Peripheral Lymphocytes,Obesity,and Metabolic Syndrome in Young Adults: An Immunometabolism Study

Abstract

Background:

Obesity is characterized by a low-intensity chronic inflammatory process in which immune system cells interact in a complex network, which affects systemic metabolic processes. This raises interest in analyzing possible changes in the proportions of immune system cells in individuals with obesity with and without metabolic syndrome (MS), in relation to their body composition.

Methods:

Circulating cells were analyzed with flow cytometry in young adults: monocytes, granulocytes, lymphocytes (T, B, and natural killer [NK]), TCD4⁺CD62⁻, TCD8⁺CD28⁻, and naive and memory cells of TCD3⁺ and TCD4⁺. Body composition was obtained by bioelectrical impedance analysis and dual-energy X-ray absorptiometry, and metabolic parameters.

Results:

A total of 169 persons were evaluated: 20% presented normal body mass index (BMI); 49% was overweight, and 31% had obesity; 28% had MS. It was observed that with an increase in BMI and visceral adipose tissue increase (VATI), body composition and biochemical variables were negatively altered. With regard to cell subpopulations, total lymphocytes increased and granulocytes and NK lymphocytes decreased in patients with MS and VATI. Memory cells increased with BMI and VATI. In individuals with MS, monocytes, and NK lymphocytes comprised a negative association with VAT, fat mass, and skeletal muscle mass (SMM). In individuals with MS and VATI, a negative correlation was observed between monocytes and SMM.

Conclusions:

Significant changes were detected in the subpopulations of lymphocytes, suggesting that weight gain, SMM, and VAT accumulation gave rise to immunological changes at the peripheral level, and the presence of increased memory cells could be related to low-intensity chronic inflammation.

Introduction

Obesity contributes to increased morbidity and mortality. An estimated 3.5 million persons die each year from the disease. About 2 billion people worldwide are overweight and 600 million are obese, which has led to the term “globesity.”^1

–5 Obesity is characterized by early inflammation of adipose tissue, which later becomes systemic^1,6

–9 and results in multiple co-morbidities.^1,5,10,11

Adipose tissue is composed of several cell types, including adipocytes, fibroblasts, endothelial cells, and various immune cells.^1,3,10,12 In previous studies, in the hypertrophic and hyperplastic visceral adipose tissue (VAT) of obese patients with metabolic syndrome (MS), a large number of macrophages, CD8 T lymphocytes, natural killer (NK) T cells, and a low content of regulatory T lymphocytes have been observed.^13

–16 It has also been reported that when a high-fat diet persists, the proportion of anti-inflammatory lymphocytes, including CD4 regulatory T cells, is decreased.¹⁷

In contrast, at the level of the peripheral blood, different studies have been reported on the percentages of the lymphocyte subpopulations in relation to obesity; where total lymphocytes, neutrophils,¹⁸ TCD3, TCD8, NK, and B lymphocytes have been observed increased in persons with obesity, compared with individuals with normal weight. These changes could reflect adipose inflammation in these subjects.¹⁹ In addition, circulating leukocytes could represent a key factor in the study of obesity and its associated co-morbidities such as MS.¹⁸

With the purpose of increasing knowledge of the changes in lymphocyte cells related with metabolic inflammatory process, the present investigation focused on comparing variations in lymphocyte populations in peripheral blood, considering the body mass index (BMI) of the individuals, the presence of MS, and body composition.

Materials and Methods

Study design and selection of participants

A study was performed with students of the Metropolitan Autonomous University-Xochimilco (México). Individuals with infections, who were pregnant, who had diabetes mellitus 1 and 2, with autoimmune disease, hepatic, renal, and endocrine cancers, heart disease, and who were taking medication, were excluded from the study. All participants were previously informed about the objectives and procedures of the study and they signed an informed consent form. All of the procedures adhered to were reviewed and approved by the Committee of the Metropolitan Autonomous University-Xochimilco.

Anthropometric and corporal composition measurements

Anthropometric measurements consisted of weight, height, and waist circumference (WC), following the standard protocol of the International Society for the Advancement of Kinanthropometry (ISAK).²⁰ A SECA 213 stadiometer was used for the measurement of height. WC was measured with a SECA 201.

The BMI (kg/m²) was calculated in the participants according to World Health Organization²¹ criteria for adults.

Body composition was evaluated by bioelectrical impedance analysis with InBody720 (Biospace Co., Ltd.) equipment. Individuals with ≥100 cm² of VAT were diagnosed as having visceral obesity. Weight was also measured with the equipment cited previously. The percentage of subcutaneous fat and skeletal muscle mass (SMM) and fat mass (FM) was evaluated using dual-energy X-ray absorptiometry (Hologic Discovery).

Measurement of metabolic parameters

Triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), glucose (Glu), and total cholesterol (Total Chol) were measured after a 12-h fast with an automated IKEM, clinical chemistry analyzer. All peripheral blood samples were collected by venipuncture in Becton Dickinson (BD) vacutainer SST. For plasma preparation, the blood was centrifuged and supernatants were used for analysis. Low-density lipoprotein cholesterol (LDL-c) was calculated with the Friedewald equation. Blood pressure measurement was performed according to the guidelines of the Official Mexican Norm (NOM-030-SSA2-2009) for the Prevention, Treatment, and Control of Hypertension.

The definition of the Cholesterol Education National Program (ATP III), modified for Hispanics, was taken into account for the diagnosis of MS, where the presence of three or more of the following conditions were sufficient to diagnose MS²²: blood pressure (≥130/85 mmHg); fasting glucose (≥100 mg/dL); triglycerides (≥150 mg/dL); HDL-c (<40 mg/dL, for men and women); and WC (≥80 cm for women, ≥90 cm for men). Individuals without MS were those that had ≤2 of the above criteria.

Flow cytometry

A second venous blood sample was collected in tubes containing K2 EDTA (BD) to identify lymphocyte subpopulations. The cells were stained with commercial antibodies conjugated (BD), the combinations employed were the following: control isotype FITC-anti-IgG1/PE-anti-IgG2, FITC-anti-CD45/Pe-anti-CD14, FITC-anti-CD3/PE-anti-CD16⁺CD56/PerCP-anti-CD19, FITC-anti-CD4/PE-anti-CD62/APC-anti-CD3, FITC-anti-CD8/PE-anti-CD28/APC-anti-CD3, and FITC-anti-CD45RA/PE-anti-CD45RO/PerCP-anti-CD4/APC-anti-CD3. Peripheral blood samples were collected in BD Vacutainer Cell Preparation Tubes with conjugated antibodies for 20 min in the dark. Then, 3 mL of lysis buffer solution was added to the samples. The samples were washed with PBS and fixed with 1% p-formaldehyde.^23,24

Cell analysis was performed using a FACSCanto II Cytometer (BD). For each sample 10,000 cells were counted. The region for subpopulation analysis was conducted using a Forward Scatter and an LFL-3 Scatter. Two-fluorescence dual-axes graphs were made using FACS Diva software (BD).

Statistical methods

The Kolmogorov–Smirnov test was employed to explore the data distribution of each variable. Non-normally distributed variables were transformed by natural logarithm before the analysis. The results were depicted as mean and standard deviation (SD) for variables that exhibited normal distribution, the median and interquartile range (IR) was utilized for those who did not demonstrate this. One-way analysis of variance (ANOVA) and the Bonferroni post hoc test were applied to determine differences among >2 groups. Comparison between two groups was performed using the Student's t-test. Correlation coefficients were obtained by performing linear regressions between variables employing the Pearson test. Data were adjusted for gender and age. A P value of <0.05 was considered significant, employing the SPSS statistical software program.

Results

General information

A total of 169 persons were evaluated between September 2015 and January 2017. Participants had a mean age of 26.4 ± 6.8 years, 121 were women (71%), and 49 were men (29%). Thirty four individuals presented with normal BMI (20%), 82 was overweight (49%), and 53 had obesity (31%). According to the relationship between BMI and visceral obesity, it was observed that the presence of visceral adipose tissue increase (VATI) was in the majority of persons with obesity (97%) and overweight (49%), compared with those with normal weight (3%) (data not shown).

All anthropometric, body composition, biochemical variables, and blood pressure measurements were higher for individuals with obesity and VATI, compared with those with normal weight and normal VAT, respectively, with nearly all of these differences being significant. LDL-c exhibited no significant differences when the comparison was made according to the BMI, neither in the glucose or total cholesterol content when comparisons were conducted according to visceral obesity. Finally, once data were adjusted for gender and age, TG, Glu and total cholesterol, none of these presented statistical differences according to BMI, TG, and LDL-c according to visceral obesity (Tables 1 and 2).

Table 1.

Distribution of the Anthropometric Variables, Body Composition, and Biochemical Parameters of the Study Groups According to Body Mass Index

Variable (n = 169)	Normal (n = 34)	Overweight (n = 82)	Obesity (n = 53)	P	p (post hoc)	p^@
WC (cm)	82.8 ± 8.3	92 ± 7.2	106.4 ± 9.3^a,b	0.000	0.000	0.000
ST (%)	31.8 (28.1–34.8)	38.5 (34.5–41.2)^a	43.3 (37.1–44.6)^a,b	0.000	0.000	0.000
FM (kg)	17.9 ± 3.7	25.7 ± 4^a	35.3 ± 6.5^a,b	0.000	0.000	0.000
VAT (cm²)	78.5 (49.7–86.5)	99.3 (85.9–111.2)^a	143.8 (125.4–159.9)^a,b	0.000	0.000	0.000
SMM (kg)	36.3 (29.9–42.4)	37.8 (35.1–42.8)	41.7 (38.8–58.3)^a,b	0.000	0.033	0.000
TG (mg/dL)	103 (81–159.4)	119.9 (91.4–156.7)	152.4 (102–197.5)^b	0.018	0.029	0.093
HDL-c (mg/dL)	49 (42.9–62.2)	39 (30.6–47.9)^a	39 (30.4–48.7)^a	0.001	0.003	0.002
Glu (mg/dL)	84 (75.5–90.9)	79.5 (71.1–90)	87 (76.8–99.7)^b	0.033	0.029	0.126
LDL-c (mg/dL)	85.4 ± 27.3	81.1 ± 24.5	92.9 ± 32.8	0.069		0.397
Total Cho (mg/dL)	161.9 ± 36.7	151.1 ± 30.9	167.8 ± 39.8^b	0.026	0.026	0.640
SBP (mmHg)	110 (100–110)	110 (100–119)	120 (110–120)^a,b	0.000	0.000	0.000
DBP (mmHg)	70 (60–80)	70 (70–80)	80 (70–83)^a,b	0.000	0.017	0.000

Data are presented in media ± SD or median and interquartile range (IR).

p (post hoc), P value adjusted with Bonferroni test.

p^@, P value adjusted by sex and age.

Statistically significant values are highlighted in bold.

Statistically significant difference versus normal (P < 0.05).

Statistically significant difference versus overweight (P < 0.05).

WC, waist circumference; ST, subcutaneous adipose tissue; FM, fat mass; VAT, visceral adipose tissue; SMM, skeletal muscle mass; TG, triglycerides; HDL-c, high-density lipoprotein cholesterol; Glu, glucose; LDL-c, low-density lipoprotein cholesterol; Total Cho, total cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; SD, standard deviation.

Table 2.

Distribution of the Anthropometric Variables, Body Composition, and Biochemical Parameters of the Study Groups According to Visceral Obesity

Variable (n = 168)	Normal VAT (n = 75)	High VAT (n = 93)	P	p^@
WC (cm)	85.6 ± 7.4	102 ± 9.7^a	0.000	0.000
ST (%)	36.4 (31.4–39.1)	40.1 (35.6–43.8)^a	0.003	0.000
FM (kg)	21.9 ± 4.9	31 ± 7^a	0.000	0.000
SMM (kg)	36.5 (32–39.7)	41.3 (37.7–48.8)^a	0.000	0.000
TG (mg/dL)	103.3 (82.3–160.4)	139.7 (100.5–183.2)^a	0.035	0.380
HDL-c (mg/dL)	44.6 (33.3–56.3)	38.3 (30.9–47.1)^a	0.007	0.006
Glu (mg/dL)	81.8 (70.1–90.7)	86 (74.3–92.4)	0.065	0.333
LDL-c (mg/dL)	79.5 ± 24.9	90.6 ± 29.9^a	0.014	0.237
Total Cho (mg/dL)	153.3 ± 33.7	162.9 ± 37.1	0.090	0.678
SBP (mmHg)	110 (100–110)	111 (110–120)^a	0.000	0.000
DBP (mmHg)	70 (60–80)	80 (70–80)^a	0.001	0.010

Data are presented in media ± SD or median and interquartile range (IR).

p^@, P value adjusted by sex and age.

Statistically significant values are highlighted in bold.

Statistically significant difference (P < 0.05).

WC, waist circumference; ST, subcutaneous adipose tissue; FM, fat mass; SMM, skeletal muscle mass; TG, triglycerides; HDL-c, high-density lipoprotein cholesterol; Glu, glucose; LDL-c, low-density cholesterol; Total Cho, total cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure.

Metabolic parameters and MS in association with BMI

The prevalence of MS was found in 28% of individuals in the sample (n = 45). Factors associated with the presence of MS included elevated WC in 87.3% of persons, followed by low HDL-c (47.9%) and high TG (36.2%). The factors that were less altered included glucose (14.1%), systolic blood pressure (8.4%), and diastolic blood pressure (11.4%). It was also observed that the presence of MS increased as BMI and increased VAT, with a prevalence of 53% in persons with obesity and of 71% in those with VATI. However, this was also observed in individuals with normal BMI (9%) and normal VAT (29%) (data not shown).

Behavior among lymphocyte subpopulation, BMI, and visceral obesity

The relationship between lymphocyte subpopulations and BMI demonstrated that total lymphocytes increased as BMI increased, whereas granulocytes and NK lymphocytes revealed an inverse behavior (Table 3). When lymphocyte subpopulations were analyzed according to the visceral obesity, an increase in total lymphocytes was also present. In contrast, granulocytes and NK lymphocytes decreased in persons with VATI when compared with those of individuals with normal VAT; this set of differences was statistically significant (Table 4). When the data were adjusted for gender and age, the differences were maintained (Tables 3 and 4).

Table 3.

Leukocyte Distribution in Relation with Body Mass Index Diagnosis

Variable (%) (n = 160)	Normal (n = 33)	Overweight (n = 75)	Obesity (n = 52)	P	p (post hoc)	p^@
Total lymphocytes	32.1 ± 10	34.3 ± 9.5	41.2 ± 11.5^a,b	0.000	0.001	0.000
Monocytes	8.6 ± 3.6	7.6 ± 2.5	7.6 ± 2.6	0.198		0.095
Granulocytes	61.1 (51.3–68.3)	57.5 (52.5–64)	53.8 (42.2–59.2)^a,b	0.000	0.000	0.000
Lymphocytes T	69.5 (59.7–77.9)	73.3 (67.4–79.3)	74.3 (65.7–79.8)	0.189		0.085
Lymphocytes B	8.4 (5.9–11.5)	8.8 (6.4–12.1)	9.9 (7.1–12.7)	0.145		0.298
Lymphocytes NK	19.5 (13.2–31.8)	15.6 (11.5–20.6)^a	14.9 (11.3–19.7)^a	0.009	0.011	0.000
CD4⁺	49.4 (41.6–55.7)	50.8 (46.3–57.3)	50.3 (40.9–61.7)	0.879		0.620
CD8⁺	33.5 ± 10	35 ± 9.4	32.2 ± 10.8	0.319		0.407
CD4⁺CD62⁻	29.6 ± 8.7	34 ± 10.3	35.1 ± 10.2	0.130		0.137
CD8⁺CD28⁻	37.8 (27.5–55.4)	36.8 (26.2–51.8)	39.1 (26.2–51.8)	0.947		0.917
CD3⁺	69.1 (59.2–75.3)	65.9 (59.2–73.2)	65.4 (61.2–72.5)	0.955		0.760
CD3⁺CD45RA⁺	47.1 ± 12.7	46.6 ± 9.7	43.5 ± 12.9	0.473		0.340
CD3⁺CD45RO⁺	24.7 (19.7–32.7)	28.4 (23.1–38.4)	33.6 (29.4–40.6)^a	0.004	0.003	0.032
CD3⁺CD45RA⁺CD45RO⁺	13.4 (11.4–25)	15.6 (13–18.8)	15.5 (11–19.1)	0.752		0.226
CD4⁺	46 (35.8–62.2)	51.9 (43–60.9)	61.7 (56.6–69.6)^a,b	0.014	0.002	0.025
CD4⁺CD45RA⁺	33.8 ± 12.9	33.9 ± 10.2	33.2 ± 13.9	0.974		0.902
CD4⁺CD45RO⁺	46.3 ± 13.1	41.7 ± 12.6	43.1 ± 12.8	0.461		0.488
CD4⁺CD45RA⁺CD45RO⁺	16.2 (13.5–21.4)	14.8 (11.1–22.3)	13.5 (10.7–19.8)	0.810		0.771

Data are presented in media ± SD or median and interquartile range (IR).

p (post hoc): P value adjusted with Bonferroni test.

p^@, P value adjusted by sex and age.

Statistically significant values are highlighted in bold.

Statistically significant difference versus normal (P < 0.05).

Statistically significant difference versus overweight (P < 0.05).

Table 4.

Lymphocyte Distribution in Relation with Visceral Obesity

Variable (n = 168)	Normal VAT (n = 75)	Hight VAT (n = 93)	P	p^@
Total lymphocytes	33.1 ± 10.3	38.6 ± 10.8^a	0.001	0.003
Monocytes	7.9 ± 2.9	7.6 ± 2.5	0.492	0.316
Granulocytes	60.1 (53.2–67.3)	55.4 (46.9–59.8)^a	0.000	0.000
Lymphocytes T	71.1 (63.8–78.1)	74.5 (67–79.7)	0.265	0.075
Lymphocytes B	8.7 (6.7–11.8)	9.8 (6.5–12.6)	0.640	0.718
Lymphocytes NK	18.8 (13–25.5)	14.9 (11.3–19.7)^a	0.011	0.001
CD4⁺	49.4 (39.9–55.7)	51.2 (45–60.1)	0.084	0.217
CD8⁺	34.3 ± 10.1	33.2 ± 9.8	0.477	0.394
CD4⁺CD62⁻	32.1 ± 8.8	34.7 ± 10.9	0.211	0.497
CD8⁺CD28⁻	37.4 (27–54.2)	37.1 (26.4–50.9)	0.788	0.884
CD3⁺	65.4 (59.5–71.9)	66.2 (59.1–75.6)	0.638	0.833
CD3⁺CD45RA⁺	47.5 ± 10.5	44.6 ± 11.5	0.215	0.503
CD3⁺CD45RO⁺	27.7 (21–32.8)	32.3 (24.9–38.8)^a	0.010	0.048
CD3⁺CD45RA⁺CD45RO⁺	16.1 (12.5–21.6)	15.1 (11.7–17.3)	0.129	0.131
CD4⁺	50.6 (40.9–63.5)	58 (49.2–62.8)^a	0.009	0.025
CD4⁺CD45RA⁺	33.7 ± 11	33.7 ± 12.3	0.978	0.786
CD4⁺CD45RO⁺	42.9 ± 11.8	43 ± 13.5	0.988	0.640
CD4⁺CD45RA⁺CD45RO⁺	16.7 (14.1–22.2)	13.7 (10.6–19.1)	0.094	0.106

Data are presented in media ± SD or median and interquartile range (IR).

p^@, P value adjusted by sex and age.

Statistically significant values are highlighted in bold.

Statistically significant difference (P < 0.05).

However, no statistical differences were found either when the BMI and VAT effect exerted on cells with cytotoxic (CD8⁺CD28⁻) and helper (CD4⁺CD62⁻) functions were analyzed, or when the degree of association of these two variables and the presence/absence of MS were analyzed by the regression model (data not shown).

On the other hand, it was found that individuals with MS and VATI had highest levels of total lymphocytes and lowest monocytes, granulocytes, and NK lymphocytes compared with persons with MS and normal VAT. No differences were found in helper and cytotoxic lymphocytes (Table 5).

Table 5.

Lymphocyte Distribution According to Metabolic Syndrome and Visceral Obesity

	Without MS (n = 112)			MS (n = 45)
Variable (%) (n = 157)	N VAT (n = 56)	I VAT (n = 56)	P	N VAT (n = 13)	I VAT (n = 32)	P
Total lymphocytes	34 ± 10.7	38.5 ± 11.7^a	0.037	29.7 ± 8.7	38.7 ± 9.2^a	0.005
Monocytes	7.7 ± 3	7.7 ± 2.8	0.981	9.2 ± 2	7.5 ± 1.9^a	0.017
Granulocytes	59.9 (53.2–65.8)	55.5 (46.9–60.4)^a	0.001	64.8 (49–68.2)	53.5^a (44.8–59.3)	0.029
Lymphocytes T	71.7 (64.6–78.4)	74.5 (67.4–80)	0.645	69.5 (58.3–75.1)	74.3 (65.8–79)	0.102
Lymph. B	8.7 (6.7–12)	9.4 (6.2–12.6)	0.839	9.5 (7.2–11.9)	10.3 (7.3–12.7)	0.323
Lymph. NK	16.4 (12.5–23.3)	15.3 (11.4–19.6)	0.166	22.7 (15–34.4)	14.6 (10.7–21.5)^a	0.007
CD4⁺	49.1 (38.8–56)	51.8 (44.9–58.9)	0.109	51.5 (43.8–56.3)	49.9 (45.1–62)	0.863
CD8⁺	34.4 ± 10	34.2 ± 10.6	0.898	35.2 ± 11.3	31.5 ± 8.3	0.228
CD4⁺CD62⁻	31.2 ± 9.1	33.7 ± 12.5	0.342	35.1 ± 5.7	36.2 ± 7.4	0.749
CD8⁺CD28⁻	38.2 (25–57.4)	36.3 (23.6–51.9)	0.808	36.7 (32.3–46.3)	41 (28.1–45.8)	0.950
CD3⁺	66.7 (59.6–72.1)	67.4 (59.4–75.7)	0.904	29.7 ± 8.7	38.7 ± 9.2	0.464
CD3⁺CD45RA⁺	44.6 ± 10	44.4 ± 11.5	0.440	49.8 ± 12.9	44.8 ± 11.9	0.320
CD3⁺CD45RO⁺	28.4 (20.3–33.3)	34.4 (26.1–41.1)^a	0.006	24.8 (21.5–29.6)	29.6 (24.9–36.4)	0.079
CD3⁺CD45RA⁺ CD45RO⁺	17.8 (13.2–21.6)	15.3 (12.5–17.1)	0.055	9.5 (7.2–11.9)	10.3 (7.3–12.7)	0.619
CD4⁺	47.2 (41.9–58)	59.2 (49.2–63.4)^a	0.011	55.2 (26.7–67.3)	58 (50.1–63.8)	0.173
CD4⁺CD45RA⁺	34.6 ± 9.7	32.2 ± 12.8	0.428	33.3 ± 14.7	36.1 ± 11.3	0.598
CD4⁺CD45RO⁺	43.6 ± 10.3	45.4 ± 14.5	0.588	38.1 ± 15.9	39 ± 10.9	0.856
CD4⁺CD45RA⁺ CD45RO⁺	16.2 (14–21.4)	13.7 (10.3–21.3)	0.284	35.1 ± 5.7	36.2 ± 7.4	0.463

Data are presented in media ± SD or median and interquartile range (IR).

Statistically significant values are highlighted in bold.

Statistically significant difference (p < 0.05).

MS, metabolic syndrome; N VAT, normal visceral adipose tissue; I VAT, increased visceral adipose tissue; Lymph. B, lymphocytes B; Lymph. NK, natural killer lymphocytes.

Regarding memory cells in relation to BMI, total memory (CD3⁺CD45RO⁺) and CD4⁺ cells were increased in individuals with obesity compared with normal-weight individuals (Table 3). When the lymphocyte subpopulations were analyzed in relation to visceral obesity, total memory and CD4⁺ cells increased significantly in persons with VATI compared with individuals with normal VAT (Table 4).

Association between lymphocyte subpopulation and MS

Regarding the presence of MS in individuals, differences were not observed in the lymphocyte subsets. However, when subpopulations were compared in relation to MS and visceral obesity, it was found that persons without MS and with VATI had highest CD3⁺CD45RO⁺ and CD4⁺ percentages, compared with those without MS and with normal VAT. A trend toward diminution in CD3⁺CD45RA⁺CD45RO⁺ was found in these same patients (Table 5).

Linear regression between metabolic state and corporal composition

Linear regression analysis adjusted for gender and age in individuals with MS revealed a negative association between VAT with monocytes and NK lymphocytes, where the increase of 1 cm² of VAT was associated with a decrease of 7% (range, 3%–11%) of monocytes and 2% (range, 0.5%–3%) of NK lymphocytes (Models 1 and 2, respectively). In relation to subcutaneous FM, a negative correlation was found with monocytes (Model 3) and NK lymphocytes (Model 4), where an increase of 1 kg of subcutaneous FM was associated with a decrease of 2% (range, 0.3%–3.0%) of monocytes and 0.5% (range, 0.1%–1.0%) of NK lymphocytes. Finally, a negative association was found between SMM with monocytes (Model 5) and with NK lymphocytes (Model 6). In this case, an increase of 1 kg of SMM was associated with a decrease of 2% (range, 0.7%–3%) of monocytes and 1% (range, 0.3%–1.0%) of NK lymphocytes (Table 6). With regard to memory cells, no statistically significant association was found with any body variable.

Table 6.

Linear Regression Between Lymphocyte Subpopulations and Body Composition in Patients with Metabolic Syndrome

Model	Variables	B	I.C. 95%	P
1	VAT versus Monocytes	−7.118	−11.5 to −2.8	0.002
2	VAT versus Lymph. NK	−1.683	−2.994 to −0.373	0.013
3	FM versus Monocytes	−1.516	−2.712 to −0.312	0.015
4	FM versus Lymph. NK	−0.499	−0.882 to −0.116	0.013
5	SMM versus Monocytes	−1.725	−2.748 to −0.701	0.002
6	SMM versus Lymph. NK	−0.613	−0.937 to −0.324	0.000

Statistically significant values are highlighted in bold.

VAT, visceral adipose tissue; FM, fat mass; SMM, skeletal muscle mass; Lymph NK, natural killer lymphocytes.

When patients with MS and VATI were analyzed under linear regression models (adjusted for gender and age) to establish a possible relationship between these lymphocyte subpopulations and body composition variables, no association was found between monocytes and NK lymphocytes with FM. In contrast, when SMM was analyzed, a negative relation with monocytes was observed, in which an increase of 1 kg of SMM was associated with a decrease of 1.5% of monocytes (range, 0.3%–3.0%) in persons with MS and VATI (P < 0.017) (data not shown).

Discussion

Obesity leads to immunophenotypic alterations at the level of adipose and systemic tissues, caused by excessive and inappropriate activation of the immune system, which plays an important role in the pathogenesis and exacerbation of the metabolic abnormalities of obesity, most notably insulin resistance, dyslipidemias, and MS.^3,9,10,25,26

In the present study, it was found that persons with obesity and increased VAT had highest levels of lipids and presence of MS. Nevertheless, a low percentage of the study population with obesity did not present alterations in the variables studied, while some persons with normal weight did show metabolic alterations. These data are in agreement with studies that have suggested that 10%–40% of individuals with obesity are metabolically healthy.^12,27,28 It has also been observed that individuals with normal weight have metabolic abnormalities.^12,27,28

In addition to the metabolic aspects, some immune cells (monocytes, neutrophils, T cells, B cells, among others) have been analyzed, which are directly correlated with the degree of metabolic dysfunction in patients with obesity.^29
–31 In the present investigation, we observed that total lymphocytes, CD4⁺, and total memory cells (CD3⁺CD45RO⁺) were increased in persons with obesity and VATI, while granulocytes and NK lymphocytes exhibited decreased values compared with those of patients with normal weight and normal VAT. Some studies have reported decreased NK³² and increased total lymphocytes,¹⁸ similar to those reported in this study.

It has been mentioned that chronic inflammation states are associated with the presence of MS.³¹ In this regard, the present study analyzed the association between the presence of MS with different lymphocyte subpopulations, without any relation found. However, when both MS and VAT variables were analyzed, total lymphocytes were found to increase and granulocytes to decrease in persons with VATI compared with persons with normal VAT, both in individuals with and without MS. These data indicate that, for total lymphocytes and granulocytes, the presence of VAT appears to be the primary factor that influences changes in both types of cells at the peripheral blood level.

Some authors have pointed out that during early stages of obesity, total lymphocytes play an important role in the development of MS, even when their co-morbidities are not present. It has been suggested that lymphocytes could be key cells in the initiation of morbidities associated with obesity.¹⁸ This possibility is supported in the present study by the fact that lymphocytes increased in patients who did not yet have MS but who did have VATI.

Based on these results, it can be hypothesized that total lymphocytes are increased in peripheral blood to amplify the inflammatory response in patients with obesity. The inflammatory response is a normal reaction of the organism to “foreign” pathogens, which is noted, in the case of obesity, in the participation of some “antigens” such as fatty acids and the release of molecules by the death of adipocytes, among others. This phenomenon has been termed by some authors as “altered antigen presentation.”²⁶

Otherwise, granulocytes are formed at a greater proportion by neutrophils, which are effector cells in the acute inflammatory response and which are recruited immediately from the blood at the sites of damage. This function is maintained during the inflammatory process of obesity.^1,33,34

In contrast, monocytes and NK lymphocytes were found to be decreased in persons with VATI and MS. Several studies have found that individuals with obesity have decreased NK lymphocytes in peripheral blood.^32,35 On the other hand, monocytes have been found to be both increased¹⁸ and decreased³⁶ in individuals with obesity and MS.

NK cells play an important role in controlling polarization into an activated phenotype of macrophages and inflammation in the VAT, regulating local and systemic insulin resistance, thus the presence of MS.³⁷ Monocytes and NK cells have even been attributed a predominant role in metabolic alterations associated with obesity and MS³⁷

The present study suggests that monocytes and NK cells could be decreased in the peripheral blood in patients with increased VAT and SM, since they may be found in the AT supporting the inflammatory process. In relation to the differential behavior in leukocyte mobilization between VAT and peripheral blood, it has been observed that the Treg cells of patients with obesity were found at a higher proportion in TAV than in peripheral blood.¹⁹

In this work, the cytotoxic and helper cells were analyzed without our finding any difference among the study groups. Moreover, CD3⁺CD45RO⁺ and CD4⁺ cells were found to be increased in individuals without MS and with VATI. This phenomenon could reflect what some authors have pointed out about the proliferation and activation capacity of memory cells, making evident the high degree of chronic adaptive immune activation in these patients.^26,38

It was also found that CD3⁺ virgin cells (CD45RA⁺) showed a tendency to decrease as the BMI increased or as the VATI. This behavior can be compared with that of older adults, who present a progressive reduction in the number and proportion of virgin cells and a concomitant increase in memory and senescent cells, which has been related to a progressive decrease in the function of the immune system and the increased prevalence of infectious diseases.³⁹ In addition, certain other studies have mentioned percentages of body fat as related to the presence of senescent T lymphocytes.^40,41

Finally, it was recently observed that, in addition to VAT, other tissues are inflammed, such as skeletal muscle, which also participates in the immunometabolic changes of persons with obesity.^16,42
–44 It has been reported that SMM possess an immune cell phenotype similar to that present in VAT, to what has been denominated “adiposopathy.”⁴⁴ In this regard, in the present study, we observed a negative correlation between SMM and the monocyte in individuals with MS and VATI, which could indicate that the higher SMM in these individuals, the greater the probability of monocyte infiltration occurring and the greater likelihood of the percentage at the peripheral level to be smaller, which would be related to the increase of the inflammatory process and the presence of metabolic alterations such as MS. In this manner, the involvement of this tissue in the inflammatory process triggered by visceral obesity returns to importance, reaffirming what other authors have mentioned with respect to the participation of nonlymphoid tissues in chronic systemic inflammation in obesity.

In other studies, it was found that in SMM, the content of macrophages is strongly associated with BMI, exhibiting higher values in individuals with obesity. This has led to thinking that these cells could secrete proinflammatory mediators that contribute to the low-level chronic inflammation state at the systemic level.^42,44

The strength of the present study is to contribute to knowledge on the association between VAT, SMM tissue, and the presence of co-morbidities such as MS with peripheral immune cells (lymphocytes and memory cells), in obesity inflammatory process. One of the limitations was not being able to include other inflammation variables such as adipocytokines, cytokines, or acute-phase proteins.

Conclusions

In the present study with young adults, significant changes were detected in the subpopulations of lymphocytes, suggesting that weight gain, and SMM and VAT accumulation cause immunological changes at the peripheral level. Changes in immune cells may reflect adipose inflammation.

The behavior of the memory, cytotoxic, and helper cells in the present study could reflect the chronic inflammatory process at the peripheral level and contribute to the presence of alterations and metabolic diseases such as MS. It is important to continue these studies to understand the effects of peripheral immune cells on systemic metabolism.

Footnotes

Acknowledgments

The authors thank Ms. María Magdalena Rodríguez-Magallanes at the Unit of Nutrition, Body Composition, and Energy Expenditure of the UAM-Xochimilco for facilitating the installation and the equipment to perform the measurements and the biochemical analysis. The authors thank Mr. Cesar Iván Ayala-Guzmán for his collaboration in carrying out measurements for the densitometry technique. The authors also thank Dr. Héctor E. Nájera-Catalán for statistical support, as well as CONACYT for the scholarship awarded to MSc Carmen Paulina Rodríguez-López (302016).

Author Disclosure Statement

No competing financial interests exist.

References

Huh

, Park

, Ham

, et al. Crosstalk between adipocytes and immune cells in adipose tissue inflammation and metabolic dysregulation in obesity. Mol Cells, 2014; 37:365–371.

Seijkens

, Kusters

, Chatzigeorgiou

, et al. Immune cell crosstalk in obesity: A key role for costimulation?. Diabetes, 2014; 63:3982–3991.

Al Haj Ahmad

, Al-Domi

. Complement 3 serum levels as a pro-inflammatory biomarker for insulin resistance in obesity. Diabetes Metab Syndr, 2017; 11:S229–S232.

Conroy

, Galvin

, Doyle

, et al. Parallel profiles of inflammatory and effector memory T cells in visceral fat and liver of obesity-associated cancer patients. Inflammation, 2016; 39:1729–1736.

Fucho

, Casals

, Serra

, et al. Ceramides and mitochondrial fatty acid oxidation in obesity. FASEB J, 2017; 31:1263–1272.

Cavallari

, Denou

, Foley

, et al. Different Th17 immunity in gut, liver, and adipose tissues during obesity: The role of diet, genetics, and microbes. Gut Microbes, 2016; 7:82–89.

Notarnicola

, Tutino

, Tafaro

, et al. Dietary olive oil induces cannabinoid CB2 receptor expression in adipose tissue of ApcMin/+ transgenic mice. Nutr Healthy Aging, 2016; 4:73–80.

Shirakawa

, Yan

, Shinmura

, et al. Obesity accelerates T cell senescence in murine visceral adipose tissue. J Clin Invest, 2016; 126:4626–4639.

Magrone

, Jirillo

, Spagnoletta

, et al. Immune profile of obese people and in vitro effects of red grape polyphenols on peripheral blood mononuclear cells. Oxid Med Cell Longev, 2017; 2017:9210862.

10.

Krishna

, Stefanovic-Racic

, Dedousis

, et al. Similar degrees of obesity induced by diet or aging cause strikingly different immunologic and metabolic outcomes. Physiol Rep, 2016; 4:pii: e12708.

11.

Maioli

, Goncalves

, Miranda

, et al. High sugar and butter (HSB) diet induces obesity and metabolic syndrome with decrease in regulatory T cells in adipose tissue of mice. Inflamm Res, 2016; 65:169–178.

12.

Pandolfi

, Ferraro

, Sananez

, et al. ATP-Induced inflammation drives tissue-resident Th17 cells in metabolically unhealthy obesity. J Immunol, 2016; 196:3287–3296.

13.

Cancello

, Henegar

, Viguerie

, et al. Reduction of macrophage infiltration and chemoattractant gene expression changes in white adipose tissue of morbidly obese subjects after surgery-induced weight loss. Diabetes, 2005; 54:2277–2286.

14.

O'Rourke

, White

, Metcalf

, et al. Hypoxia-induced inflammatory cytokine secretion in human adipose tissue stromovascular cells. Diabetologia, 2011; 54:1480–1490.

15.

Anderson

, Gutierrez

, Kennedy

, et al. Weight cycling increases T-cell accumulation in adipose tissue and impairs systemic glucose tolerance. Diabetes, 2013; 62:3180–3188.

16.

Esser

, Legrand-Poels

, Piette

, et al. Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes. Diabetes Res Clin Pract, 2014; 105:141–150.

17.

Deiuliis

, Shah

, et al. Visceral adipose inflammation in obesity is associated with critical alterations in tregulatory cell numbers. PLoS One, 2011; 6:e16376.

18.

Ryder

, Diez-Ewald

, Mosquera

, et al. Association of obesity with leukocyte count in obese individuals without metabolic syndrome. Diabetes Metab Syndr, 2014; 8:197–204.

19.

Donninelli

, Del Cornó

, Pierdominici

, et al. Distinct blood and visceral adipose tissue regulatory T cell and innate lymphocyte profiles characterize obesity and colorectal cancer. Front Immunol, 2017; 8(643):1–11.

20.

Marfell-Jones

. International Society for the Advancement of Kyneanthropometry I. Girths. 2006. Available at: www.google.com.mx/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwjBtP7Xiu7VAhXBPCYKHfkPAw8QFggpMAA&url=http%3A%2F%2Fwww.ceap.br%2Fmaterial%2FMAT17032011184632.pdf&usg=AFQjCNEmHO9AvHGFIvnotz0AvxiYsHi7Tw accessed July, 2017 .

21.

Organización Mundial de la Salud (OMS/WHO). 10 datos sobre la obesidad. 2015. Available at: www.who.int/features/factfiles/obesity/facts/es/ accessed July, 2017 .

22.

López

, Pérez

. Nutrition and metabolic syndrome. Nutrición Clínica, 2012; 32:92–97.

23.

Nishimura

, Manabe

, Nagasaki

, et al. CD8+ effector T cells contribute to macrophage recruitment and adipose tissue inflammation in obesity. Nat Med, 2009; 15:914–920.

24.

Durrer

, Francois

, Neudorf

, et al. Acute high-intensity interval exercise reduces human monocyte toll-like receptor 2 expression in type 2 diabetes. Am J Physiol Regul Integr Comp Physiol, 2017; 31:R529–R538.

25.

Wieser

, Adolph

, Grander

, et al. Adipose type I interferon signalling protects against metabolic dysfunction. Gut, 2018; 67:157–165.

26.

Mauro

, Smith

, Cucchi

, et al. Obesity-induced metabolic stress leads to biased effector memory CD4+ T cell differentiation via PI3K p110delta-Akt-mediated signals. Cell Metab, 2017; 25:593–609.

27.

Fabbrini

, Cella

, McCartney

, et al. Association between specific adipose tissue CD4+ T-cell populations and insulin resistance in obese individuals. Gastroenterology, 2013; 145:366–374.

28.

Patel

, Abate

. Body fat distribution and insulin resistance. Nutrients, 2013; 5:2019–2027.

29.

Hirosumi

, Tuncman

, Chang

, et al. A central role for JNK in obesity and insulin resistance. Nature, 2002; 420:333–336.

30.

Pal

, Wunderlich

, Spohn

, et al. Alteration of JNK-1 signaling in skeletal muscle fails to affect glucose homeostasis and obesity-associated insulin resistance in mice. PLoS One, 2013; 8:e54247.

31.

Patel

, Miranda-Nieves

, Chen

, et al. Targeting P-selectin glycoprotein ligand-1/P-selectin interactions as a novel therapy for metabolic syndrome. Transl Res, 2017; 183:1–13.

32.

Lynch

, O'Connell

, Kwasnik

, et al. Are natural killer cells protecting the metabolically healthy obese patient?. Obesity (Silver Spring), 2009; 17:601–605.

33.

Talukdar

, Oh da

, Bandyopadhyay

, et al. Neutrophils mediate insulin resistance in mice fed a high-fat diet through secreted elastase. Nat Med, 2012; 18:1407–1412.

34.

Kredel

, Siegmund

. Adipose-tissue and intestinal inflammation—Visceral obesity and creeping fat. Front Immunol, 2014; 5:462.

35.

Duffaut

, Zakaroff-Girard

, Bourlier

, et al. Interplay between human adipocytes and T lymphocytes in obesity: CCL20 as an adipochemokine and T lymphocytes as lipogenic modulators. Arterioscler Thromb Vasc Biol, 2009; 29:1608–1614.

36.

Simar

, Versteyhe

, Donkin

, et al. DNA methylation is altered in B and NK lymphocytes in obese and type 2 diabetic human. Metabolism, 2014; 63:1188–1197.

37.

Lee

, Kim

, Pae

, et al. Adipose natural killer cells regulate adipose tissue macrophages to promote insulin resistance in obesity. Cell Metab, 2016; 23:685–698.

38.

Olson

, Doyle

, de Boer

, et al. Associations of circulating lymphocyte subpopulations with type 2 diabetes: Cross-sectional results from the Multi-Ethnic Study of Atherosclerosis (MESA). PLoS One, 2015; 10:e0139962.

39.

Spielmann

, McFarlin

, O'Connor

, et al. Aerobic fitness is associated with lower proportions of senescent blood T-cells in man. Brain Behav Immun, 2011; 25:1521–1529.

40.

Tchkonia

, Morbeck

, Von Zglinicki

, et al. Fat tissue, aging, and cellular senescence. Aging Cell, 2010; 9:667–684.

41.

Brown

, Bigley

, Sherry

, et al. Training status and sex influence on senescent T-lymphocyte redistribution in response to acute maximal exercise. Brain Behav Immun, 2014; 39:152–159.

42.

Tam

, Sparks

, Johannsen

, et al. Low macrophage accumulation in skeletal muscle of obese type 2 diabetics and elderly subjects. Obesity (Silver Spring), 2012; 20:1530–1533.

43.

Marette

, Liu

, Sweeney

. Skeletal muscle glucose metabolism and inflammation in the development of the metabolic syndrome. Rev Endocr Metab Disord, 2014; 15:299–305.

44.

Khan

, Perrard

, Brunner

, et al. Intermuscular and perimuscular fat expansion in obesity correlates with skeletal muscle T cell and macrophage infiltration and insulin resistance. Int J Obes (Lond), 2015; 39:1607–1618.