European Journal of Clinical Nutrition (2011) 65, 386–393
& 2011 Macmillan Publishers Limited All rights reserved 0954-3007/11
www.nature.com/ejcn
ORIGINAL ARTICLE
Differential effects of walnuts vs almonds on
improving metabolic and endocrine parameters
in PCOS
S Kalgaonkar1, RU Almario1, D Gurusinghe1, EM Garamendi1, W Buchan2, K Kim3 and
SE Karakas1,4
1
Department of Internal Medicine, Division of Endocrinology, Diabetes and Metabolism, The University of California at Davis,
Davis, CA, USA; 2Family and Consumer Sciences Department, California State University Sacramento, Sacramento, CA, USA;
3
The Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California at Davis, Davis,
CA, USA and 4Department of Veterans Affairs Northern California Health Care System, Mather, CA, USA
Background/Objectives: Polycystic ovary syndrome (PCOS) is commonly associated with insulin resistance, dyslipidemia
and increased inflammation, which all benefit from dietary intake of monounsaturated and n-3 polyunsaturated fatty acids
(MUFA and n-3 PUFA). Our goal was to compare the effects of MUFA-rich almonds vs n-3/n-6 PUFA-rich walnuts on metabolic
and endocrine parameters in PCOS.
Subjects/Methods: Thirty-one PCOS patients randomly received either walnuts or almonds containing 31 g of total fat per day
for 6 weeks. At the beginning and at the end, anthropometric parameters, fasting lipids, phospholipid-fatty acids, inflammatory
markers, androgens, oral glucose tolerance tests (OGTT) and frequently sampled intravenous-GTT were obtained.
Results: Weight remained stable. Within group, walnuts increased the n-3/n-6 essential PUFA in the diet and plasma
phospholipids. Walnuts decreased low-density lipoprotein-cholesterol by 6% from 3.76±0.27 to 3.38±0.22 mmol/l (P ¼ 0.05)
and apoprotein B by 11% from 0.72±0.04 to 0.64±0.05 g/l (Po0.03). Although almonds also reduced low-density
lipoprotein-cholesterol by 10% and apoprotein B by 9%, these were not significant. Walnuts increased insulin response during
OGTT by 26% (Po0.02). Both walnuts and almonds increased adiponectin (walnuts from 9.5±1.6 to 11.3±1.8 mg per 100 ml,
P ¼ 0.0241; almonds from 10.1±1.5 to 12.2±1.4 mg/dl, P ¼ 0.0262). Walnuts decreased HgBA1 from 5.7±0.1 to 5.5±0.1%
(P ¼ 0.0006) with significant intergroup difference from almonds (P ¼ 0.0470). Walnuts increased sex hormone-binding globulin
from 38.3±4.1 to 43.1±4.3 nmol/l (P ¼ 0.0038) and almonds reduced free androgen index from 2.6±0.4 to 1.8±0.3
(P ¼ 0.0470).
Conclusion: Nut intake exerted beneficial effects on plasma lipids and androgens in PCOS.
European Journal of Clinical Nutrition (2011) 65, 386–393; doi:10.1038/ejcn.2010.266; published online 15 December 2010
Keywords: PCOS; almonds; walnuts; monounsaturated fatty acids; polyunsaturated fatty acids
Introduction
Polycystic ovary syndrome (PCOS) affects 1 out of 16 young
women (Ehrmann, 2005). These patients have irregular
menstrual periods and infertility, and elevated androgen
levels causing excess facial and body hair. In the USA,
Correspondence: Dr SE Karakas, Division of Endocrinology, Clinical Nutrition
and Vascular Medicine, University of California at Davis, 4150 V Street, PSSB
Suite G400, Sacramento, CA 95817, USA.
E-mail: [email protected]
Received 7 May 2010; revised 8 November 2010; accepted 9 November
2010; published online 15 December 2010
majority of PCOS patients are obese and B50% have
metabolic syndrome (Glueck et al., 2003; Ehrmann et al.,
2006)—characterized by insulin resistance, hyperlipidemia,
hypertension and increased inflammation (Dunaif, 2000).
Consequently a large number of young women seeking
help from nutritionists for treatment of obesity and hyperlipidemia may have PCOS.
Metabolic disorders associated with PCOS can benefit from
monounsaturated and polyunsaturated fatty acids (MUFA
and PUFA). The MUFA and PUFA improve plasma lipids, and
PUFA (especially n-3) increases insulin sensitivity, decreases
blood pressure and inflammation (Vessby, 2000; Sacks and
Dietary fatty acids and PCOS
S Kalgaonkar et al
387
Campos, 2006; Berglund et al., 2007). As nuts are rich sources
of MUFA and PUFA, PCOS patients are frequently advised to
increase nut intake. In general, such recommendations do
not consider significant differences in fatty acid compositions of nuts. For example, almonds contain 30% MUFA
whereas walnuts contain 9% MUFA. Their PUFA contents
and composition also differ. Although almonds contain 12%
PUFA (all n-6 class), walnuts contain 47% PUFA (with 1:4
n-3/n-6 ratio). American diet can have n-3:n-6 PUFA ratio up
to 1:20 (Simopoulos et al., 2000). This is important because
n-3 vs n-6 PUFA exert contrasting biological effects.
For example, although n-3 PUFA increases insulin sensitivity
and reduces insulin levels, n-6 PUFA may stimulate insulin
secretion. Although n-3 PUFA are anti-inflammatory and
anti-coagulant, n-6 PUFA are pro-inflammatory and procoagulant (Abbate et al., 1996). In order to compare the
metabolic and endocrine effects of MUFA vs PUFA rich nuts,
we investigated the effects of almonds vs walnuts on
anthropometric parameters, plasma fatty acids, plasma
lipids, inflammatory markers, glucose homeostasis and
androgens in PCOS.
Subjects and methods
This study was approved by the Institutional Review Board of
University of California, Davis, CA, USA. Women between the
ages 20–45 years and with a body mass index of 25–45 kg/m2
fulfilling the NIH criteria for PCOS (Azziz, 2005) were recruited.
Patients were excluded if they used oral contraceptives, antiandrogens, insulin sensitizers, d-chiro inositol, or any other
medicines or supplements affecting weight or insulin sensitivity during the preceding 2 months; had diabetes mellitus,
untreated thyroid disease and any other systemic illness such
as renal, hepatic and gastrointestinal disease; smoke; or drink
42 alcoholic drinks per week.
Consort statement
A total of 142 PCOS patients were assessed for eligibility; 90
subjects failed to meet the inclusion criteria: 52 were consented
and screened; 16 did not qualify; remaining 36 were
randomized to treatment groups (n ¼ 18 per group). Four in
the almond and one in the walnut group dropped out (one due
to pregnancy, one transportation, one diarrhea, one personal
reasons and one for having difficulty with blood draw).
Thirty-one women (22 White, four Hispanic, two African
American, two Asian and one mixed) completed the study.
Study design
This was a randomized, prospective study with two parallel
arms. The goal was to exchange 31 g of the habitual dietary
fats per 1800 kcal with nuts (equivalent of 46 g of almonds or
36 g of walnuts) for 6 weeks, which is adequate for changes in
plasma lipids (Schaefer et al., 1995). Although 46 g almonds
or 36 g walnuts each contain 31 g oil, their compositions
differ: 31 g of almond-oil delivers 2.4 g saturated fat, 19.5 g
MUFA, 7.5 g linoleic acid (LA; 18:2 n-6) and no a-linolenic
acid (ALA; 18:3 n-3; Abbate et al., 1996) whereas 31 g walnutoil delivers 2.9 g saturated fat, 4.5 g MUFA, 19.2 g LA and
4.3 g ALA. The fat exchange was accomplished as described
previously (Kasim-Karakas et al., 2004). Amount of nut
intake was based on average daily energy intake, which was
assessed using 7-day food records (Food Processor SQL
software; ESHA Research, Salem, OR, USA). The subjects
were counseled by the Clinical Research Center dietitian to
maintain fat and energy intakes constant. Daily allotments
of the nuts were individually packaged for each subject. To
assess compliance, the participants were asked to return
unused portions of the nuts.
Data collection
Data were obtained at the beginning and at the end of the
study. The oral glucose tolerance test (OGTT) and frequently
sampled intravenous glucose tolerance test were performed
1 week apart.
Anthropometrics
Weight was measured in light clothing using the Tanita
BWB800-P Digital Medical Scale (Tokyo, Japan). Height was
measured using an Ayrton Model S100 stadiometer (Ayrton
Corp, Prior Lake, MN, USA).
OGTT
After an overnight fast, baseline samples were obtained;
participants drank 75 g of glucose (Glucola, Fisher Healthcare,
Houston, TX, USA) and additional blood samples were
obtained every 30 min for 2 h. Average carbohydrate intake
was 237±16 g/day before OGTT.
Frequently sampled intravenous glucose tolerance test
After an overnight fast, an intravenous catheter was placed
in their forearm and kept open with normal saline. Heating
pads were used in order to maximize blood flow. Three
blood samples were obtained at time 15, 10 and 5 min.
Glucose (0.3 U/kg as 25% dextrose) was given intravenously
at time 0 min. Intravenous insulin 0.03 U/kg (Humulin
Regular, Eli Lilly, Indianapolis, IN, USA) was given at time
20 min after the glucose administration. Blood samples were
obtained at time 0, 2, 3, 4, 5, 6, 8,10, 12, 14, 16, 19, 22, 23,
24, 25, 27, 30, 40, 50, 60, 70, 90, 100, 120, 140, 160 and
180 min. Acute insulin response to glucose (AIRGlucose: an
index of insulin secretion), b-cell function, sensitivity index
and disposition index were calculated using MiniMod
Millennium software (Dr Bergman, Los Angeles, CA, USA).
Biochemical measurements
Glucose was measured using YSI 2300 STAT Plus Glucose and
Lactate Analyzer (YSI Life Sciences, Yellow Springs, OH, USA),
European Journal of Clinical Nutrition
Dietary fatty acids and PCOS
S Kalgaonkar et al
388
with coefficient of variation (CV) of 1%. Insulin was
measured by radioimmunoassay (Millipore, St Charles, MO,
USA) with a CV of 8.2%. The homeostatic model assessment
and Matsuda Index, surrogate measures of hepatic and
peripheral insulin sensitivity, were calculated as previously
published (Karakas et al., 2010). Triglyceride, cholesterol
and high-density lipoprotein-cholesterol were measured
using Poly-Chem System Analyzer (Polymedco, Inc.,
Cortlandt Manor, NY, USA) with CVs of 3.5, 4 and 3.6%,
respectively. Leptin and adiponectin were measured using
radioimmunoassay (Millipore) with CVs of 4.3 and 6.5%. The
high-sensitivity C-reactive protein was measured using a
highly sensitive latex-enhanced immunonephelometric assay
with CV o5%. Interleukin (IL)-6, IL-1b, tumor necrosis factora were measured using the High Sensitivity Human Cytokine
Panel-3 Plex (Milliplex) kit (Millipore) with CV of 11%. Total
testosterone, sex hormone binding globulin (SHBG) and
dehydroepiandrosterone sulfate were measured by radioimmunoassay (Diagnostic Systems Laboratories, Webster,
TX, USA) with CVs of 8.3, 4.4 and 9.6%, respectively. Fatty
acid composition was measured using gas chromatography by
the Lipid Technologies, LLC (Austin, MN, USA).
Statistical analysis
The SAS software, version 9.1 (SAS Institute Inc., Cary, NC,
USA) was used. Descriptive statistics were calculated for each
outcome, for each group, before and after intervention. The
data were log-transformed in order to improve the normality
of residuals and homoscedasticity of errors as appropriate
before analysis. Paired t-test was performed to determine the
significance of within-group change. Two-group comparison
was performed by analysis of covariance, adjusted for the
baseline values. The longitudinal trajectories for changes in
glucose and insulin during OGTT were estimated by repeated
measures analysis of variance. Individual trajectories for
changes in glucose and insulin were estimated from linear
random effects models. Each observed level was entered as the
dependent variable. Treatment, time and treatment time
interaction term were entered as independent variables. The
coefficients for the interaction term were to estimate
the additional changes in glucose and insulin level over time
associated with treatment. To account for between-subject
heterogeneity in the change of glucose or insulin level,
intercept and time were modeled as random effects. Multiple
comparisons were controlled by the Bonferroni method. An
as-treated analysis was performed. We chose not to perform an
intent-to-treat analysis because those who dropped out had
only the baseline, but no interim, data. At the planning stage,
the sample size was calculated to detect differences between
groups in insulin at 6 weeks. A sample size of 18 in each group
would yield 80% power to detect differences of 0.85 s.d. of the
mean for each of these outcomes with a 0.05 two-sided a-level.
At the end of the study, the number of completers was not
adequate to detect the significant differences in all parameters.
Results
Changes in weight and diet
Weight did not change in either group (Tables 1 and 3). The
participants consumed all the nuts. The 7-day food records
demonstrated that percent fat intake did not change; before
and after values for percentage of fat were 36.9±1.9
Table 1 Effects of almond and walnut supplementations on the daily intake of the nutrients
Almonds (n ¼ 14)
Energy (kJ)
Fat
(g)
(%)
CHO
(g)
(%)
Protein
(g)
(%)
Saturated fat (g)
MUFA (g)
LA (g)
ALA (g)
Cholesterol (mg)
Fiber (g)
Walnuts (n ¼ 17)
P almonds
Before
After
D
8164±578
7189±364
976±490
73±6
33±1
74±5
39±2
248±24
50±1
188±15
44±2
79±5
17±1
39±7
25±3
5.5±1.1
0.91±0.25
275±39
16±2
81±7
19±1
29±5
35±3
7.5±0.6
0.34±0.07
225±26
18±2
P walnuts
P almond vs
walnuts
38±653
0.9230
0.3589
Before
After
0.2543
6414±452
6448±628
0.9419
0.1647
59±4
35±1
69±6
40±1
10±8
6 ±1
0.3608
0.0086
0.5508
0.9465
60±14
7±2
0.0650
0.0536
181±11
48±1
174±21
45±2
8±15
3±1
0.6869
0.0468
0.1058
0.1588
2 ±2
2 ±1
10±5
9 ±4
2.0±1.3
0.58±0.20
50±31
2 ±2
0.5812
0.0667
0.0419
0.3482
0.3994
0.1553
0.3185
0.5073
71±8
18±1
32±2
21±3
3.5±1.5
0.37±0.03
289±62
10±1
62±6
17±1
32±3
19±3
15.0±1.0
3.25±0.3
167±30
15±2
8±5
2±1
0.2±4
2±3
12±2
2.88±0.30
122±37
5 ±1
0.2546
0.3274
0.6919
0.7270
0.0047
0.0013
0.0446
0.0605
0.2142
0.0567
0.6200
0.2611
0.0163
0.0002
0.5459
0.2899
1 ±7
5 ±2
D
Abbreviations: ALA, a-linoleic acid; MUFA, monounsaturated fatty acids.
Values are means±s.e.m.
P almonds, P walnuts: significance for the change between the baseline and the 6 weeks within a group as determined by paired t-test.
P almonds vs walnuts: significance comparing the changes between the almonds and the walnut groups after baseline adjustment.
Significant values are in bold.
European Journal of Clinical Nutrition
Dietary fatty acids and PCOS
S Kalgaonkar et al
389
Table 2 Changes in phospholipid fatty acids (%)
Almonds (n ¼ 5)
Before
After
Walnuts (n ¼ 6)
P almonds
Before
D
Oleic acid 18:1 (n-9)
7.73±0.37 8.46±0.16
0.73±0.30
Linoleic acid 18:2 (n-6)
15.50±0.83 14.16±0.92 1.33±0.68
a-Linolenic acid 18:3 (n-3)
0.17±0.02 0.17±0.03
0.00±0.01
Arachidonic acid 20:4 (n-6) 9.24±0.62 9.56±0.82
0.32±0.37
EPA 20:5 (n-3)
0.47±0.06 0.39±0.04 0.08±0.05
DHA 22:6 (n-3)
2.40±0.51 1.98±0.17 0.42±0.43
0.5790
0.1440
0.6929
0.0240
0.5826
0.7207
After
P walnuts P almonds vs
walnuts
D
8.19±0.52 7.51±0.32 0.68±0.61
16.44±0.91 17.60±0.55
1.16±0.67
0.19±0.01 0.22±0.01
0.03±0.02
7.86±0.64 7.10±0.39 0.76±0.29
0.41±0.05 0.44±0.04
0.03±0.03
2.20±0.26 1.78±0.15 0.42±0.13
0.4928
0.2292
0.2834
0.0473
0.1008
0.0918
0.6142
0.0477
0.0142
0.0355
0.3858
0.2204
Abbreviations: DHA, docosahexanoic acid; EPA, eicosapentanoic acid.
Values are means±s.e.m.
P almonds, P walnuts: significance for the change between the baseline and the 6 weeks within a group as determined by paired t-test.
P almonds vs walnuts: significance comparing the changes between the almonds and the walnut groups after baseline adjustment.
Significant values are in bold.
vs 39.5±1.9 (P ¼ 0.2839) in the almond group and 36.6±1.4
vs 38.6±1.4 (P ¼ 0.3595) in the walnut group. The two
groups had significantly different LA and ALA intakes
(P ¼ 0.0163 and P ¼ 0.0002, respectively). Within group
changes indicated that walnuts increased ALA intake by
11-folds (from 0.3±0.1 to 3.5±0.03 g, Po0.0001) and LA
intake by fivefolds (from 3.2±0.5 to 16.0±1.5 g, Po0.0001),
but did not alter saturated fat or MUFA intakes. Almonds
decreased saturated fat intake (from 29.2±4.7 to 22.2±2.7 g,
P ¼ 0.0419) and tended to increase MUFA (from 17.7±3.4 to
23.2±2.7 g, P ¼ 0.1991).
Changes in fatty acid composition of plasma phospholipids
There were significant differences between the groups for
changes in LA (P ¼ 0.0477), ALA (P ¼ 0.0142) and arachidonic acid (P ¼ 0.03555) (Table 2). Within group, walnuts
increased LA (18:2 n-6) by 7% from 16.44±0.91 to
17.60±0.55% and ALA (18:3 n-3) by 16% from 0.19±0.01
to 0.22±0.01%. Walnuts did not increase the long chain n-3
PUFA eicosapentanoic acid (EPA) or docosahexanoic acid
(DHA). Almonds increased (P ¼ 0.0240) and walnuts
decreased (P ¼ 0.0473) arachidonic acid (AA; 20:4 n-6).
Although almonds increased the oleic acid (18:1 n-9) by
9% and walnuts decreased it by 8%, these changes were not
significant.
Changes in glucose homeostasis, insulin secretion and action
The only significant difference between the treatment effects
was seen in HgBA1c (P ¼ 0.047) while the change in
AUCInsulin approached significance (P ¼ 0.0628) (Table 3;
Figures 1 and 2). Walnuts decreased HgBA1 (from 5.7±0.1 to
5.5±0.1%, P ¼ 0.0006). Although almonds also caused a
slight decrease in HgBA1c from 5.8±0.1 to 5.7±0.1%, this
change was not significant (P ¼ 0.1309). Neither treatment
changed fasting glucose, insulin or homeostatic model
assessment.
As seen in Figure 1, neither almonds nor walnuts changed
glucose response during OGTT. Walnuts increased insulin
response (Figure 2), and AUCInsulin by 26% (from 2556±351
to 3227±519 pmol/l per 2 h, P ¼ 0.0182), whereas almonds
did not affect insulin. Walnuts or almonds did not change
any of the intravenous glucose tolerance test parameters.
Both treatments increased adiponectin (almonds by 21%;
2.1 mg per 100 ml, P ¼ 0.0192 and walnuts by 19%; 18 mg per
100 ml, P ¼ 0.0179) and walnuts increased leptin by 18%
(from 25.7±3.0 to 30.3±3.7 ng/ml, P ¼ 0.0416).
Changes in cardiovascular risk factors
There were no differences between the treatments for plasma
lipids and inflammatory risk factors (Table 3). Within groups,
fasting triglyceride was not affected by either treatment.
Walnuts decreased low-density lipoprotein-cholesterol by
6% or 0.24±0.1 mmol/l (P ¼ 0.05) and apoprotein B by
11% or 0.08±0.03 g/l (P ¼ 0.0236). Although walnuts also
decreased high-sensitivity C-reactive protein by 11.5% and
IL-1b by 23%, these changes were not significant. Decreases
caused by almonds in low-density lipoprotein-cholesterol
(10%), apoprotein B (9%), IL-6 (19%) and tumor necrosis
factor-a (20%) were not significant either (Table 3).
Changes in androgens
There were no differences between the treatments for
androgens (Table 3). Within groups, walnuts increased SHBG
by 12.5% (from 38.3±4.1 to 43.1±4.3 nmol/l, P ¼ 0.0104).
Almonds increased SHBG by 16% (P ¼ 0.0596). As SHBG
binds testosterone avidly, increased SHBG can decrease the
free fraction of androgens. Therefore, free androgen index
was calculated. Almonds reduced free androgen index from
2.6±0.4 to 1.8±0.3, P ¼ 0.0491. Testosterone and dehydroepiandrosterone sulfate levels did not change.
Discussion
Nut intake altered fatty acid composition of the diet.
Almonds increased MUFA intake by 33% and decreased
saturated fat by 25% without altering n-3 or n-6 PUFA.
European Journal of Clinical Nutrition
Dietary fatty acids and PCOS
S Kalgaonkar et al
390
Table 3 Changes in metabolic and inflammatory variables, testosterone, sex hormone-binding globulin, free androgen index and dehydroepiandrosterone sulfate
Almonds (n ¼ 14)
Walnuts (n ¼ 17)
P almonds
P walnuts P almonds vs
walnuts
Before
After
D
Before
After
D
Age (years)
36.2±1.7
—
—
31.2±1.7
—
—
Anthropometrics
Weight (kg)
BMI (kg/m2)
97.1±6.2
35.1±1.8
96.7±6.4
35.4±2.2
0.4±0.5
0.3±0.6
101.0±5.3
35.2±1.6
100.5±5.3
35.1±1.6
0.4±0.4
0.1±0.1
Fasting
Glucose (mmol/l)
Insulin (pmol/l)
Adiponectin (ng/ml)a
Leptin (ng/ml)
HOMA
HgBA1 (%)
5.47±0.14
126±13
10.1±1.5
30.6±3.2
5.4±1.9
5.8±0.1
5.35±0.11 0.12±0.09
127±17
1±9
12.2±1.4
2.1±0.8
29.3±3.1
1.4±2.1
5.4±1.4
0.03±0.78
5.7±0.1
0.06±0.04
0.2282
0.6996
0.0262
0.5339
0.5027
0.1309
5.56±0.18
131±16
9.5±1.6
25.7±3.0
4.1±0.5
5.7±0.1
OGTT
AUCGlucose-120
AUCInsulin-120
ISIMatsuda
35.5±1.8
3312±521
2.28±0.31
35.0±1.5
3250±486
2.35±0.30
0.5±1.0
60±270
0.07±0.06
0.6013
0.8262
0.6589
FSIVGTT
SIa
AIRglucosea
b-cell functiona
DIa
5.5±2.9
723±230
327±115
1434±366
11.5±9.7
590±176
335±90
977±168
5.6±6.6
113±74
8±48
387±246
CVD risk factors
Triglyceride (mmol/l)
Cholesterol (mmol/l)
LDL-C (mmol/l)
HDL-C (mmol/l)
Apo B (g/l)
hs-CRP (mg/l)
IL-1b (pg/ml)
IL-6 (pg/ml)
TNF-a (pg/ml)
1.00±0.08 1.04±0.09
5.28±0.32 4.85±0.28
3.76±0.27 3.38±0.22
1.07±0.06 1.00±0.05
0.86±0.08 0.79±0.07
8.5±2.5
7.3±1.6
220.1±73.9 150.0±34.8
80.9±23.7 60.8±16.2
332.2±89.6 254.0±60.1
Androgens
Testosterone (nmol/l)
2.9±0.3
2.6±0.2
SHBG (nmol/l)
37.8±5.2
45.0±4.4
FAI
2.6±0.4
1.8±0.3
DHEAS (mmol/l)
139.4±18.0 127.2±16.9
0.3629
0.6098
0.9862
0.3896
0.9697
0.3619
5.57±0.14
0.02±0.23
167±26
36±19
11.3±1.8
1.8±0.7
30.3±3.7
4.7±2.1
4.2±0.6
0.10±0.46
5.5±0.1
0.20±0.05
0.6526
0.1060
0.0241
0.0416
0.8013
0.0006
0.2829
0.1466
0.4327
0.0865
0.4880
0.0470
31.4±1.7
2556±351
3.22±0.57
32.0±1.5
0.6±1.3
3227±519
671±257
3.15±0.56 0.07±0.03
0.6478
0.0182
0.7074
0.8033
0.0628
0.5799
0.3818
0.2463
0.8111
0.1787
4.1±0.5
585±118
234±29
1744±377
4.2±0.6
659±111
252±43
2259±884
0.10±0.46
65±87
18±37
514±822
0.3725
0.3507
0.8977
0.9337
0.4757
0.1803
0.8355
0.3203
0.05±0.07
0.42±0.26
0.38±0.21
0.07±0.05
0.08±0.06
1.1±1.9
53.9±37.3
15.4±12.4
67.1±46.9
0.4128
0.1309
0.0917
0.1639
0.2399
0.8574
0.7640
0.1502
0.1206
1.36±0.13
1.39±0.17
4.66±0.17
4.40±0.18
2.99±0.13
2.75±0.13
1.05±0.03
1.01±0.04
0.72±0.04
0.64±0.05
5.2±1.2
4.7±1.2
127.0±33.1
97.7±25.0
49.2±12.9
46.7±13.5
192.9±50.6 167.9±47.0
0.03±0.09
0.27±0.13
0.24±0.11
0.04±0.04
0.08±0.03
0.6±0.5
29.3±15.5
2.5±5.9
25.0±18.9
0.6461
0.0597
0.0500
0.2418
0.0236
0.1496
0.0961
0.3585
0.1750
0.4484
0.8042
0.3801
0.6110
0.4005
0.1419
0.1146
0.8893
0.8535
0.3±0.3
6.1±4.7
0.7±0.3
1.3±10.2
0.4213
0.0596
0.0470
0.4029
3.1±0.3
3.2±0.3
38.3±4.1
43.1±4.3
2.7±0.4
2.4±0.3
164.3±20.5 164.7±20.1
0.1±0.2
4.8±1.6
0.3±0.2
6.2±9.4
0.6485
0.0038
0.4485
0.5667
0.1621
0.3842
0.0602
0.3474
Abbreviations: BMI, body mass index; CVD, cardiovascular disease; DHEAS, dehydroepiandrosterone sulfate; DI, disposition index; FAI, free androgen index;
FSIVGTT, frequently sampled intravenous glucose tolerance test; HDL-C, high-density lipoprotein cholesterol; HOMA, homeostatic model assessment; hs-CRP, highsensitivity C-reactive protein; IL, interleukin; LDL-C, low-density lipoprotein cholesterol; OGTT, oral glucose tolerance tests; SHBG, sex hormone-binding globulin;
SI, sensitivity index; TNF, tumor necrosis factor.
Values are mean±s.e.m.
P-almonds and P-walnuts: significance for the change within a group as determined by paired t-test.
P almonds vs walnuts: significance comparing the changes between the almond and the walnut groups after adjusting for the baseline differences.
a
Data are log-transformed before statistical analysis.
Significant values are in bold.
In contrast, walnuts increased n-3 PUFA and n-6 PUFA
intakes by 11-fold and 5-fold, without affecting saturated fat
or MUFA intakes.
Composition of the plasma phosholipids also changed.
Almonds tended to increase and walnuts tended to decrease
the MUFA oleic acid. Walnuts increased LA and ALA—the
essential PUFA that cannot be produced in vivo. However, LA
can be converted to the longer and more unsaturated
European Journal of Clinical Nutrition
product AA (20:4) and ALA can be converted to EPA (20:5)
and DHA (22:6). Despite walnuts providing large amounts of
LA and ALA, the long chain product of LA (AA) decreased,
whereas the long chain products of ALA (EPA and DHA)
did not change. In humans, conversion of the essential
PUFA (LA and ALA) to the long chain PUFA (AA, EPA
and DHA ) is inefficient (Arterburn et al., 2006) because LA
and ALA compete with each other on the rate limiting step
Dietary fatty acids and PCOS
S Kalgaonkar et al
391
Figure 1 Plasma glucose concentrations during OGTT before
(open symbols) and after (solid symbols) consumption of almonds
(broken lines) or walnuts (solid lines) (mean±s.e.m.).
Figure 2 Plasma insulin concentrations during OGTT before
(open symbols) and after (solid symbols) consumption of almonds
(broken lines) or walnuts (solid lines) (mean±s.e.m.).
(Marshall and Johnston, 1982). Ratio ALA to LA (1:4) is much
higher in walnuts than in the Western diet (1:20). Thus,
relative abundance of ALA may have interfered with
conversion of LA into AA. Similarly, the high LA content
may have prevented conversion of ALA to EPA and DHA.
A major focus was the changes in glucose homeostasis.
Blood glucose levels are regulated by the balance between
glucose utilization in the tissues and insulin secretion from
the pancreas. When the tissues cannot use glucose efficiently
(insulin resistance), insulin secretion increases (hyperinsulinemia). Insulin resistance is the major underlying pathology in
PCOS, metabolic syndrome and type 2 diabetes. Although
experimental data have shown that n-3 PUFA increase insulin
sensitivity and glucose utilization (Kopecky et al., 2009;
Martin de Santa Olalla et al., 2009), and studies in human
muscle have demonstrated that MUFA and PUFA in the cell
membrane correlate directly with glucose uptake (Baur et al.,
1998), insulin resistance did not decrease in our study. This
may be due to the specific mechanism by which n-3 PUFA
overcomes insulin resistance. Glucose uptake requires binding
of insulin to its receptor in the cell membrane, followed
by serial phosphorylation steps that occur in the insulin
receptor and its substrate. Phosphorylation of tyrosine
residues in the cascade advances, and serine phosphorylation
reduces insulin signaling. The n-3 PUFA overcomes insulin
resistance by preventing serine phosphorylation (Ma et al.,
2009). In PCOS, there is intrinsic increase in serine phosphorylation (Dunaif et al., 1995). It is conceivable that n-3
PUFA cannot reverse this intrinsic abnormality, and therefore
cannot correct insulin resistance. To test this, future research
will need to compare the effects of n-3 PUFA in PCOS vs
healthy control women.
We demonstrated that walnuts increased insulin response
during OGTT. Walnuts increased fasting insulin in type 2
diabetic patients as well (Ma et al., 2010). This may be
explained by two different mechanisms. First, walnuts could
have worsened insulin resistance, thus caused compensatory
increase in insulin. However, there was no increase in insulin
resistance in our study. Alternatively, walnuts could have
stimulated insulin secretion from pancreas directly. Animal
and cell culture studies have shown that the major PUFA
in walnuts (LA) can stimulate insulin secretion directly
(Pareja et al., 1997).
Increased insulin secretion without any increase in insulin
resistance should lower blood glucose. Consistent with this,
walnuts decreased HgBA1c. In fact, this was the only
significant difference between the almond and walnut
groups. The change in HgBA1c occurred within 6 weeks.
Although 50% of the change in HgBA1c can be attributed to
changes in blood glucose during the preceding month, full
change occurs in 3 months. Therefore, a longer study might
have shown a larger decrease.
Previously we have shown that walnuts increased plasma
glucose without changing insulin in PCOS (Kasim-Karakas
et al., 2004). However, weight has decreased in this study.
Similar findings were observed in type 2 diabetic patients
when body weight decreased (Tapsell et al., 2009). Taken
altogether, these results support that changes in weight
modify insulin response to PUFA.
Both almonds and walnuts increased adiponectin. Experimental data have demonstrated that serum adiponectin
correlated directly with oleic acid (MUFA in almonds) and LA
(major PUFA in walnuts) in the adipocyte membrane (Perez
de Heredia et al., 2009), as well as ALA (Sekine et al., 2008).
Although adiponectin correlates with insulin sensitivity
(Stefan and Stumvoll, 2002; Spranger et al., 2004), insulin
sensitivity did not increase in our study. It is possible that
the intrinsic defect in insulin signaling in PCOS may be
unresponsive to the favorable effects of adiponectin.
Walnuts also increased SHBG, which is secreted by the
liver and correlates with insulin sensitivity (Stefan and
European Journal of Clinical Nutrition
Dietary fatty acids and PCOS
S Kalgaonkar et al
392
Stumvoll, 2002; Bonnet et al., 2009). To our knowledge,
effects of PUFA on SHBG have not been previously
reported.
Walnuts increased leptin by 18%. The literature indicates
that n-6 PUFA do not alter leptin whereas n-3 PUFA exert
variable results (Takahashi and Ide, 2000; Reseland et al.,
2001; Korotkova et al., 2002; Peyron-Caso et al., 2002).
Insulin stimulates leptin production and secretion (Havel,
2002). In our study, walnuts may have increased leptin by
increasing insulin.
Another area of interest was the cardiovascular risk factors.
Plasma triglyceride did not change with either treatment.
This is consistent with the literature reporting that MUFA or
plant based essential n-3 PUFA did not decrease plasma
triglyceride—unlike the long chain n-3 PUFA from fish
(Finnegan et al., 2003; Ma et al., 2010). Both almonds
and walnuts decreased cholesterol, as previously reported
(Almario et al., 2001; Feldman, 2002; Griel and
Kris-Etherton, 2006; Banel and Hu, 2009). These changes
were statistically significant only with walnuts, possibly due
to a smaller population size in the almond group.
Both MUFA and n-3 PUFA have anti-inflammatory effects,
whereas n-6 PUFA are considered pro-inflammatory (Calder,
2001). Therefore, we anticipated almonds to decrease
inflammatory markers, whereas the effects of walnuts could
not be predicted due to the contrasting effects of n-3 vs n-6
PUFA. We found that although both almonds and walnuts
tended to reduce these markers (high-sensitivity C-reactive
protein, tumor necrosis factor-a, IL-1b and IL-6), the changes
were not significant, possibly due to the smaller population
size in the almond group.
We also investigated changes in androgens. Testosterone is
the major ovarian androgen and its bioavailability depends
upon the abundance of SHBG. Insulin resistance lowers
SHBG, decreases testosterone binding and consequently
increases free testosterone—the fraction which causes the
undesirable clinical effects such as excess hair growth and
acne (Hamilton-Fairley et al., 1995). Walnuts increased SHBG
by 12.5%, without affecting insulin sensitivity, suggesting
that the rise in SHBG may have been a direct effect. Almonds
decreased free androgen index through a dual mechanism, both by increasing SHBG by 16% and decreasing
testosterone. Thus both nuts exerted favorable effects on
circulating androgens.
In summary, we compared metabolic and endocrine effects
of nuts with different fatty acid compositions in high-risk
women. The limitations of the study were the lack of a
non-treatment PCOS arm or a non-PCOS control group, and
relatively small population size. Despite these limitations,
the results support inclusion of nuts in the PCOS diet
because of their beneficial effects on lipids, androgens and
possibly inflammatory markers. Our findings also stress
the importance of monitoring changes in glucose homeostasis during nut intake. Future research can focus on the
metabolic/endocrine effects of individual fatty acids in other
insulin resistant states such as metabolic syndrome.
European Journal of Clinical Nutrition
Conflict of interest
The authors declare no conflict of interest.
Acknowledgements
This study was supported by grants from the National
Center for Complementary and Alternative Medicine (R21
AT002280) and the ALSAM Foundation to Dr SE Karakas.
The clinical studies were partially supported by the UC Davis
Clinical and Translational Science Center grant (RR 024146).
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