Associations of Circulating PYY 3-36 Concentrations with Metabolic Syndrome in Extremely Obese Subjects

Abstract

Background:

The gut hormone peptide YY_3-36 (PYY_3-36) plays major roles in regulation of appetite and energy metabolism, mediates beneficial effects of bariatric surgery, and may be a potential weight-reducing and glucose-modulating therapy. Obesity may influence the metabolic expression of circulating PYY_3-36 and metabolic markers. We studied the relationship of PYY_3-36 concentrations with metabolic syndrome (MetSyn) components, lipids, insulin resistance, and inflammatory biomarkers in subjects with extreme obesity.

Methods:

We measured MetSyn components and PYY_3-36, lipids, hormones, homeostasis model assessment (HOMA) index, and inflammatory biomarkers in consecutively referred patients (180 women and 111 men) aged 18–78 years with body mass index (BMI) ≥40 kg/m². Associations of PYY_3-36 to components, insulin resistance, and biomarkers were examined with partial correlations and linear regression.

Results:

PYY_3-36 concentrations were not related to MetSyn components, HOMA index, or to inflammatory biomarker or leptin concentrations. PYY_3-36 concentrations correlated with systolic blood pressure (r = 0.21; P < 0.0001) after adjustment for age and gender. In linear regression analysis, PYY_3-36 concentrations were associated with systolic blood pressure after adjustment for age, gender, and central obesity in the entire sample (Beta 0.21; 95% CI 0.09–0.34) as well as in subjects not taking blood pressure-lowering medication (Beta 0.19; 95% CI 0.04–0.36). These associations were not statistically significant in the small subset of participants (22%) with type 2 diabetes.

Conclusions:

In extremely obese patients, fasting PYY_3-36 concentrations were linked to systolic blood pressure, but not to other components of MetSyn, suggesting divergence between pathways of blood pressure and glucose/body weight regulation. However, this finding will need to be further investigated.

Background

Metabolic syndrome (MetSyn), a constellation of abdominal obesity, hypertension, dyslipidemia, and dysglycemia, contributes to cardiovascular diseases (CVD) and type 2 diabetes directly and by creating a proinflammatory milieu.¹ Paralleling the obesity epidemic, MetSyn affects one-third of the United States and other populations,² yet in developed countries, CVD incidence continues to decrease, due, in part, to improvements in other risk factors.³ Persons with very high body mass index (BMI ≥40 kg/m²), a condition here referred to as extreme obesity, bear a large burden of morbidity and mortality. Extreme obesity has increased relatively more markedly in the population than lesser forms of obesity.⁴ The musculoskeletal, health, and psychosocial consequences of extreme obesity are evident. However, cardiometabolic aberrations do not always correlate with the degree of obesity with some extremely obese individuals exhibiting no signs of lipid disturbance or insulin resistance.⁵ In one study, MetSyn patients with extreme obesity had a lower waist–hip ratio and free fatty acid and triglyceride concentrations than patients with MetSyn who were not morbidly obese.⁶ On the other hand, another study found that fat distribution predicted lipid abnormalities in women even with BMI ≥40 kg/m² and dysregulated glucose is very common in this group.^7,8

Gastrointestinal hormones play important roles in the control of satiation, appetite, and energy expenditure and may exhibit impaired effects in obesity.⁹ Among these hormones, peptide tyrosine-tyrosine (PYY_1-36) is released by endocrine L-cells in the small bowel and colon and cleaved by the enzyme dipeptidyl peptidase-4 to the major circulating form that is more bioactive, PYY_3-36.¹⁰ Following a meal, circulating concentrations of PYY_3-36 start to rise within ∼15 min and remain elevated for several hours.¹¹ PYY_3-36 inhibits food intake directly due to its high affinity for the presynaptic inhibitory Y2R neurons in the hypothalamic arcuate center and Y2R in afferent vagal fibers.^10,11 Baseline concentrations are influenced by adiposity, age, gender, and lifestyle, while postprandial concentrations are stimulated by caloric load and macronutrient composition, as well as acute exercise.^10
–12 PYY_3-36 responses are blunted in obesity and enhanced following bariatric surgery, in part, explaining the weight and, possibly, glucose reducing effects of surgery.¹⁰

Bariatric surgery remains currently the only effective treatment option for patients with extreme obesity, yet not all qualify or wish for surgery. New treatments are needed to bridge the gap between lifestyle changes, which may not be effective and no treatment, which puts patients at high risk of CVD and type 2 diabetes. Gut hormones represent potential therapeutic agents against obesity, as exemplified by the approval of high-dose liraglutide as an antiobesity agent. Studies demonstrated that administration of PYY_3-36 led to a dose-dependent reduction in food intake indicating that PYY_3-36 would be a potential antiobesity therapy.^13,14 However, we are aware of one cross-sectional study that found adverse associations between high PYY_3-36 concentrations and metabolic risk factors in patients with coronary artery disease.¹⁵

Understanding the pathophysiology of PYY_3-36 in patients with extreme obesity, its relationship to MetSyn, and influences of PYY_3-36 on risk factors for CVD and type 2 diabetes is imperative. In this study, we studied cross-sectional associations of PYY_3-36 and components of MetSyn, lipids, insulin resistance, and inflammatory biomarkers in patients with BMI ≥40 kg/m², herein defined as extreme obesity.

Materials and Methods

A total of 291 consecutive patients aged 18–78 years with BMI ≥40 kg/m² (180 women and 111 men) referred to the Preventive Cardiology Clinic at Oslo University Hospital, Oslo, Norway, participated in the study between April 2005 and December 2010. The participation rate was over 95%. The study conformed to the Helsinki Declaration and was evaluated by the Regional Ethics Committee.

After written informed consent, participants completed a health questionnaire and underwent anthropometric measurements. A constant tension body tape measure was used to determine waist and hip circumferences. Waist circumference was measured at midpoint between the inferior costal margin and the highest point of the iliac crest, and hip circumference was measured at the widest point around the hips. Height was measured using a stadiometer and recorded to the nearest centimeter. Patients were weighed to the nearest 1.0 kg using a calibrated mobile electronic scale (Seca 720; Medical Scales and Measuring Systems). BMI was calculated in accordance with the Quetelet's formula: Body weight in kilograms divided by the square of body height in meters (kg/m²).

The participants' blood pressure was measured with an automatic blood pressure monitor (52000 Series Vital Signs Monitor; Welch Ally). Measurements were carried out with participants seated and having rested for 5 min prior using an appropriate cuff size. The average of the two lowest measurements was recorded.

All subjects were stratified by number of MetSyn components (from 1 to 5) as follows: elevated waist circumference ≥102 cm for men and ≥88 cm for women, systolic blood pressure ≥130 mmHg and/or diastolic ≥85 mmHg, or/and use of medication for hypertension, triglycerides ≥1.7 mM, high-density lipoprotein cholesterol (HDL-C) ≤1.0 mM for men or ≤ 1.3 mM for women, and fasting glucose ≥5.6 mM. Subjects with fasting glucose ≥7.0 mM or HbA1c ≥6.5%, who had known type 2 diabetes, or used antidiabetic drugs were grouped together as the type 2 diabetes subgroup (22% of the sample). Of the entire sample, use of blood pressure-lowering medication was reported among 31.3%; among those without diabetes, 26.1% used medication versus 49.2% of those with diabetes. Statin use was recorded in 8.9% of the entire sample; among those without diabetes in 5.3% and of those with diabetes in 21.5%.

Laboratory analyses

Participants fasted overnight for at least 10 hr, before providing blood samples between 8:00 and 11:00 a.m. the following day. Following immediate centrifugation, serum samples were stored at −80°C. Analyses were performed at Oslo University Hospital (Clinical Chemistry Laboratory at Ullevål and Endocrine Laboratory at Aker). Total cholesterol, HDL-C, triglyceride, glucose, and CRP concentrations were measured on the automated analyzer Cobas Integra 800 (Roche Diagnostics). LDL-cholesterol was calculated using Friedewald's formula. Apolipoprotein B was determined with an immunoturbidimetric assay on an automated analyzer (Cobas Tinaquant 917, Roche/Hitachi; Roche Diagnostics). White blood cells were analyzed using Sysmex XE 2100 (Sysmex). Serum ferritin was determined by an ADIVA Centaur analysis (ADIVA Centaur; Siemens Healthcare Diagnostics, Inc.).

PYY_3-36 and insulin assays were carried out between November 2010 and May 2011 on frozen samples. Serum PYY_3-36 was measured using a radioimmunoassay kit, which utilizes an antibody that only recognizes the _3–36 form of human PYY. The intra-assay and interassay variations of coefficients were less than 15%, and the recovery was 85%–129% by the linear range of the assay. The detection limit of the assay was 14 pg/mL (100 μg sample size). The assay had a specificity of 100% for human serum PYY_3-36. Insulin was determined by noncompetitive immunofluorometric assay, using an AutoDelfia 1235 Automatic Immunoassay System (H1855-21291) (Perkin Elmer, Inc.). The homeostasis model assessment of insulin resistance (HOMA-IR) index was calculated to estimate insulin resistance.¹⁶

Serum leptin was measured using a human serum leptin radioimmunoassay (Luminex). The intra-assay and interassay variations of coefficients were <10%, and the recovery was 103%–105% by the linear range of assay. The detection limit of the assay was 0.5 ng/L (100 μL sample size). The assay had a specificity of 100% for human serum leptin.

Statistical analyses

Statistical analyses were performed using SPSS 21 (SPSS, Inc.). Skewed variables, including PYY_3-36, triglycerides, glucose, C-reactive protein, ferritin, white blood cells, alanine transaminase, insulin, leptin, and homeostasis model assessment (HOMA) index, were log-transformed before analysis. Categorical and continuous data are presented as counts and percentages, or mean ± standard deviation (SD), respectively. One-way analysis of variance (ANOVA) or independent t-tests were performed to compare components of MetSyn and other risk factors across groups, as appropriate. Partial correlation coefficients (corrected for age and gender) were used to analyze relationships between PYY_3-36 and risk factors. Linear regression analysis was used to establish independent relationships between PYY_3-36 concentrations and risk factors that showed statistically significant partial correlations. Adjustment was made for age, gender, and waist circumference. Two-sided P-values of <0.05 were considered statistically significant.

Results

Subject characteristics are presented in Table 1. Of the total of 291 subjects (180 women and 111 men), 23% were cigarette smokers. Smoking was not related to PYY_3-36 concentrations; additionally, PYY_3-36 concentrations did not differ according to use of statins (data not shown). Weight loss was attempted one or more times among 61% of those with one to two MetSyn components, 59% of those with three components, 47% of those with four components, 63% of those with five components, and 55% of those with type 2 diabetes (P = 0.4).

Table 1.

BMI, Metabolic Syndrome Components, Lipids, Inflammatory Biomarkers, PYY _3–36, Hormones, and Homeostatic Assessment Index of Insulin Resistance Stratified by Number of Metabolic Syndrome Components and Diabetes (N = 291) ^a

	Number of components					Presence of type 2 diabetes
Parameter	One or two (n = 75)	Three (n = 75)	Four (n = 57)	Five (n = 19)	P	No diabetes (n = 226)	Diabetes (n = 65)	P
Age, years	37.4 (12.2)	40.1 (11.3)	40.6 (10.4)	46.7 (8.7)	<0.0001	39.9 (11.4)	41.1 (9.3)	0.007
Female, n (%)	52 (69.3)	45 (60.0)	37 (64.9)	6 (31.6)	0.010	140 (61.9)	40 (61.5)
BMI, kg/m²	45.1 (4.2)	45.5 (4.0)	45.4 (4.7)	44.9 (4.8)	1.0	45.3 (4.3)	46.6 (5.1)	0.035
Metabolic syndrome components
Waist circumference, cm	126.5 (14.6)	131.2 (13.1)	130.6 (13.8)	134.7 (13.8)	0.003	129.8 (13.9)	135.1 (15.6)	0.009
Systolic BP, mmHg	126.8 (15.5)	139.3 (20.0)	137.0 (14.3)	138.2 (9.1)	<0.0001	134.4 (17.2)	136.6 (13.8)	0.4
Diastolic BP, mmHg	82.5 (10.3)	88.0 (11.6)	87.8 (10.4)	87.5 (6.6)	<0.0001	86.1 (10.8)	87.7 (8.8)	0.3
HDL-C, mM	1.4 (0.3)	1.3 (0.3)	1.1 (0.2)	0.9 (0.1)	<0.0001	1.2 (0.3)	1.1 (0.3)	<0.0001
Triglycerides, mM	1.2 (0.4)	1.6 (0.8)	2.3 (0.9)	3.2 (1.5)	<0.0001	1.8 (1.0)	2.1 (1.2)	0.004
Glucose, mM	5.0 (0.4)	5.6 (0.7)	5.6 (0.6)	6.0 (0.4)	<0.0001	5.4 (0.6)	8.7 (3.2)	<0.0001
Other lipids
LDL-C, mM	3.2 (0.9)	3.3 (1.0)	3.5 (0.9)	3.2 (1.5)	0.4	3.3 (0.9)	3.0 (1.1)	0.043
Apolipoprotein B, g/L	0.9 (0.2)	1.0 (0.2)	1.1 (0.2)	1.1 (0.2)	<0.0001	1.0 (0.2)	1.0 (0.3)	1.0
Inflammatory biomarkers
C-reactive protein, mg/L	12.4 (21.2)	10.4 (9.9)	10.9 (17.7)	5.9 (3.0)	0.6	10.8 (16.2)	10.4 (8.8)	0.3
Ferritin, μg/L	117.4 (136.7)	135.5 (125.4)	147.6 (135.8)	188.7 (129.1)	0.2	137.1 (132.7)	121.1 (104.2)	0.6
White blood cells, × 10⁹/L	7.1 (1.9)	8.0 (6.5)	7.4 (1.8)	7.1 (2.0)	0.7	7.5 (4.1)	7.6 (1.8)	0.2
Hormones
Fasting PYY_3–36, pg/mL	95.8 (65.6)	120.5 (136.4)	129.0 (147.8)	120.2 (107.4)	0.4	114.4 (118.7)	106.8 (71.7)	0.9
Insulin, pM	90.5 (67.9)	126.4 (67.0)	127.8 (55.4)	163.9 (64.7)	<0.0001	117.1 (67.5)	134.7 (86.6)	0.2
Leptin, ng/mL	60.7 (29.0)	63.4 (37.7)	53.7 (31.8)	51.6 (27.6)	0.1	59.1 (32.8)	52.3 (34.7)	0.052
HOMA-IR index	2.9 (2.3)	4.5 (2.6)	4.6 (2.2)	6.2 (2.3)	<0.0001	4.1 (2.5)	7.2 (4.5)	<0.0001

Mean (SD) shown. For insulin 4 missing, for LDL-C 11 missing, for apolipoprotein B and ferritin 1 missing, for white blood cells 3 missing.

BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment of insulin resistance; SD, standard deviation.

The range of BMI was between 40 and 74 kg/m². In subjects without type 2 diabetes, BMI did not differ according to the number of MetSyn components, although age and the proportion of males increased with increasing number of components. Each of the MetSyn component levels worsened with increasing number of MetSyn components, as did apolipoprotein B, insulin concentrations, and HOMA-IR index. Participants with type 2 diabetes were slightly older than their counterparts without type 2 diabetes, and had higher BMI, waist circumference, triglyceride and glucose concentrations and HOMA-IR, and lower HDL-C and low-density lipoprotein cholesterol (LDL-C) concentrations. PYY_3-36 concentrations did not differ according to MetSyn components or the presence of type 2 diabetes.

Correlational analyses shown in Table 2 found statistically significant relationships between PYY_3-36 concentrations and systolic blood pressure in the entire sample and in those not taking blood pressure-lowering medication. This finding was not statistically significant in the subgroup with type 2 diabetes. No other statistically significant correlations were observed between PYY_3-36 concentrations and components of MetSyn, lipids, and leptin, or insulin concentrations or HOMA-IR index.

Table 2.

Partial Correlations Between PYY _3–36 Concentrations and BMI, Number of Metabolic Syndrome Components, Components of Metabolic Syndrome, Lipids, Inflammatory Biomarkers, Hormones, and HOMA-IR Index, Adjusted for Age and Gender ^a

	All (N = 291)		No type 2 diabetes (n = 226)		With type 2 diabetes (n = 65)
	Coefficient	P	Coefficient	P	Coefficient	P
BMI	0.070	0.2	0.098	0.1	0.007	1.0
# Of MetSyn components	0.031	0.6	0.051	0.4	-
Components of MetSyn
Waist circumference	0.093	0.1	0.107	0.1	0.059	0.6
Systolic BP (all)	0.205	<0.0001	0.200	0.003	0.205	0.1
Diastolic BP (all)	0.062	0.3	0.039	0.6	0.142	0.3
Systolic BP (no medication)	0.178	0.012	0.182	0.020	0.153	0.4
Diastolic BP (no medication)	0.056	0.4	0.051	0.5	0.135	0.5
HDL-C	−0.014	0.8	0.003	1.0	−0.069	0.6
Triglycerides	0.052	0.4	0.051	0.4	0.047	0.7
Fasting glucose	0.088	0.1	0.082	0.2	0.226	0.1
Other Lipids
LDL-C	−0.010	0.9	−0.007	0.9	−0.031	0.8
Apolipoprotein B	−0.013	0.8	−0.009	0.9	−0.032	0.8
Inflammatory biomarkers
C-reactive protein	0.042	0.5	0.060	0.9	−0.029	0.8
Ferritin	0.036	0.5	0.022	0.7	0.076	0.6
White blood cells	−0.011	0.8	0.001	1.0	−0.082	0.5
Hormones
Insulin	0.039	0.5	0.098	0.1	−0.165	0.2
Leptin	−0.014	0.8	0.010	0.9	−0.085	0.5
HOMA-IR index	0.068	0.3	0.105	0.1	−0.055	0.7

For insulin 4 missing, for LDL-C 11 missing, for apolipoprotein B and ferritin 1 missing, for white blood cells 3 missing.

MetSyn, metabolic syndrome.

In multiple linear regression analysis, we found significant associations between concentrations of PYY_3-36 and systolic blood pressure in the entire sample, and in the subgroup without type 2 diabetes after adjustment for age, gender, and central obesity (Table 3). The associations between PYY_3-36 and systolic blood pressure were also statistically significant in the subgroup of patients not using antihypertensive medication.

Table 3.

Adjusted Associations of PYY _3–36 to Systolic Blood Pressure in Participants With and Without Type 2 Diabetes According to Use of Blood Pressure-Lowering Medication ^a

	Systolic BP	Systolic BP	Systolic BP
All	All (N = 291)	No type 2 diabetes (n = 226)	With type 2 diabetes (n = 65)
B (unstandardized coefficient)	0.004	0.004	0.005
95% CI for B	0.002–0.007	0.001–0.007	−0.001–0.010
Beta (standardized coefficient)	0.211	0.207	0.205
95% CI for Beta	0.087–0.335	0.059–0.343	−0.054–0.524
P	0.001	0.006	0.1
No BP-lowering medication	n = 200	n = 167	n = 33
B	0.004	0.004	0.005
95% CI for B	0.001–0.007	0.001–0.007	−0.007–0.017
Beta	0.192	0.193	0.161
95% CI for Beta	0.036–0.356	0.018–0.360	−0.364–0.862
P	0.017	0.030	0.4

Adjusted for age, gender, and waist circumference.

BP, blood pressure.

Discussion

Our main findings were that fasting, unstimulated PYY_3-36 concentrations did not relate to number of MetSyn components or to single components of MetSyn with the following exceptions: PYY_3-36 concentrations were linked to systolic blood pressure in the entire sample and in participants not taking blood pressure-lowering medication. Furthermore, PYY_3-36 concentrations were not related to inflammatory biomarkers or lipids (LDL-C, apolipoprotein B), leptin concentrations, or to HOMA-IR index in extremely obese subjects.

Genetic data indicate that common variation at the PYY locus influences not only PYY concentrations but also multiple hereditable MetSyn traits, including BMI and lipids.¹⁷ However, this study did not account for the influence of PYY variation on PYY_3-36 or PYY_1-36 concentrations and their potentially divergent associations with Metsyn.^10,17 The two native forms of PYY, PYY_1-36 and its metabolite PYY_3-36, differ in their actions on appetite and glucose homeostasis, due to different binding affinities on the YR receptors.¹⁰ PYY_3-36, but not PYY_1-36, improves glucose tolerance, although these effects are difficult to distinguish from the effects of PYY_3-36 on feeding.^10,18 Recently, the importance of measuring the biologically relevant form of PYY, namely PYY_3-36, as done in this study, was emphasized.¹⁰

In individuals with extreme obesity, our finding of no relationship between PYY_3-36 concentrations and MetSyn components, insulin resistance, or presence of type 2 diabetes is consistent with some studies and differs from others. A study of patients with coronary artery disease, who were not selected for obesity, found that high fasting PYY_3-36 concentrations were independently and positively associated with type 2 diabetes and obesity-associated insulin resistance.¹⁵ Furthermore, in this study, PYY_3-36 concentrations were associated with adiposity,¹⁵ in contrast to a number of other studies showing that low PYY_3-36 concentrations are associated with adiposity.^10,19 Notably, first-degree relatives of subjects with type 2 diabetes exhibited lower fasting PYY concentrations than matched controls, but in this study, only total PYY was measured.²⁰ Ukkola et al. studying patients with coronary artery disease additionally found an association between high fasting PYY_3-36 concentrations and fasting glucose concentrations, although the association was abolished after adjustment for BMI.¹⁵

The authors speculated that differences in age, other population's characteristics, and different experimental setting may explain these divergent results.¹⁵ Furthermore, disturbed PYY concentrations may be a cause or a consequence of type 2 diabetes and associated obesity in cross-sectional studies. Thus, associations shown may be dependent on the stage of diabetes or obesity. Our findings are consistent with observations in subjects with extreme obesity (mean BMI, 53), examined after a fat load.²¹ Insulin resistance impaired the PYY response to a fat load, but the study did not find significant differences in PYY levels between subjects with different degrees of insulin resistance or with diabetes in these morbidly obese subjects.²¹ Unfortunately, in this study, only total PYY was measured.

While some authors have speculated that a state of partial PYY resistance may be present in patients with coronary artery disease, parallel to insulin and leptin resistance,¹⁵ we found no relationship between circulating PYY_3-36 and leptin or insulin concentrations. Obesity and insulin resistance are characterized by subclinical inflammation, and markers of inflammation associate both with visceral obesity and insulin resistance.¹ In this study, fasting PYY_3-36 concentrations were not related to markers of inflammation, including CRP, ferritin, and white cell count.

The association between PYY_3-36 concentrations and systolic blood pressure appears to be a novel one, and underscores a possible avenue of future research. The relationship was observed both in the entire sample, and after exclusion of subjects taking antihypertensive medication. PYY appears to induce vasoconstriction by Y1R agonism and has been shown to be present in atherosclerotic plaque in animal studies.²² However, the PYY_3-36 metabolite that binds to Y2R neurons has not been previously associated with increased blood pressure to our knowledge, although variants in the Y2R genes are associated with a reduced risk of hypertension.²³ PYY acts as a vasoconstricting agent and PYY infusion cause significant vasoconstricting effects.²⁴ Divergent effects of PYY_1-36 and PYY_3-36 on appetite and glucose homeostasis may or may not be paralleled by divergence in regard to blood pressure. Among patients with coronary artery disease, no relationship was observed between PYY_3-36 and blood pressure.¹⁵ However, subjects with extreme obesity may display a disrupted PYY_3-36 function. Further study will be needed to clarify this finding.

Limitations

The main study limitation is that we did not have postprandial PYY_3-36 concentrations. It also would have been useful to compare the results to those of a lean control group or to individuals with lesser obesity, but these data are not available. Furthermore, novel markers of subclinical inflammation such as cytokine concentrations were not measured. Blood pressure measurement was based on a clinical measurement on a single occasion. A comparison with ambulatory 24-hour blood pressure would have been informative, but we did not have these measurements.

Conclusion

PYY_3-36 appears to be linked to systolic blood pressure in extremely obese individuals, but not associated to other components of MetSyn, insulin resistance, inflammation, or leptin. These findings suggest the need to distinguish pathways whereby PYY_3-36 may influence cardiovascular risk factors.

Footnotes

Author Disclosure Statement

No conflicting financial interests exist

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