version 28.0.1.0 (142).
Results
Baseline characteristics for the primary (all-cause mortality)
and secondary population (CVD) are presented in Table1.
Study participants were all men and had a median age of
71 (IQR 0.80) at baseline. Over half (58.9–59.8% for all-
cause mortality and CVD, respectively) reported that they
engaged in moderate physical activity more than 3h per
week, 14.2–14.5% (for all-cause mortality and CVD, respec-
tively) were current smokers and 14.7–15.0% (for all-cause
mortality and CVD, respectively) had a university degree.
Self-reported median daily energy intake was 1712 (IQR
584) kcal for the primary population and 1718 (IQR 592)
kcal for the secondary population. For the rs174550 FADS1
genotype, 40.4% were homozygous for the TT-variant in
both populations. Baseline dietary intake stratified by the
rs174550 FADS1 genotype are presented in Supplemen-
tary Table1, online resource 1. Over a mean follow-up of
13.7years (maximum 20.4years and 15,498 person-years)
for all-cause mortality and 11.6years (maximum 20.4years
and 11,275 person-years) for CVD, n = 774 cases of all-
cause mortality was captured and n = 494 developed CVD.
Substitution analyses (leave‑one‑out model)
No associations were observed for substituting SFA with
PUFA or meat with fish on either all-cause mortality or
CVD (Table2 for the full population and Table3 for the
stratified population). Substituting butter with vegetable oils
indicated an inverse association with all-cause mortality [HR
0.93 (95% CI 0.84, 1.02)) and CVD (HR 0.89 (95% CI 0.79,
1.01)] (Table2). Substituting SFA with carbohydrates was
associated with a 21% decreased risk of all-cause mortal-
ity [HR 0.79 (95% CI 0.65, 0.97)] in the full population
(Table2) and with a 26% decreased risk of all-cause mor-
tality in participants with the CT/CC-genotype [HR 0.74
(95% CI 0.56, 0.98)] in post-hoc analyses (Table3). For the
TT-genotype the HR was 0.79 (95% CI 0.57, 1.11) (Table3).
Addition analyses (all‑components model)
In the all-components model, the HR for SFA intake on all-
cause mortality in the full population was 1.18 (95% CI 0.93,
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149European Journal of Nutrition (2024) 63:145–153
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1.49) (Table2). No associations were observed for intake
of PUFA, meat, fish or vegetable oils on either mortality or
CVD (Table2). Butter intake indicated a positive association
with mortality [HR 1.11 (95% CI 0.99, 1.23)] in the full pop-
ulation (Table2) whereas intake of vegetable oils indicated a
positive association with mortality [HR 1.08 (95% CI 0.99,
1.18)] in individuals with the CT/CC-genotype (Table3).
SFA intake was associated with a 44% increased risk of all-
cause mortality in participants with the CT/CC-genotype
[HR 1.44 (95% CI 1.05, 1.97)] but not in participants with
TT-genotype [HR 0.98 (95% CI 0.66, 1.44)] (Table3).
Sensitivity analyses
Complete-case analyses (Supplementary Table2 for the
full population and Supplementary Table3 for the stratified
population, Online Resource 1) and analyses whereby the
first two years of follow-up were excluded (Supplementary
Table4 for the full population and Supplementary Table5
for the stratified population, Online Resource 1) demon-
strated similar associations with both all-cause mortality
and CVD as for the main analyses, except for some minor
deviations that did not alter the interpretations of the results.
Discussion
In this prospective cohort study of elderly Swedish men, no
clear associations were observed between food and nutrient
substitutions nor additions and all-cause mortality and CVD
in the whole population, except for the substitution of SFA
with carbohydrates that was inversely associated with all-
cause mortality. The latter was also shown for men with the
combined CT/CC-genotype, but not for the TT-genotype of
the FADS1 gene (rs174550). Furthermore, a novel finding
was that adding SFA to the habitual diet was associated with
all-cause mortality in men with the CT/CC-genotype in the
FADS1 gene (rs174550), but not for men carrying the TT-
genotype. This association is to our knowledge novel, and
might point towards a gene-diet interaction with potential
clinical implications.
In line with a newly published meta-analysis of prospec-
tive cohort studies of primarily middle-aged populations,
we found an inverse association of substituting SFA with
carbohydrates on all-cause mortality in a post-hoc analy-
sis [28]. On the contrary, we found no clear associations
of substituting SFA-rich foods such as meat or butter with
the carbohydrate-rich source F&V on mortality. The inverse
Table 1 Population
characteristics according to
the event studied: all-cause
mortality and CVD
Data are presented in medians (IQR) for continuous variables and counts and percentages for categorical
variables
For descriptive statistics in Table1, physical activity is based on n = 1103 for all-cause mortality and
n = 945 for CVD; rs145550 FADS1 on n = 1084 for all-cause mortality and n = 932 for CVD; current
smokers on n = 1083 for all-cause mortality and n = 931 for CVD; family history of T2D on n = 1111 for
all-cause mortality and n = 952 for CVD; family history of CVD on n = 1114 for all-cause mortality and
n = 955 for CVD; difficulties sleeping at night on n = 1102 for all-cause mortality and n = 944 for CVD;
experience of stress on n = 1099 for all-cause mortality and n = 943 for CVD
CVD Cardiovascular disease, FADS1 fatty acid desaturase enzyme 1, MPA moderate physical activity,
PUFA polyunsaturated fatty acids, SFA saturated fatty acids, T2D Type-2 diabetes
All-cause mortality (n = 1133) CVD (n = 971)
Sex (n (%) men) 1133 (100) 971 (100)
Age (years) 71 (0.80) 71 (0.80)
University education (n (%)) 167 (14.7) 146 (15.0)
Current smokers (n (%)) 161 (14.2) 141 (14.5)
Physical activity (n (%) of 3h/week of MPA) 667 (58.9) 581 (59.8)
Experience of stress (n (%)) 37 (3.3) 28 (2.9)
Difficulties sleeping at night (n (%)) 121 (10.7) 97 (10.0)
Family history of T2D (n (%)) 167 (14.7) 144 (14.8)
Family history of CVD (n (%)) 669 (59.0) 559 (57.6)
rs174550 FADS1 (n (%) of TT/TC and CC) 458 (40.4)/626 (55.3) 393 (40.4)/539 (55.5)
Total energy intake (kcal/d) 1712 (584) 1718 (592)
Butter and butter-based spreads (kcal/d) 63 (102) 63 (108)
Margarine and vegetable oils (kcal/d) 0 (61) 0 (61)
Unprocessed fish (kcal/d) 0 (6) 0 (6)
Red and processed red meat (kcal/d) 133 (94) 134 (94)
SFA (kcal/day) 255 (123) 257 (124)
PUFA (kcal/day) 84 (40) 86 (39)
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150 European Journal of Nutrition (2024) 63:145–153
1 3
association observed on a nutrient level may therefore be due
to other isolated or mixed food replacements, warranting
further investigation. Lack of associations for the a priori
determined food exposures in our study on all-cause mor-
tality are partly in line with a previous Danish cohort study
investigating the substitution of 150g of meat per week
with 150g of fish per week, showing a HR of 0.99 (95%
CI 0.97–1.01) [29]. In contrast to our null findings, inverse
associations on mortality of substituting SFA with PUFA
have been demonstrated in other cohort studies [28, 30, 31].
Few longitudinal studies have however been conducted to
investigate the substitution of SFA-rich foods such as butter
with PUFA-rich foods such as vegetable oils. Although our
findings indicated an inverse association of substituting but-
ter with vegetable oils on mortality [HR 0.93 (95% CI 0.84,
1.02)], the precision of the point estimate was wide, covering
an upper CI level of one. Guasch-Ferré etal. showed, using
repeated measures of diet, a decreased risk on all-cause mor-
tality when substituting butter with olive oil, but that study
was conducted in middle-aged men and women in the U.S
with olive oil as the comparator food [32]. The two studies
are therefore discrepant on at least three points: different
comparator foods, inclusion of both men and women and
different age groups. Furthermore, it is possible that trans
fatty acid content in some margarines may have contributed
to attenuated associations between the substitution of butter
with vegetable oil in our population.
Table 2 Associations between food and nutrient substitutions and
additions and all-cause mortality and CVD
Data are presented as hazard ratios with 95% confidence intervals for
all-cause mortality (n = 1133) and CVD (n = 971)
Both models are adjusted for age, education, smoking, stress, sleep,
family history of CVD, family history of type-2 diabetes and physi-
cal activity. The leave-one-out model is furthermore adjusted for total
energy intake and includes all nutrients or foods except for the one
to be substituted. The all-components model includes all nutrients
or foods. All substitutions and additions are modelled in the unit of
100kcal
Number of cases = 774 for all-cause mortality and 494 for CVD
All-cause mortality CVD
Leave-one-out model
SFA with PUFA 1.27 (0.86, 1.88) 1.54 (0.93, 2.54)
Butter with oils 0.93 (0.84, 1.02) 0.89 (0.79, 1.01)
Meat with fish 1.29 (0.84, 1.98) 1.30 (0.76, 2.24)
SFA with carbohydrates 0.79 (0.65, 0.97) 0.93 (0.71, 1.20)
Butter with F&V 0.89 (0.77, 1.03) 0.93 (0.78, 1.11)
Meat with F&V 1.00 (0.86, 1.15) 1.04 (0.87, 1.24)
The all-components model
SFA 1.18 (0.93, 1.49) 1.01 (0.75, 1.35)
PUFA 1.53 (0.94, 2.48) 1.55 (0.85, 2.84)
Butter 1.11 (0.99, 1.23) 1.07 (0.93, 1.22)
Oils 1.03 (0.96, 1.11) 0.95 (0.87, 1.05)
Fish 1.27 (0.83, 1.94) 1.24 (0.73, 2.12)
Meat 0.99 (0.89, 1.09) 0.95 (0.84, 1.08)
Table 3 Associations between
food and nutrient substitutions
and additions and all-cause
mortality and CVD, stratified by
rs174550 FADS1-genotype
Data are presented as hazard ratios with 95% confidence intervals for all-cause mortality (n = 1084) and
CVD (n = 932)
Both models are adjusted for age, education, smoking, stress, sleep, family history of CVD, family history
of type-2 diabetes and physical activity. The leave-one-out model is furthermore adjusted for total energy
intake and includes all nutrients or foods except for the one to be substituted. The all-components model
includes all nutrients or foods. All substitutions and additions are modelled in the unit of 100kcal
Number of cases = 264 (TT) and 350 (CT/CC) for all-cause mortality and 190 (TT) and 286 (CT/CC) for
CVD
All-cause mortality CVD
TT CT/CC TT CT/CC
Leave-one-out model
SFA with PUFA 1.53 (0.83, 2.81) 0.96 (0.56, 1.66) 1.32 (0.58, 2.99) 1.31 (0.67, 2.55)
Butter with oils 0.88 (0.76, 1.03) 0.99 (0.86, 1.13) 0.90 (0.74, 1.11) 0.97 (0.82, 1.15)
Meat with fish 1.23 (0.61, 2.48) 1.40 (0.78, 2.53) 1.70 (0.72, 3.99) 1.26 (0.58, 2.74)
SFA with carbohydrates 0.79 (0.57, 1.11) 0.74 (0.56, 0.98) 0.89 (0.59, 1.34) 0.89 (0.63, 1.26)
Butter with F&V 0.84 (0.67, 1.06) 0.95 (0.77, 1.18) 0.86 (0.64, 1.16) 1.07 (0.82, 1.39)
Meat with F&V 0.91 (0.73, 1.14) 1.04 (0.85, 1.28) 0.92 (0.69, 1.23) 1.13 (0.88, 1.46)
The all-components model
SFA 0.98 (0.66, 1.44) 1.44 (1.05, 1.97) 0.96 (0.59, 1.56) 1.15 (0.78, 1.71)
PUFA 1.45 (0.67, 3.15) 1.48 (0.76, 2.88) 1.26 (0.46, 3.41) 1.54 (0.69, 3.41)
Butter 1.09 (0.91, 1.31) 1.09 (0.94, 1.27) 1.02 (0.81, 1.27) 1.02 (0.85, 1.23)
Oils 0.97 (0.85, 1.10) 1.08 (0.99, 1.18) 0.92 (0.76, 1.10) 0.99 (0.88, 1.12)
Fish 1.24 (0.64, 2.44) 1.40 (0.78, 2.50) 1.62 (0.71, 3.67) 1.22 (0.57, 2.62)
Meat 1.01 (0.87, 1.18) 1.00 (0.86, 1.16) 0.95 (0.78, 1.17) 0.96 (0.81, 1.15)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

151European Journal of Nutrition (2024) 63:145–153
1 3
Our null findings for CVD are also in line with some
observational studies conducted in older individuals [33],
but not with others [34]. Steur, etal. showed weaker asso-
ciations for incident CVD in individuals over 52 compared
to those under 52years of age of substituting carbohydrates
with SFA [33]. Interestingly, in the CORDIOPREV study,
the Mediterranean diet [defined by higher intakes of unsatu-
rated fat and lower SFA, lower red meat and butter intake
and higher intakes of fish, vegetable oils (olive oil) and
F&V] compared to a low-fat diet was not associated with
CVD when stratified by over 70years of age [35]. This sug-
gestive age-dependent effect on CVD was also shown in the
REDUCE-IT trial where participants received extra energy
from 4g of daily omega-3 fatty acids [23]. These findings
may suggest that dietary habits earlier in life may have more
profound effects on CVD but potentially also on other car-
diometabolic risk factors associated with all-cause mortality.
In contrast, the PREDIMED study showed no effect modi-
fication by age [36]. Lack of associations in our study on
CVD could also be a consequence of lower statistical power,
indicated by the wide CI, and should therefore be interpreted
with caution. Importantly, these findings do not contradict
the body of evidence from both RCTs and observational
studies showing a reduction in LDL-cholesterol/apoB con-
taining lipoprotein levels and CVD outcomes in younger and
middle-aged populations when substituting SFA and SFA-
rich foods with PUFA and PUFA-rich foods [8, 34, 37, 38].
Findings from this study are to be interpreted in the context
for which it has been conducted; in elderly men.
Potential effect measure modification by the rs174550
FADS1 genotype on all-cause mortality has, to our knowl-
edge, not been previously investigated in nutrient or food
based substitution analyses. In contrast to smaller dietary
trials, our findings did not point to any clear potential effect
heterogeneity of this particular genotype for any PUFA-rich
foods [13–15]. Although the substitution of SFA with car-
bohydrates was associated with all-cause mortality in men
carrying the CT/CC-genotype and not the TT-genotype,
these findings may be explained by lower statistical power
in the TT-strata, as indicated by the wide CI. Interestingly
though, we did observe some stratum-specific results for
the addition of SFA on all-cause mortality, with a stronger
association in men carrying the CT/CC-genotype compared
to the TT-genotype [HR 1.44 (95% CI 1.05, 1.07)] versus
[HR 0.98 (95% CI 0.66, 1.44)]. This is to our knowledge a
new observation with potential implications for personal-
ized nutrition, if confirmed in other cohorts. Whether these
findings may be explained by differences in the distribution
of metabolic risk factors (and thereby potential interactions
with dietary factors), as indicated by other studies [14, 39],
is highly speculative. RCTs by which SFA-rich foods, such
as butter, is replaced by PUFA-rich or other macronutrient-
rich foods or added to the habitual diet may provide a deeper
understanding of this specific gene-diet interaction on car-
diometabolic risk markers.
There are several strengths and limitations worthy to
mention. First, since the ULSAM cohort has collected
unique phenotype characteristics and multiple measurements
among these elderly participants, the sample size is rela-
tively small, i.e. the statistical power to detect associations
for CVD might have been compromised. Thus, the results
should therefore be interpreted with caution, as outlined in
the discussion above. Due to this, CVD as an outcome was
determined to be secondary to all-cause mortality. Secondly,
the study population consisted of only men, which may limit
transportability to other populations including women. Like-
wise, due to potential effect measure modification by the
rs174550 FADS1 genotype for the association of SFA on
all-cause mortality, different SNP distributions may provide
somewhat different estimates in other populations. Further-
more, since pooled nutrients such as SFA, PUFA and car-
bohydrates may reflect different foods (and/or subclasses
of the pooled nutrients) over different age spans as well as
geographical locations, this may further impact the extent
to which extrapolations of our findings can be done. How-
ever, using specified substitution analyses may indeed help
mitigate some of the issues of generalizability and transport-
ability imposed by traditional nutritional epidemiological
approaches that adjust for total energy intake without any
clear replacement foods or nutrients in mind [40]. Addi-
tionally, all covariates (including diet) were self-reported
which may have led to measurement error and thereby
measurement bias/residual confounding bias. Using mainly
foods instead of nutrients as the exposure and adjusting for
total energy intake in combination with other foods/nutri-
ents might have reduced some of this potential bias [41].
There are however two potential caveats with food substitu-
tion models that are important to bear in mind, namely (1)
the practical implications of each replacement (100kcal of
meat corresponds to 70g whereas 100kcal of F&V corre-
sponds to 200–250g) and (2) the inherent restriction in the
underlying dietary pattern that is imposed by adjusting for
all other foods in the model. However, we do believe that
the former caveat is feasible for some individuals and the
latter of adjusting for all other foods is necessary to answer
the research question of isolated food substitutions. Lastly,
diet was only measured at one point in time, treating diet
as a time-fixed exposure. Further analyses should be con-
ducted with repeated measures of diet (and confounders) to
allow for changes over time with the use of more appropriate
statistical methods (i.e. g-methods) to account for exposure-
confounding feedback when conditioning on post-baseline
covariates influenced by past exposure. Strengths of this
study include the long follow-up to detect associations for
all-cause mortality, the use of a 7-day food record to capture
detailed information on the exposure, the use of DAGs to
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152 European Journal of Nutrition (2024) 63:145–153
1 3
identify potential confounding paths, no loss to follow-up
due to the ability to link individuals to different registries
through their personal identification number, and the use
of substitution (and addition) models to potentially mimic
real-life dietary choices.
In conclusion, regarding the replacement of foods varying
in fat quality, few associations were observed for the food
groups we investigated and all-cause mortality and CVD in
this population. These findings might suggest that in Swed-
ish elderly men, replacements of single foods varying in
fat quality alone may not be sufficient to counteract other
age-related biological processes associated with these out-
comes. However, substituting SFA with carbohydrates was
associated with decreased all-cause mortality and adding
SFA to the habitual diet in men with the CT/CC genotype in
the FADS1 gene (rs174550) was positively associated with
increased mortality. This association is to our knowledge
novel and might point towards a gene-diet interaction with
potential clinical implications. Our findings warrant further
investigation and confirmation in larger population-based
studies.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s00394- 023- 03249-y.
Author contributions MF, FR, LL, UR, and EO designed research; LL,
and EO conducted research; MF, FR, and UR analyzed the data; MF
wrote the first draft of the manuscript and all authors edited, reviewed
and approved the final version of the manuscript; UR had primary
responsibility for final content.
Funding Open access funding provided by Uppsala University. Exo-
diab, Swedish Heart and Lung Foundation, Formas.
Data sharing Data described in the manuscript will be made available
upon reasonable request from the corresponding author.
Declarations
Conflict of interest The authors have no relevant financial or non-fi-
nancial interests to disclose.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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