1. Chem. Senses 39: 349–357, 2014 doi:10.1093/chemse/bju006
Advance Access publication March 3, 2014
© The Author 2014. Published by Oxford University Press. All rights reserved.
For permissions, please e-mail: journals.permissions@oup.com
Associations Between BMI and Fat Taste Sensitivity in Humans
Robin M. Tucker
1
, Claire Edlinger
2
, Bruce A. Craig
3
and Richard D. Mattes
1
1
Department of Nutrition Science, Purdue University, 700 W. State Street, West Lafayette,
IN 47907, USA,
2
Université de Lorraine, ENSAIA (Ecole Nationale Supérieure d’Agronomie et
des Industries Alimentaires), 2 avenue de la Forêt de Haye, BP 172, 54505, Vandoeuvre-lès-
Nancy, France and
3
Department of Statistics, Purdue University, 150 N. University Street,
West Lafayette, IN 47907, USA
Correspondence to be sent to: Richard D. Mattes, Department of Nutrition Science, Purdue University, 700 W. State Street, West
Lafayette, IN 47907, USA. e-mail: mattes@purdue.edu
Accepted January 21, 2014
The objective of this study was to examine the reliability of associations between fat taste, hunger, dietary fat intake, and
body mass index (BMI). Detection thresholds for oleic acid (OA) were obtained during each of 7 consecutive visits using a
modified staircase procedure. Participants were 48 (N = 17 male; N = 31 female) healthy adults (mean age: 28.5 ± 10.4 years)
with BMI’s ranging from 18.9 to 47.2 (≥25 kg·m−2
, N = 24). OA detection thresholds and self-reported hunger (100-mm
visual analog scale) were assessed at each visit. BMI and dietary fat intake (Block Rapid Fat Screener) were determined at
baseline. There was a significant decrease of threshold concentration over repeated trials among lean and overweight (BMI
between 25.0 and 29.9 kg·m−2
) participants but not in the obese. Combining the lean and overweight and contrasting their
responses to the obese revealed the lean plus overweight group to be significantly more sensitive at visits 6 and 7. No change
of threshold sensitivity or correlation with fat intake was observed in the obese participants unlike findings in the lean and
lean plus overweight participants. Correlations between saturated fat intake and threshold sensitivity were positive (greater
intake associated with higher thresholds) at baseline for the group, with additional correlations observed among the lean
plus overweight but not in the obese, leaving open questions about the nutritional significance of the association. No sig-
nificant associations were observed between sensitivity to OA and hunger. Repeated testing is required to assess associations
between fat taste and other outcome variables.
Key words: body mass index, dietary fat intake, fat taste, hunger, taste thresholds
Introduction
The evidence supporting human nonesterified fatty acid
(NEFA) taste, commonly referred to as “fat taste,” has
grown over the past 15 years. The role of taste sensitivity
in promoting intake of specific foods or ingredients associ-
ated with chronic diseases has long been an area of inter-
est. Examples include the associations between sensitivity to
6-n-propylthiouracil and cruciferous vegetable intake with
implications for cancer or goiter risk (Bell and Tepper 2006;
Duffy et al. 2010; Tsuji et al. 2012); sensitivity to NaCl and
sodium intake posited to promote hypertension and stom-
ach cancer (Yang et al. 2011; Zhang and Zhang 2011); and
sensitivity to glucose and sugar intake purportedly leading
to obesity and diabetes (Perros et al. 1996). In no case has
causality been established because the links between taste,
intake, and disease are complex. Indeed, associative claims
are made for lower and higher taste sensitivity leading to
the same dietary behavior such as increased salt intake. It
is argued that lower sensitivity leads to increased intake to
achieve a desired level of sensory stimulation and greater
sensitivity results in stronger activation of reward centers
driving increased intake. Of course, the reverse may also
be true where intake alters sensitivity. Nonetheless, with
increasing evidence supporting the ability of humans to taste
NEFAs (Kamphuis et al. 2003; Chalé-Rush et al. 2007a,b;
Mattes 2009a,b; Stewart et al. 2010; Stewart, Feinle-Bisset,
et al. 2011; Stewart, Newman, et al. 2011; Stewart, Seimon,
et al. 2011; Galindo et al. 2012; Pepino et al. 2012; Stewart
and Keast 2012; Tucker et al. 2013; Tucker and Mattes
2013), similar interest in so-called “fat taste” sensitivity, fat
consumption, and myriad health risks has arisen (Stewart
et al. 2010; Stewart, Newman, et al. 2011; Stewart and Keast
2012). With the touted links between fat intake and many
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2. 350 R.M. Tucker et al.
adverse health outcomes such as obesity, diabetes, cardio-
vascular disease, selected cancers, osteoarthritis, and social
stigma, it is imperative this issue be clearly characterized.
Fatty foods are often highly flavorful and associated with
obesity, for example, Ryan et al. (2012). This has prompted
hypotheses of an association between NEFA taste sensitivity
and body mass index (BMI) with mixed experimental sup-
port. Some have reported inverse associations between BMI
and sensitivity to NEFA (increases in BMI were associated
with reduced sensitivity or higher thresholds) (Stewart et al.
2010; Stewart, Newman, et al. 2011; Stewart, Seimon, et al.
2011), whereas others fail to find associations (Kamphuis
et al. 2003; Mattes 2009b; Mattes 2011; Stewart and Keast
2012).
Explanations for both sets of findings have been proposed.
Based on the recognition that NEFA impart unpleasant or
aversive sensations, one explanation for the inverse asso-
ciation between BMI and sensitivity to NEFA holds that
decreased sensitivity to fat contributes to increased intake
as the unpalatable sensations NEFA impart would be less
apparent and less likely to evoke rejection responses. Others
propose that habituation to a high-fat diet leads to a require-
ment for greater exposure to generate an appropriate oral
response (Stewart et al. 2010; Stewart, Newman, et al. 2011;
Stewart and Keast 2012). This increased fat intake presum-
ably contributes to weight gain. Alternatively, it may be that
dietary fat intake or obesity modifies fat taste. In a single
4-week trial, high-fat diet exposure decreased sensitivity to
NEFA in lean but not overweight/obese subjects (Stewart
and Keast 2012). However, a larger body of evidence indi-
cates there is no significant association between BMI and
NEFA sensitivity. The latter findings are not unexpected.
First, a lack of association mirrors findings from previous
work in other taste qualities (Grinker et al. 1972; Rodin 1975;
Grinker 1978; Malcolm et al. 1980; Frijters and Rasmussen-
Conrad 1982; Scruggs et al. 1994). Second, thresholds to
particular tastes are often not associated with liking or pref-
erence for that stimulus at the suprathreshold concentrations
commonly encountered in daily eating (Bartoshuk 1979).
Third, there may be a methodological basis for a poor asso-
ciation. Evidence of learning effects on tests of threshold
sensitivity for NEFA indicate multiple trials must be con-
ducted to establish the lower limits of an individual’s sensory
sensitivity to NEFA (Tucker and Mattes 2013). Threshold
measurements reported to date do not account for learning
effects and likely fail to reflect true fat taste sensitivity. This
methodological issue may be especially relevant for NEFA
due to a lack of participant familiarity with: 1) the stimuli,
2) descriptors for the sensation, and/or 3) testing procedures.
Misclassification of sensitivity could obscure associations
between sensitivity and BMI or other variables of interest.
In light of the mixed findings on oral fat detection in
lean versus overweight/obese individuals, the present study
tested the oleic acid (OA) detection threshold of individu-
als 7 times to assess differences in sensitivity between lean,
overweight, and obese participants. We hypothesized no
association between BMI and NEFA detection thresholds
for the reasons discussed above. Further, the recent obser-
vation that dietary fat intake reportedly influences fat taste
sensitivity, at least in lean individuals (Stewart and Keast
2012), prompted a second hypothesis—that high habitual
dietary fat intake would correlate with decreased sensitivity.
Third, we also measured hunger, as there are reports that
hunger improves taste sensitivity (Zverev 2004), and there
are mixed reports of differential hunger sensations in lean
and obese individuals (Cornier et al. 2004; Mckiernan et al.
2009; Brennan et al. 2012). We hypothesized that sensitivity
would be improved with increased levels of hunger, and that
hunger may lead to different dietary responses to fat in lean
and obese individuals.
Materials and methods
The test stimuli consisted of an emulsion of 5% w/v
(1.8 × 10
−1
M) OA (Spectrum Chemicals) in deionized water
with 12% gum arabic (Sigma-Aldrich), 0.01% xanthan gum
(Jungbunzlauer Inc.), and 0.01% ethylenediaminetetraacetic
acid (Spectrum Chemicals). The vehicle was designed to
mask textural cues that NEFA may contribute, including vis-
cosity and lubricity (Ramirez 1992; Schiffman et al. 1998).
Five percent OA emulsions prepared in 200 mL batches were
formed by homogenization (IKA T18 Basic Ultra Turrax)
for 20 min at 15 500 rpm and then diluted by quarter log steps
to create a range of 39 stimulus concentrations to accommo-
date the most sensitive participant. Five percent was selected
as the upper OA concentration as amounts higher than this
are rarely found in foods that are not rancid. The vehicle was
treated in the same fashion as the OA emulsions. Samples
were made less than 24 h before testing, stored under nitro-
gen in polypropylene containers, and served at room temper-
ature. Particle size distributions of the 5% OA emulsion were
obtained using a Malvern Instruments Mastersizer 2000
with a Hydro 2000MU dispersion unit. The dispersant was
deionized water, and a refractive index of 1.458 and absorp-
tion of 0.005 was used for OA. Viscosity was analyzed using
an ARG2 Rheometer from TA Instruments equipped with
a 40 mm, 2° cone and plate geometry with a solvent trap
and a Peltier plate for temperature control. Shear rate was
increased logarithmically from 1 to 300 s
−1
at 37 °C, with 10
data points per decade.
Subjects
Forty-eight (N = 17 male; N = 31 female) adults (mean age:
28.5 ± 10.4 years) gave informed consent and completed the
study. Body mass indices (BMI) ranged from 18.9 to 47.2 (lean
[≤24.9 kg·m
−2
]: N = 24; overweight [25.0–29.9 kg·m
−2
]: N = 11;
obese [≥30 kg·m
−2
]: N = 13). Eight participants were Asian, 5
were Black, 1 was mixed race, 33 were White, and 1 partici-
pant failed to provide racial information. Recruitment took
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3. BMI and Fat Taste Sensitivity 351
place via public advertisements. This study was approved by
the University’s Human Subjects Institutional Review Board
and was registered at ClinicalTrials.gov (#NCT01550120).
Study design
The study consisted of 7 test visits where OA detection
thresholds were determined using a 3-alternative forced
choice modified staircase procedure. Previous work showed
that significant improvement from baseline was seen over
the first 7 visits (Tucker and Mattes 2013). Participants were
instructed to abstain from food, beverages other than water,
and oral care products for at least an hour before testing.
Testing sessions were typically conducted at the same time of
day for each participant, but when occasional conflicts arose,
visits were scheduled at the convenience of the participant.
Prior to each testing session, participants were asked to rate
their hunger level on a 100-mm visual analog scale (VAS).
Height, weight, and habitual dietary fat intake, as quantified
by the Block Rapid Fat Screener (BRFS; NutritionQuest),
were measured at baseline. The BRFS, is a one-page, self-
administered survey previously validated against a 100-item
questionnaire and is reported to correlate significantly with
total fat, saturated fat, monounsaturated fat, percent of calo-
ries from fat, and cholesterol intake (Spearman r = 0.6–0.72)
(Block et al. 2000).
Taste testing methodology
Participants wore blindfolds and nose clips to eliminate
visual and olfactory cues. During each trial, 3 samples were
presented to participants; 2 samples contained the vehicle,
and 1 sample contained the vehicle plus the OA. Participants
were instructed to choose the sample that was different from
the others following a forced choice format. No more than
one testing trial was conducted per day.
The starting concentration at each visit was 3.2 mM.
A 2-down, 1-up rule was used to determine the concentra-
tions presented to the participants. Once 5 reversals had
occurred or when the participant had tasted all concentra-
tion levels without 5 reversals, testing ceased for that session.
If 5 reversals were not obtained, the threshold was deemed
beyond the range of thresholds considered. If 5 reversals
were obtained, the last 4 reversals were averaged to obtain
the threshold estimate.
Statistics
A linear mixed model that handles censoring (via the
NLMIXED procedure in SAS) was used to assess and
compare changes in performance over visits for each of the
BMI groups. This model was used because of the multiple
measurements per participant and the fact that on some
visits participants were observed to have thresholds above
5% w/v, the maximal concentration used in this study. We
treated these responses above 5% as right censored observa-
tions. Correlations were evaluated using Spearman’s rho (ρ).
Bonferroni corrections for multiple comparisons were made.
Chi-square tests examined the probability of group member-
ship (reliable or unreliable responder). The level of signifi-
cance was set at P <0.05, 2 tailed. Data were analyzed using
IBM SPSS Statistics 20, SAS v9.2, and Microsoft Excel 2010.
Results
Emulsion particle size measurements were made twice using
2 different samples. The average volume weighted mean
(D[4,3]) was 2.8 µm. Viscosity measurements at 50 s
−1
did
not differ between the 5% OA emulsion (0.01024 Pa·s) and
the vehicle (0.01005 Pa·s).
Results from the NLMIXED procedure demonstrated that
the intercepts of the lean versus overweight and lean versus
obese were not different (P > 0.05). However, the intercepts
of the overweight compared with the obese were different
(P = 0.040). The slopes of the regression lines between lean
and overweight were not different (P > 0.05), but the slope
of the obese differed from both the lean (P = 0.013) and the
overweight (P = 0.008). As the slopes and intercepts did not
differ between the lean and overweight, these 2 groups were
combined into 1 group while retaining the obese as a sepa-
rate group. The fit statistics for the 2-group model (lean plus
overweight vs. obese) indicated it was superior to the 3-group
model (lean vs. overweight vs. obese) (Bayesian information
criterion [BIC] = −249.5 vs. −243.4). Regression lines for
the 2-group model are shown in Figure 1. The intercepts are
not significantly different (P > 0.05); however, the slopes are
still different (P = 0.005). There were also significant differ-
ences in threshold sensitivity at visits 6 (P = 0.041) and 7
(P = 0.012).
Figure 1 Regression lines ± standard error by BMI group (lean and over-
weight = circles; obese = squares) over the course of 7 visits. Significant
differences in thresholds were observed at visits 6 and 7. The slopes of the
regression lines were significantly different (P = 0.005).
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4. 352 R.M. Tucker et al.
Thresholds that exceeded 0.18 M (5% w/v OA) occurred
in 47.9% (N = 23) of individuals at baseline and in 16.7%
(N = 8) of people at visit 7. All participants achieved a
threshold at least once during testing, and 31.3% (N = 15) of
participants obtained a threshold at each visit. Chi-square
analysis revealed that neither BMI group nor sex was asso-
ciated with the likelihood of the overall median threshold
being greater than 5%, and performance over time did not
differ by sex. However, 13 (27.1%) participants had a median
threshold greater than 5%, and 9 of these participants were
overweight (binomial distribution, P = 0.09). A review of
the data revealed 2 response patterns, one where once par-
ticipants obtained their first threshold they were reliable per-
formers and obtained a threshold at each subsequent visit
(reliable; N = 20), and one where those who did not consist-
ently obtain a threshold (unreliable; N = 28). Post hoc analy-
ses demonstrated that BMI did not predict reliability. The
intercepts of the 2 groups were not different, but the slopes
did show a strong trend for significant difference (P = 0.052)
(Figure 2).
Correlationswereobservedbetweeneachvisitandthemean
threshold concentration for the whole group (r = 0.495–
0.697; P < 0.001) (Supplementary Table S1). Thresholds
appeared to achieve stability, defined as each visit being sig-
nificantly associated with the visit preceding and following it,
at visit 5 (r = 0.477–0.665; P < 0.001). Correlations between
baseline and mean threshold concentrations followed dif-
ferent patterns in the lean plus overweight compared with
the obese (Table 1). Later visits tended to correlate with the
mean threshold in the obese, but the opposite was found in
both the lean and lean plus overweight groups with earlier
visits associated with mean performance. When tested, the
mean of the first 5 visits for the lean plus overweight groups
was significantly higher (less sensitive) than the mean of the
last 2 visits (P = 0.007). This was not the case for the obese
participants. Correlations between visits for lean only and
overweight only are shown in Supplementary Table S2.
Total fat grams, saturated fat grams, and percent fat were
significantly correlated with each other (r = 0.862–0.981;
P < 0.001). Regardless of weight category, saturated fat
intake was positively correlated with baseline sensitivity
among all participants; that is, as intake increased, threshold
concentrations increased (sensitivity decreased) (Table 2).
The intake of fat, as measured by BRFS, did not differ
among lean, overweight, and obese individuals (P > 0.05).
Among the lean plus overweight participants, total fat intake
in grams at baseline was significantly associated with thresh-
old sensitivity (r = 0.571; P = 0.001). All 3 BRFS fat meas-
ures were significant and positively correlated with overall
mean thresholds (total fat r = 0.561; P < 0.001; saturated fat
grams: r = 0.521; P = 0.001; percent fat: r = 0.573; P < 0.001).
No consistent pattern of associations between thresholds
and hunger were observed. Although the lean reported sig-
nificantly higher average hunger compared with the obese
(45.6 ± 3.20 mm vs. 31.0 ± 5.2 mm; P = 0.04), the overweight
did not differ from the lean or obese (39.0 ± 4.8 mm). Average
hunger levels were not associated with intake across BMI
groups.
Discussion
Threshold sensitivity to OA was measured in lean, over-
weight, and obese individuals over the course of 7 test visits.
Statistical analyses revealed no differences between the lean
and overweight group, so the 2 groups were combined (lean
plus overweight). The slope of threshold concentrations ver-
sus visit was significantly different and negative for the lean
plus overweight compared with the positive slope for the
obese participants. This indicates that the rate of improve-
ment was significantly greater in the lean plus overweight
group compared with the obese. The best estimate of the
lower limits of OA acid detection (visit 7) indicated the obese
were less sensitive than the lean plus overweight participants.
Although the present data do not permit a determination
for the discrepant responses of the obese versus the other
groups, BMI-based differences in externality and learning
offer possible explanations. The obese may be more attuned
to external stimuli (Nisbett 1968; Schachter 1968) and NEFA
sensory cues than individuals with lower adiposity. Thus, they
were already operating at the limits of their capabilities so did
not improve over time. In contrast, the lean and overweight
participants were initially less sensitized to NEFA concen-
trations in their environment (foods) so had greater capacity
for improvement. Rodent models suggest deficits in learning
and memory in obese animals compared with lean controls
Figure 2 Regression lines ± standard error by reliability grouping over
the course of 7 visits. Reliable performers (open circles) obtained their first
threshold and obtained a threshold at each subsequent visit (N = 18), com-
pared with those who did not consistently obtain a threshold (unreliable;
N = 30; triangles). A trend for significance (P = 0.052) was noted between
the 2 slopes.
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5. BMI and Fat Taste Sensitivity 353
(Farr et al. 2008). Weight loss via either Roux-en-Y gastric
bypass surgery or caloric restriction resulted in improvements
in memory in formerly obese rats (Grayson et al. 2013). In
humans, memory deficits were also observed in obese adults
compared with lean and overweight individuals, and these
findings were independent of age (Gunstad et al. 2006).
The increased sensitivity over time among the lean plus over-
weight may constitute an improvement in actual sensitivity or
an improvement in testing performance. Both explanations
have previously been invoked to explain increased sensitivity
after repeated testing (Eylam and Kennedy 1998; Kobayashi
and Kennedy 2002; Kobayashi et al. 2006; Tucker and Mattes
2013). An improvement in sensitivity could be the result of
increased receptor expression (Wysocki et al. 1989), represent-
ing a true, biologically mediated improvement in sensitivity.
Recent work in rodents suggests that expression levels of one
of the putative long chain fatty acid receptors, CD36, is influ-
enced by acute oral exposure to NEFA (Martin et al. 2011).
Although fasted lean and obese mice did not differ in terms of
expression level (Chevrot et al. 2013), postprandial responses
Table 1. Spearman’s rho (ρ) correlation coefficients for each visit by BMI group
Visit 1 Visit 2 Visit 3 Visit 4 Visit 5 Visit 6 Visit 7 Mean
Lean + overweight (N = 35)
Visit 1 ρ 1.000 0.565 0.498 0.285 0.397 0.158 0.358 0.763
P value <0.001 0.002 0.097 0.018 0.365 0.035 <0.001
Visit 2 ρ 1.000 0.447 0.411 0.448 0.285 0.245 0.729
P value 0.007 0.014 0.007 0.096 0.156 <0.001
Visit 3 ρ 1.000 0.474 0.528 0.235 0.480 0.686
P value 0.004 0.001 0.175 0.004 <0.001
Visit 4 ρ 1.000 0.734 0.369 0.480 0.658
P value <0.001 0.029 0.004 <0.001
Visit 5 ρ 1.000 0.407 0.455 0.728
P value 0.015 0.006 <0.001
Visit 6 ρ 1.000 0.347 0.469
P value 0.041 0.005
Visit 7 ρ 1.000 0.483
P value 0.003
Obese (N = 13)
Visit 1 ρ 1.000 0.162 −0.116 0.253 0.143 0.015 0.319 0.399
P value 0.598 0.705 0.404 0.642 0.962 0.289 0.177
Visit 2 ρ 1.000 −0.288 0.181 0.268 0.545 0.491 0.507
P value 0.340 0.555 0.376 0.054 0.088 0.077
Visit 3 ρ 1.000 −0.145 0.020 0.007 0.060 −0.080
P value 0.635 0.949 0.982 0.845 0.795
Visit 4 ρ 1.000 0.531 0.514 0.706 0.764
P value 0.062 0.072 0.007 0.002
Visit 5 ρ 1.000 0.539 0.510 0.673
P value 0.058 0.075 0.012
Visit 6 ρ 1.000 0.683 0.790
P value 0.010 0.001
Visit 7 ρ 1.000 0.875
P value <0.001
Bold values denote a significant association (P ≤ 0.001).
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6. 354 R.M. Tucker et al.
differed between the lean and obese, with a decrease in expres-
sion in the lean an hour after the start of exposure. If rodents
are an appropriate model for human fat taste, this would sug-
gest that the obese would remain more sensitive during a test-
ing session, which our data do not support.
Understanding of the genetics of fat taste is in its infancy,
but previous work in a sample of 21 obese individuals sug-
gests that single nucleotide polymorphisms in the CD36 gene
were associated with sensitivity to OA (Pepino et al. 2012).
Thus, genetic differences may contribute to sensitivity dif-
ferences. In the present trial, more than 25% of participants
had a median threshold greater than 5%, but BMI did not
predict the likelihood of falling into this hyposensitive cat-
egory. Genetic differences may also explain the reliable and
unreliable performer subgroups that were observed as BMI
did not predict membership in these categories.
A more likely explanation for the improvement in the lean
and overweight is an improvement in performance (lower
thresholds) through training. If learning is responsible for
the improvement over time in the lean and overweight but
not obese, an explanation for this differential response pat-
tern is required. One theory is that a Western-style diet (high
fat, high sugar) contributes to neurodegeneration (Francis
and Stevenson 2013). Although most studies supporting
this hypothesis have been conducted in rodents or rely on
epidemiological associations, 1 intervention study fed 20
men a high-fat diet (74% of energy from fat) for 7 days after
a 3-day lead-in period (Edwards et al. 2011). Measures
of attention and awareness were significantly lower after
the high-fat diet intervention. Another study relying on a
similar design found diminished attention after 5 days on
a high-fat diet (Holloway et al. 2011). High-fat diet expo-
sure resulted in increased OA acid thresholds among lean
participants in previous work with no change in thresholds
in the overweight participants (Stewart and Keast 2012).
While we failed to detect differences in fat intake among
the lean, overweight, and obese participants, fat is often
regarded as unhealthy and is frequently underreported
(Briefel et al. 1997), especially by the obese (Livingstone
and Black 2003).
Evidence for detection threshold differences between lean
and overweight/obese participants is mixed. In contrast to
the present findings, there are other reports that overweight/
obese individuals have higher detection thresholds than their
lean counterparts with fewer than 4 test visits (Stewart et al.
2010; Stewart, Newman, et al. 2011; Stewart, Seimon, et al.
2011). Prior reports have not clearly described the level of
sensory testing experience of study participants; that is, if
subjects participate in multiple studies, learning could con-
found the results. The present work suggests this may be an
important methodological factor and could account for the
apparently inconsistent reports.
Thresholds for the group appeared to stabilize at visit
5. Limited power likely explains the lack of correlations
within the separate BMI groups. However, correlations
between threshold concentrations among visits followed
different patterns in the lean and lean plus overweight com-
pared with the obese. Later visits tended to correlate with
the mean threshold in the obese, but the opposite was found
in the lean and lean plus overweight groups with earlier vis-
its associated with mean performance. The fact that the lean
plus overweight group had a lower mean threshold for the
last 2 visits versus the first 5 suggests they were continuing
to improve.
Comparing the detection threshold results of one study to
another can be challenging due to differences in methodol-
ogy, stimulus preparation, vehicle composition, sample char-
acteristics, and so on. This is evidenced by a comparison to
our previous work that compared the effect of different test-
ing methodologies on OA detection thresholds (Tucker et al.
2013). Differences in threshold values between this study and
the previous are an order of magnitude higher in this study.
A number of factors distinguish this study from the first
study including more obese participants and the use of cen-
soring rather than assigning a value for those participants
without a threshold; each would tend to increase threshold
values.
By nearly all accounts, NEFA are aversive stimuli, unlike
triacylglycerol (TAG), the predominant form of dietary fat.
TAG generally imparts desirable sensory qualities to foods
Table 2. Spearman’s rho (ρ) correlation coefficients for diet-taste relationships and BMI group
Total fat (g) Saturated fat (g) Percent fat (%)
Baseline Mean Baseline Mean Baseline Mean
Whole group
(N = 48)
ρ NS NS 0.474 NS NS NS
P value 0.001
Lean plus
overweight
(N = 35)
ρ 0.517 0.561 NS 0.521 NS 0.573
P value 0.001 <0.001 0.001 <0.001
Obese (N = 13) ρ NS NS NS NS NS NS
P value
Bold values denote a significant association (P ≤ 0.001).
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7. BMI and Fat Taste Sensitivity 355
such as creaminess and lubricity and may plausibly be directly
related to BMI (Zverev 2004). In this study and previously,
participants overwhelmingly described OA, a NEFA, as bit-
ter, although sour was also frequently mentioned (Tucker
et al. 2013). Participants also used adjectives associated with
flavors like “plastic,”“woody,”or “dirty.”Humans often learn
to appreciate tastes and flavors that are initially unpleasant,
like those of strong cheeses, fermented products, or alcohol.
Participants were asked to describe the taste quality of the
OA at each session (data not reported); none of the partici-
pants reported developing a liking for the stimulus despite
hundreds of exposures, though they also did not receive
postingestive feedback from consuming the OA during these
trials. While sensitivity is not necessarily a strong predictor
of preference and/or consumption (Moskowitz et al. 1974;
Lauer et al. 1976; Wise et al. 2007; Donaldson et al. 2009),
the presumed antecedents of weight gain, if there is an asso-
ciation between NEFA sensitivity and BMI, it would likely
be inverse—reduced intake of an unpleasant, energy-dense
stimulus. In agreement with this view, the present data reveal
an inverse relationship between threshold sensitivity and fat
intake. However, the association is strongest in lean and lean
plus overweight individuals, so the nutritional implications
of the relationship are questionable.
Our analysis revealed no meaningful associations between
hunger and OA thresholds. Others have also failed to
find associations between hunger and NEFA sensitivity
(Kamphuis et al. 2003). Differences in sensitivity by sex were
also not observed in this study.
The relationship between textural attributes and fat con-
tent of foods and emulsions has been studied extensively.
Particle size and viscosity are frequently cited as predictors
of fat content. The particle size of the emulsions presented
to participants in this study should not have allowed partici-
pants to discriminate between the vehicle and the stimulus
based on this potential textural cue. While physical properties
do not always accurately translate into consumer experience
(Kokini 1987; Akhtar et al. 2005), others have reported that
panelists could not discriminate between emulsions with a
mean droplet size of 0.5 µm and another with a mean drop-
let size of 2.3 µm (Akhtar et al. 2005). One study reported
that particles must be greater than 5.0 µm to be detected in
the oral cavity (Tyle 1993). Greater viscosity may also con-
tribute to the perceived fat content of foods and emulsions
(Mela 1988). The viscosities of the vehicle and the highest fat
containing stimulus were not different from each other at the
shear rate that may correspond most closely to that occurring
in the oral cavity (Wood 1968; Richardson et al. 1989), mak-
ing it unlikely that participants perceived viscosity cues that
allowed them to distinguish between the samples presented.
Our work suggests differences of fat taste sensitivity between
BMI categories under conditions of repeated testing where
thresholds decline for the lean and overweight but remain sta-
ble in the obese. Thus, the lean and overweight had lower limits
of detection for OA. Increased dietary fat intake was associated
with decreased NEFA taste sensitivity, but only in the lean and
lean plus overweight participants, so the nutritional implica-
tions are questionable. Underreporting of dietary fat intake
by obese individuals may have contributed to the failure to
observe diet-taste associations in this group. While possibly
unrelated to BMI, a subgroup of the population appears to be
relatively insensitive to NEFA taste. Further exploration of the
genetic contribution to fat taste will likely yield further insight
into individual differences in sensory responses.
Supplementary material
Supplementary material can be found at http://www.chemse.
oxfordjournals.org/
Funding
This work was supported by NIH/NCATS-Indiana Clin­
ical and Translational Sciences Institute–TL1 Program
[A. Shekhar, PI; 5/01/08-04/30/13] and the United States
Department of Agriculture [HATCH IND084055].
Acknowledgements
We wish to thank Dr Ganesan Narsimhan, Ms Laura Zimmerer,
and Ms Cordelia Running for assistance with the particle size meas-
urements. We also thank Dr Osvaldo Campanella for the use of the
rheometer and Ms Cordelia Running for assistance with the viscos-
ity measurements.
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