Circulating prolactin level is increased in metabolically healthy obesity

in Endocrine Connections
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  • 1 Department of Endocrinology, Beijing Chao-yang Hospital, Capital Medical University, Chaoyang District, Beijing, China

Correspondence should be addressed to G Wang: drwg6688@126.com

Objective

Prolactin (PRL) has been demonstrated as a metabolic hormone to regulate energy metabolism recently. The present study aims to investigate the association between PRL and metabolic alterations in different obesity phenotypes.

Methods

A total of 451 drug-naive participants were recruited, comprising 351 obese patients and 100 age- and sex-matched healthy participants with normal weight. PRL, anthropometric, and clinical parameters were measured.

Results

In the obesity group, 15.1% (53/351) were categorized as 'metabolically healthy obesity (MHO)'. Besides favorable blood pressure, glucose, and lipids profiles, the MHO group exhibited increased PRL, and lower levels of high-sensitivity C-reactive protein (hsCRP), homeostasis model assessment of insulin resistance (HOMA-IR), and adipose tissue insulin resistance (adipo-IR) than the metabolically unhealthy obesity (MUHO) group (PRL, HOMA-IR, and adipo-IR: P < 0.01; hsCRP: P < 0.05). The severe MUHO group showed significantly decreased PRL levels than the mild MUHO group (P < 0.05). Multivariate linear regression analysis indicated that fasting plasma glucose (FBG) and adipo-IR were significantly associated with PRL (FBG: β = −0.263, P < 0.05; adipo-IR: β = −0.464, P < 0.01). Multivariable logistic regression analysis showed that hsCRP (OR = 0.824) and PRL (OR = 1.211) were independent predictors of MHO (all P < 0.01).

Conclusion

The MHO group had significantly increased circulating PRL levels when compared with the control and MUHO groups, and multivariable logistic regression analysis showed that PRL was independent predictors of MHO. Our findings suggested that increased circulating PRL might be a compensatory response for favoring energy metabolism during obesity.

Abstract

Objective

Prolactin (PRL) has been demonstrated as a metabolic hormone to regulate energy metabolism recently. The present study aims to investigate the association between PRL and metabolic alterations in different obesity phenotypes.

Methods

A total of 451 drug-naive participants were recruited, comprising 351 obese patients and 100 age- and sex-matched healthy participants with normal weight. PRL, anthropometric, and clinical parameters were measured.

Results

In the obesity group, 15.1% (53/351) were categorized as 'metabolically healthy obesity (MHO)'. Besides favorable blood pressure, glucose, and lipids profiles, the MHO group exhibited increased PRL, and lower levels of high-sensitivity C-reactive protein (hsCRP), homeostasis model assessment of insulin resistance (HOMA-IR), and adipose tissue insulin resistance (adipo-IR) than the metabolically unhealthy obesity (MUHO) group (PRL, HOMA-IR, and adipo-IR: P < 0.01; hsCRP: P < 0.05). The severe MUHO group showed significantly decreased PRL levels than the mild MUHO group (P < 0.05). Multivariate linear regression analysis indicated that fasting plasma glucose (FBG) and adipo-IR were significantly associated with PRL (FBG: β = −0.263, P < 0.05; adipo-IR: β = −0.464, P < 0.01). Multivariable logistic regression analysis showed that hsCRP (OR = 0.824) and PRL (OR = 1.211) were independent predictors of MHO (all P < 0.01).

Conclusion

The MHO group had significantly increased circulating PRL levels when compared with the control and MUHO groups, and multivariable logistic regression analysis showed that PRL was independent predictors of MHO. Our findings suggested that increased circulating PRL might be a compensatory response for favoring energy metabolism during obesity.

Introduction

The global epidemic of obesity remains a growing public health concern owing to its short- and long-term adverse health sequelae (1, 2). Epidemiological studies have demonstrated that obesity is associated with increased risk of hypertension, dyslipidemia, and type 2 diabetes, further resulting in the development of cardiovascular diseases (2, 3). However, recent studies have found that a proportion of obese subjects appear to have a favorable metabolic profile with no aforementioned metabolic abnormalities, which was called 'metabolically healthy obesity (MHO)' (4, 5, 6, 7). The precise mechanisms responsible for such a favorable metabolic phenotype in obesity are not entirely understood.

Prolactin (PRL) is secreted from anterior pituitary and named for its crucial effect on lactation (8, 9). Recent studies have demonstrated that PRL plays an important role in regulating energy metabolism (8, 9, 10, 11, 12, 13, 14, 15, 16). PRL administration increased insulin sensitivity and regulated glucose and lipid metabolism in rodent models (10, 11, 12). PRL receptor knockout caused insulin resistance and severe adipose tissue dysfunction in obese mice (10). Many population-based studies found that a relatively higher PRL level was associated with lower risk of diabetes, nonalcoholic fatty liver disease and metabolic syndrome (12, 13, 14, 15). The previous study showed that obese patients had different PRL levels compared to non-obese participants (15, 16, 17). As an intermediate stage from healthy to overt metabolic abnormalities, MHO patients may have different PRL levels (7, 18). However, until now, few reports regarding the correlation of PRL and MHO have been published. The present study aims to investigate the association between PRL and metabolic alterations in different obesity phenotypes.

Materials and methods

Study design and participants

This study consecutively recruited 351 obese patients (BMI ≥ 30.0 kg/m2) attending the Obesity Clinic at Beijing Chao-yang Hospital Affiliated to Capital Medical University from January 2019 to December 2019 (2). Meanwhile, 100 age- and sex-matched healthy controls with normal weight (18.5 kg/m2 ≤ BMI < 25.0 kg/m2) were consecutively enrolled from the Physical Examination Center at the same hospital (2). All participants were between 18 and 50 years old. No participants had a history of smoking (in the last 6 months), alcohol abuse, cardiovascular disease, thyroid dysfunction, disorders in thalamus and pituitary, polycystic ovarian syndrome, hypogonadism, severe hepatic insufficiency, renal function impairment, acute or chronic infections, systemic inflammatory disease, or cancer. Participants who were pregnant, lactating, or took any medications which influence PRL, glucose, lipid, or blood pressure were excluded. In order to investigate the association between PRL and metabolic alterations in the early stages of metabolic abnormalities, participants with a history of diabetes were excluded from the present study. We also excluded participants with PRL ≥ three times the upper limit of normal (ULN) or with oligo-/amenorrhoea or/and -galactorrhoea (19, 20). Polyethylene glycol precipitation was routinely taken for patients with hyperprolactinemia in order to exclude the possibility of suffering from macroprolactinemia. This study was conducted in accordance with the Declaration of Helsinki ethical principles. The protocol was approved by the Ethics Committee of Beijing Chao-yang Hospital affiliated with Capital Medical University. All enrolled participants provided a written informed consent.

Measurements of clinical parameters

A standard questionnaire was performed to collect information about health status and medications. Anthropometric measurements were conducted after overnight fasting. Alcohol consumption and intercourse were forbidden 24 h before the examination. Height and body weight were respectively measured to the nearest 0.1 cm and 0.1 kg, with participants wearing indoor clothes and no shoes, by the same trained group. Waist circumference (WC) and hip circumference (HC) were measured to the nearest 0.1 cm between the lower rib margin and the iliac crest and at the maximum width of the buttocks in the horizontal plane, using an anthropometric tape, with the patient standing straight, abdomen relaxed, and feet together. Blood pressure was measured twice after a 5-min rest in a supine position, and systolic blood pressure (SBP) and diastolic blood pressure (DBP) were calculated as the average of two measurements. BMI was calculated as weight divided by height squared (kg/m2).

Venous blood samples were obtained between 08:00 and 09:00 h. after overnight fasting. The serum levels of total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and non-esterified fatty acid (NEFA) were measured by colorimetric enzymatic assays using an autoanalyzer (Hitachi 747, Roche Diagnostics). Fasting plasma glucose (FBG) was measured by glucose oxidase method (Hitachi 747, Roche Diagnostics). Fasting plasma insulin (FINS) was estimated by the chemiluminescence method (Dimension Vista, Siemens Healthcare Diagnostics). Hemoglobin A1c (HbA1c) was measured by high-performance liquid chromatography, using an HLC-723G7 analyzer (Tosoh Corporation, Tokyo, Japan). High-sensitivity C-reactive protein (hsCRP) was measured using an immunonephelometric assay. Estradiol (normal range: <56.0 pg/mL for male), total testosterone (TT) (normal range: 1.60–7.26 ng/mL for male; <0.73 ng/mL for female), thyroid stimulating hormone (TSH) (normal range: 0.55–4.78 μIU/mL), and PRL (normal range: 2.5–17.0 ng/mL for male; 1.9–25.0 ng/mL for female) were measured using a chemiluminescent immunometric assay by the Immulite 1000 Immunoassay Analyzer (Siemens). Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated according to the following formulas: HOMA-IR = FINS (μIU/mL) × FBG (mmol/L)/22.5 (21). Adipose tissue insulin resistance (adipo-IR) was calculated according to the formula: Adipo-IR = fasting NEFA (mmol/L) × FINS (pmol/L) (22).

Definition of MHO

The present study used a strict definition of MHO proposed by Ortega (23). An individual was categorized as MHO if BMI ≥30 kg/m2 and met none of the following criteria: (i) elevated TG (≥1.7 mmol/L), (ii) reduced HDL-C (<1.0 mmol/L for men and <1.3 mmol/L for women), (iii) elevated SBP (≥130 mmHg) or/and DBP (≥85 mmHg), and (iv) elevated FBG (≥5.6 mmol/L). Obese patients with one or more of the four metabolic risk components were categorized as metabolically unhealthy obesity (MUHO) (23).

Statistical analysis

Data were analyzed using SPSS 22.0 (SPSS). The Kolmogorov–Smirnov test was used to evaluate the distribution of continuous data. Normally distributed variables were expressed as mean ± s.d., while variables with a skewed distribution, including TG, FINS, hsCRP, HOMA-IR, adipo-IR, estradiol, and TT, were given as the median, upper and lower quartiles. The differences in continuous data between the three groups (control, MHO, and MUHO groups) were analyzed using ANOVA or Kruskal–Wallis H test followed by a post hoc test. The differences between two groups (two subgroups in the MUHO group) were analyzed using an independent t-test for normally distributed variables or a Mann–Whitney U-test for skewed-distribution variables. The proportions were analyzed using chi-squared tests. Pearson or Spearman correlation was used to test the correlation between two parameters. Multivariate linear regression analysis was performed to assess the relationship between PRL and metabolic parameters. Multivariable logistic regression analysis was performed to find the independent predictors for MHO in obese people. Statistical significance was considered at P < 0.05 (two-tailed).

Results

Clinical characteristics of control, MHO, and MUHO groups

A total of 451 participants comprising 351 obese patients and 100 age- and sex-matched healthy controls were included in the present study. The clinical characteristics of the control, MHO, and MUHO groups were summarized in Table 1. Among 351 obese patients, 15.1% (53/351) were categorized as MHO. The control, MHO and MUHO groups were comparable with respect to age and gender. Among the three groups, there were no significant differences in estradiol and TT levels. Significant differences were observed for BMI, WC, HC, SBP, DBP, TC, TG, HDL-C, LDL-C, NEFA, FBG, FINS, HbA1c, hsCRP, HOMA-IR, adipo-IR, TSH, and PRL levels among three groups (BMI, WC, HC, SBP, DBP, TC, TG, HDL-C, FINS, HbA1c, HOMA-IR, and adipo-IR: P < 0.01; LDL-C, NEFA, FBG, hsCRP, and TSH: P < 0.05).

Table 1

Clinical characteristics of control, MHO, and MUHO groups.

ParametersControl group (n = 100)MHO group (n = 53)MUHO group (n = 298)P
Age, years32.3 ± 6.732.2 ± 7.631.5 ± 7.20.656
Gender, M/F, n35/6519/34101/1970.489
BMI, kg/m221.43 ± 2.6834.09 ± 2.79**34.00 ± 2.08**0.000
WC, cm79.92 ± 6.57106.81 ± 11.39**105.40 ± 10.26**0.000
HC, cm89.61 ± 6.10117.00 ± 4.12*116.92 ± 7.91**0.000
SBP, mmHg119.3 ± 5.2121.9 ± 5.7137.2 ± 5.7**,$0.000
DBP, mmHg70.2 ± 4.974.6 ± 7.386.3 ± 4.5**,$0.000
TC, mmol/L4.57 ± 0.904.93 ± 0.69*4.97 ± 0.93*0.006
TG, mmol/L0.89 (0.70–1.27)1.25 (0.89–1.49)*1.63 (1.18–2.30)**0.000
HDL-C, mmol/L1.49 ± 0.341.32 ± 0.181.03 ± 0.19**,$0.000
LDL-C, mmol/L2.69 ± 0.812.90 ± 0.522.92 ± 0.68*0.016
NEFA, mmol/L0.52 ± 0.340.60 ± 0.220.86 ± 0.30*0.035
FBG, mmol/L4.35 ± 0.454.39 ± 0.704.75 ± 0.89**,$$0.010
FINS, mIU/L5.46 (2.01–10.04)18.80 (10.07–26.09)**24.01 (15.96–36.54)**0.000
HbA1c, %5.20 ± 0.295.30 ± 0.355.53 ± 0.38**,$$0.000
hsCRP, mg/L0.22 (0.06–0.70)2.87 (0.80–4.97)**6.18 (1.26–11.32)**,$0.012
HOMA-IR1.43 (0.63–3.05)3.30 (1.89–5.68)*5.12 (3.13–8.35)**,$$0.000
adipo-IR26.37 (5.64–44.29)57.85 (23.22–95.99)*142.24 (93.15–238.83)**,$$0.000
Estradiol, pg/mL50.60 (32.08–84.42)93.00 (47.00–179.51)70.00 (47.99–110.00)0.124
TT, ng/mL2.19 (0.34–4.03)2.90 (0.88–4.77)2.80 (1.24–4.13)0.169
TSH, μIU/mL2.26 ± 1.102.47 ± 1.202.54 ± 1.30*0.032
PRL, ng/mL12.40 ± 7.1219.19 ± 5.59**14.44 ± 5.20$$0.000
Metabolic components
 Elevated BPa00257 (86.3%)
 Hyperglycemiab00118 (39.6%)
 Dyslipidemiac00263 (88.3%)

Data are means ± s.d. unless indicated otherwise. TG, FINS, hsCRP, HOMA-IR, adipo-IR, HOMA-β, estradiol, and TT are shown as medians (upper and lower quartiles).

*Significantly different at P < 0.05 vs control group; **Significantly different at P < 0.01 vs control group. $Significantly different at P < 0.05 vs MHO group; $Significantly different at P < 0.01 vs MHO group; aElevated BP was defined as: elevated SBP (≥130 mmHg) or/and DBP (≥85 mmHg); bHyperglycemia: elevated FBG (≥5.6 mmol/L); cDyslipidemia: elevated TG (≥1.7 mmol/L) or/and reduced HDL-C (<1.0 mmol/L for men and <1.3 mmol/L for women).

Adipo-IR, adipose tissue insulin resistance; DBP, diastolic blood pressure; F, females; FBG, fasting blood glucose; FINS, fasting insulin; HbA1c, hemoglobin A1c; HC, hip circumference; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment of insulin resistance; hsCRP, high-sensitivity C-reactive protein; LDL-C, low-density lipoprotein cholesterol; M, males; MHO, metabolically healthy obesity; MUHO, metabolically unhealthy obesity; NEFA, non-esterified fatty acid; PRL, prolactin;SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; TSH, thyroid stimulating hormone; TT, total testosterone; WC, waist circumference.

We next performed post hoc analyses and found that both MHO and MUHO groups had significantly increased BMI, WC, HC, TC, TG, FINS, hsCRP, HOMA-IR, and adipo-IR levels when compared with the control group (Table 1). The significant differences in SBP, DBP, HDL-C, LDL-C, NEFA, FBG, HbA1c, and TSH were observed when comparing the MUHO group with the control group (Table 1). The MHO group had decreased SBP, DBP, FBG, HbA1c, hsCRP, HOMA-IR, and adipo-IR levels and increased HDL-C levels, as compared to the MUHO group (Table 1). Interestingly, the MHO group had significantly higher PRL levels than the control and MUHO groups, while no significant difference in PRL level was observed in the latter two groups (Fig. 1 and Table 1).

Figure 1
Figure 1

Serum PRL levels of the control, MHO and MUHO groups. Values were expressed as a box and whisker with minimum and maximal value. PRL, prolactin; MHO, metabolically healthy obesity; MUHO, metabolically unhealthy obesity.

Citation: Endocrine Connections 10, 4; 10.1530/EC-21-0040

Clinical characteristics in MUHO subjects with different degrees of metabolic abnormality

The MUHO patients were further divide into the mild MUHO group (MUHO patients with one or two metabolic risk components) and severe MUHO group (MUHO patients with three or four metabolic risk components) according to the number of the following metabolic riskcomponents: (i) elevated TG (≥1.7 mmol/L), (ii) reduced HDL-C (<1.0 mmol/L for men and <1.3 mmol/L for women), (iii) elevated SBP (≥130 mmHg) or/and DBP (≥85 mmHg), and (iv) elevated FBG (≥5.6 mmol/L). There were no significant differences in age, gender, BMI, WC, HC, TC, LDL-C, HbA1c, estradiol, TT, or TSH between mild and severe MUHO groups (all P > 0.05, Table 2). The severe MUHO group had significantly higher levels of SBP, DBP, TG, NEFA, FBG, FINS, hsCRP, HOMA-IR, and adipo-IR, and lower levels of HDL-C and PRL when compared with the mild MUHO group (SBP, DBP, TG, HDL-C, FBG, and HOMA-IR: P < 0.01; NEFA, FINS, hsCRP, adipo-IR, and PRL: P < 0.05; Table 2).

Table 2

Clinical characteristics of the mild and severe MUHO groups.

ParametersMild MUHO group (n = 151)Severe MUHO group (n = 147)P
Age, years31.2 ± 7.631.9 ± 7.10.399
Gender, M/F, n46/10555/920.384
BMI, kg/m233.96 ± 4.4134.05 ± 4.290.110
WC, cm103.58 ± 13.30107.32 ± 13.470.055
HC, cm114.29 ± 12.16118.24 ± 9.700.060
SBP, mmHg132.8 ± 5.5143.5 ± 6.20.000
DBP, mmHg85.6 ± 4.987.9 ± 4.40.000
TC, mmol/L4.94 ± 0.695.02 ± 0.840.105
TG, mmol/L1.27 (0.98–1.62)2.14 (1.70–2.96)0.000
HDL-C, mmol/L1.08 ± 0.190.97 ± 0.180.000
LDL-C, mmol/L2.89 ± 0.702.95 ± 0.610.250
NEFA, mmol/L0.77 ± 0.210.90 ± 0.340.031
FBG, mmol/L4.57 ± 0.785.09 ± 0.940.000
FINS, mIU/L19.52 (13.10–31.06)27.31 (19.44–41.16)0.021
HbA1c, %5.49 ± 0.415.56 ± 0.360.180
hsCRP, mg/L5.54 (0.94–12.25)6.95 (1.84–9.76)0.036
HOMA-IR4.87 (3.02–6.50)6.96 (4.22–10.15)0.000
adipo-IR125.53 (68.07–149.69)156.88 (121.66–273.93)0.013
Estradiol, pg/mL72.50 (49.84–115.00)67.00 (47.86–104.10)0.548
TT, ng/mL2.75 (1.22–3.86)2.98 (1.27–4.92)0.332
TSH, μIU/mL2.50 ± 1.372.60 ± 1.220.578
PRL, ng/mL15.95 ± 5.4012.04 ± 4.460.004

Data are means ± s.d. unless indicated otherwise. TG, FINS, hsCRP, HOMA-IR, adipo-IR, HOMA-β, estradiol, and TT are shown as medians (upper and lower quartiles).

Adipo-IR, adipose tissue insulin resistance; DBP, diastolic blood pressure; F, females; FBG, fasting blood glucose; FINS, fasting insulin; HbA1c, hemoglobin A1c; HC, hip circumference; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment of insulin resistance; hsCRP, high-sensitivity C-reactive protein; LDL-C, low-density lipoprotein cholesterol; M, males; MHO, metabolically healthy obesity; MUHO, metabolically unhealthy obesity; NEFA, non-esterified fatty acid; PRL, prolactin; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; TSH, thyroid stimulating hormone; TT, total testosterone; WC, waist circumference.

The correlations between PRL and clinical parameters in all participants

Bivariate correlation analyses were used to investigate the correlations between circulating PRL levels and clinical parameters in all participants. The circulating PRL levels were negatively correlated with WC, TG, NEFA, FBG, HOMA-IR, and adipo-IR, and positively correlated with TSH levels (Table 3). A similar pattern of correlations was also observed when the analysis was stratified by gender (Table 3).

Table 3

Correlation between PRL and clinical parameters in all participants.

ParametersPRL (all)PRL (male)PRL (female)
rPrPrP
Age0.0060.9300.1350.098−0.1230.056
BMI−0.0580.3570.0250.762−0.0760.465
WC−0.1410.032−0.1510.023−0.1380.045
HC−0.1370.324−0.0710.879−0.1730.319
SBP−0.1720.564−0.1940.431−0.1610.502
DBP−0.0860.787−0.0770.874−0.0900.773
TC−0.0760.2210.1130.167−0.1140.077
TG−0.2600.000−0.1620.008−0.2460.000
HDL-C0.0220.7260.0310.6170.0290.769
LDL-C−0.0930.135−0.0370.236−0.0950.139
NEFA−0.2220.002−0.6010.000−0.2900.012
FBG−0.1360.030−0.1320.043−0.1550.033
FINS−0.0800.225−0.0360.842−0.1200.134
HbA1c−0.0100.879−0.1120.1720.0560.400
hsCRP−0.0120.963−0.0030.982−0.1420.531
HOMA-IR−0.2440.000−0.2280.000−0.2320.000
adipo-IR−0.3020.000−0.3240.000−0.2960.000
Estradiol−0.0070.9050.0790.497−0.0930.181
TT0.1060.0650.2260.198−0.1040.187
TSH0.1430.0370.1740.0330.2400.000

Adipo-IR, adipose tissue insulin resistance; DBP, diastolic blood pressure; FBG, fasting blood glucose; FINS, fasting insulin; HbA1c, hemoglobin A1c; HC, hip circumference; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment of insulin resistance; hsCRP, high-sensitivity C-reactive protein; LDL-C, low-density lipoprotein cholesterol; NEFA, non-esterified fatty acid; PRL, prolactin; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; TSH, thyroid stimulating hormone; TT, total testosterone; WC, waist circumference.

Multivariate linear regression analysis was used to further assess the relationship between PRL and potentially relevant variables, including gender, WC, TG, NEFA, FBG, HOMA-IR, adipo-IR, and TSH. The analysis showed that only FBG and adipo-IR levels were negatively associated with PRL levels (FBG: β = −0.263, P < 0.05; adipo-IR: β = −0.464, P < 0.01).

Multivariate logistic regression analysis to find the independent predictors for MHO

Multivariable logistic regression analysis was used to identify the potential predictive factors for MHO and to show that hsCRP and PRL were independent predictors of MHO after adjustment for age, gender, BMI, WC and HC levels (Table 4).

Table 4

Multivariate logistic regression analysis to find the independent predictors for MHO.

ParametersMHO
POR (95% CI)
Age0.3820.941 (0.773–1.120)
Male gender0.4940.902 (0.695–1.248)
BMI0.0790.892 (0.593–1.011)
WC0.3470.899 (0.639–1.053)
HC0.6900.938 (0.703–1.141)
HsCRP0.0020.824 (0.717–0.943)
PRL0.0011.211 (1.017–1.464)

HC, hip circumference; hsCRP, high-sensitivity C-reactive protein; MHO, metabolically healthy obesity; OR, odds ratio; PRL, prolactin; WC, waist circumference.

Discussion

The present study showed that in the obesity group, 15.1% (53/351) were categorized as MHO. Besides favorable metabolic profiles of blood pressure, glucose, and lipids, the MHO group exhibited significantly higher levels of PRL and lower levels of hsCRP, HOMA-IR, and adipo-IR than the MUHO group. Interestingly, the severe MUHO group showed significantly lower PRL levels than the mild MUHO group but similar PRL levels with the healthy controls. Multivariate linear regression analysis indicated that FBG and adipo-IR were significantly associated with PRL levels, and multivariable logistic regression analysis showed that hsCRP and PRL were independent predictors of MHO.

The present study showed that 15.1% were categorized as MHO in the obesity group, which was similar with many previous studies (5, 6, 7). Despite comparable BMI levels, relatively lower levels of HOMA-IR and adipo-IR were observed in the MHO group, as compared to the MUHO group. In addition, our study found that the MHO group had significantly lower hsCRP levels than the MUHO group (24, 25). A chronic inflammatory state has been demonstrated to cause insulin resistance via activating intracellular inflammatory pathways in many previous studies (26, 27). Therefore, the relatively lower inflammatory state and elevated insulin sensitivity in both systemic and adipose tissue seem to be responsible for favorable metabolic parameters in the MHO phenotype.

Accumulating evidence has suggested that PRL as a metabolic hormone to regulate energy metabolism (8, 9, 10, 11, 12, 13, 14, 15, 16). The present study showed that the MHO patients had significantly higher circulating PRL levels than the control and MUHO group. Correlation analyses found that the circulating PRL was negatively correlated with FBG, HOMA-IR, and adipo-IR in all participants. And multivariate linear regression analysis indicated that levels of FBG and adipo-IR were significantly associated with PRL levels. Adipocytes of rodents and humans expressed PRL receptors (10, 28). Previous studies found that increased PRL levels were involved in healthy expansion of adipose tissue in both rodents and humans, especially during obesity (10, 29). In diet-induced obese rats, PRL administration inhibited adipocyte hypertrophy, down-regulated inflammatory cytokine expression in visceral adipose tissue, and alleviated insulin resistance (10, 30). PRL receptor knockout mice exhibited higher insulin resistance and severe adipose tissue dysfunction (10). Therefore, increased circulating PRL might be a compensatory response for favoring energy metabolism during obesity. Consistently, the concept of homeostasis functionally increased transient hyperprolactinemia (HomeoFIT-PRL) which has been proposed recently by Macotela et al. and this proposed definition reveals that elevated PRL levels favor metabolic homeostasis in a situation of metabolic need or challenge (20).

Several cohort studies have demonstrated that individuals with a lower circulating PRL level have an increased risk of insulin resistance, diabetes, and metabolic syndrome (13, 14, 15). In the present study, the circulating PRL level of the MUHO group was similar with the healthy controls, but significantly lower than that of MHO group. Interestingly, the severe MUHO group showed significantly lower PRL levels than the mild MUHO group, but similar PRL levels with the healthy controls. These results might suggest that the compensatory increased PRL did not occur as expected in MUHO patients, especially in severe MUHO patients. In order to investigate the association between PRL and metabolic alterations in the early stage of metabolic abnormalities, participants with a history of diabetes or taking any medications which influence glucose, lipid, or blood pressure were excluded from the present study. Therefore, the metabolic abnormality was relatively mild in the enrolled MUHO patients, which might explain why reduced PRL levels were not observed in MUHO subjects.

Levels of BP, glucose, and atherogenic lipid were relatively higher in the MHO group than the healthy control group, although they remained within the normal range. Furthermore, significantly increased hsCRP, HOMA-IR, and adipo-IR levels were also observed in the MHO group when compared with healthy controls. These results suggested that MHO manifested elevated inflammatory factor expression, decreased insulin sensitivity, and compensatory increased insulin secretion, despite without overt metabolic abnormality. Moreover, many studies have confirmed that MHO subjects were more prone to develop multiple cardiovascular diseases over time as compared with normal-weight healthy individuals, even in the absence of metabolic abnormalities (31). Therefore, every obese individual should be encouraged to achieve normal weight in the long term.

The present study has some limitations. First, a single blood sample might not well reveal daily PRL secretion, on account of the pulsatile release of PRL (32). Although mostly pulsatile during the night, PRL secretion is relatively constant in the daytime (32). Thus, for the sake of narrowing PRL variation as much as possible, blood samples were taken between 8:00 and 9:00 h in the present study. Secondly, this study is a single center study with a relatively small sample size, which may have introduced selective bias and limited the generalizability of findings. Thirdly, PRL levels vary during the menstrual cycle but the present study did not standardize for the menstrual cycle (19). Finally, in fact, a universally accepted criterion for MHO has not yet been established so far (6). The present study used a strict definition of MHO proposed by Ortega, which was accepted by most previous studies (7, 23, 33, 34). Further research is needed to better understand the role of PRL and other relevant parameters in different obesity phenotypes and to elucidate the precise mechanisms underlying the transition from MHO to MUHO.

In conclusion, the MHO group had significantly increased circulating PRL levels when compared with the control and MUHO groups, and multivariable logistic regression analysis showed that PRL were independently associated with MHO. Our findings suggested that increased circulating PRL might be a compensatory response for favoring energy metabolism during obesity. However, further studies, including human, animal, and cell experiments, are still needed to confirm these results and get better insight into the underlying mechanisms.

Declaration of interest

The authors declare that there is no conflict of interest to that could be perceived as prejudicing the impartiality of the research reported.

Funding

This work was supported by grants from the Beijing Talents foundation (2018-12) to J L and the Chinese National Natural Science Foundation (No. 81770792) and Key Projects of Science and Technology Planning of Beijing Municipal Education Commission (KZ201810025038) to G W. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author contribution statement

G W was involved in final approval of the manuscript, conception or design; J L, L Z, J F, and Q W were involved in acquisition, analysis, or interpretation of data; J L and L Z were involved in drafting the work or revising.

References

  • 1

    Flegal KM, Kit BK, Orpana H & Graubard BI Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. Journal of American Medical Association 2013 309 7182. (https://doi.org/10.1001/jama.2012.113905)

    • Search Google Scholar
    • Export Citation
  • 2

    Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, Hu FB, Hubbard VS, Jakicic JM & Kushner RF et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Obesity Society. Journal of the American College of Cardiology 2014 63 29853023. (https://doi.org/10.1016/j.jacc.2013.11.004)

    • Search Google Scholar
    • Export Citation
  • 3

    Emerging Risk Factors Collaboration, Wormser D, Kaptoge S, Di Angelantonio E, Wood AM, Pennells L, Thompson A, Sarwar N, Kizer JR & Lawlor DA et al. Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies. Lancet 2011 377 10851095. (doi:10.1016/S0140-6736(11)60105-0)

    • Search Google Scholar
    • Export Citation
  • 4

    Schulze MB Metabolic health in normal-weight and obese individuals. Diabetologia 2019 62 558566. (https://doi.org/10.1007/s00125-018-4787-8)

    • Search Google Scholar
    • Export Citation
  • 5

    Stefan N, Haring HU, Hu FB & Schulze MB Metabolically healthy obesity: epidemiology, mechanisms, and clinical implications. Lancet: Diabetes and Endocrinology 2013 1 152162. (doi:10.1016/S2213-8587(13)70062-7)

    • Search Google Scholar
    • Export Citation
  • 6

    Tsatsoulis A & Paschou SA Metabolically healthy obesity: criteria, epidemiology, controversies, and consequences. Current Obesity Reports 2020 9 109120. (https://doi.org/10.1007/s13679-020-00375-0)

    • Search Google Scholar
    • Export Citation
  • 7

    Bluher M Metabolically healthy obesity. Endocrine Reviews 2020 41 405420. (https://doi.org/10.1210/endrev/bnaa004)

  • 8

    Bernard V, Young J & Binart N Prolactin – a pleiotropic factor in health and disease. Nature Reviews: Endocrinology 2019 15 356365. (https://doi.org/10.1038/s41574-019-0194-6)

    • Search Google Scholar
    • Export Citation
  • 9

    Ben-Jonathan N & Hugo E Prolactin (PRL) in adipose tissue: regulation and functions. Advances in Experimental Medicine and Biology 2015 846 135. (https://doi.org/10.1007/978-3-319-12114-7_1)

    • Search Google Scholar
    • Export Citation
  • 10

    Ruiz-Herrera X, de Los Rios EA, Diaz JM, Lerma-Alvarado RM, Martinez de la Escalera L, Lopez-Barrera F, Lemini M, Arnold E, Martinez de la Escalera G & Clapp C et al. Prolactin promotes adipose tissue fitness and insulin sensitivity in obese males. Endocrinology 2017 158 5668. (https://doi.org/10.1210/en.2016-1444)

    • Search Google Scholar
    • Export Citation
  • 11

    Holstad M & Sandler S Prolactin protects against diabetes induced by multiple low doses of streptozotocin in mice. Journal of Endocrinology 1999 163 229234. (https://doi.org/10.1677/joe.0.1630229)

    • Search Google Scholar
    • Export Citation
  • 12

    Zhang P, Ge Z, Wang H, Feng W, Sun X, Chu X, Jiang C, Wang Y, Zhu D & Bi Y Prolactin improves hepatic steatosis via CD36 pathway. Journal of Hepatology 2018 68 12471255. (https://doi.org/10.1016/j.jhep.2018.01.035)

    • Search Google Scholar
    • Export Citation
  • 13

    Li J, Rice MS, Huang T, Hankinson SE, Clevenger CV, Hu FB & Tworoger SS Circulating prolactin concentrations and risk of type 2 diabetes in US women. Diabetologia 2018 61 25492560. (https://doi.org/10.1007/s00125-018-4733-9)

    • Search Google Scholar
    • Export Citation
  • 14

    Corona G, Wu FC, Rastrelli G, Lee DM, Forti G, O’Connor DB, O’Neill TW, Pendleton N, Bartfai G & Boonen S et al. Low prolactin is associated with sexual dysfunction and psychological or metabolic disturbances in middle-aged and elderly men: the European Male Aging Study (EMAS). Journal of Sexual Medicine 2014 11 240253. (https://doi.org/10.1111/jsm.12327)

    • Search Google Scholar
    • Export Citation
  • 15

    Chirico V, Cannavo S, Lacquaniti A, Salpietro V, Mandolfino M, Romeo PD, Cotta O, Munafo C, Giorgianni G & Salpietro C et al. Prolactin in obese children: a bridge between inflammation and metabolic-endocrine dysfunction. Clinical Endocrinology 2013 79 537544. (https://doi.org/10.1111/cen.12183)

    • Search Google Scholar
    • Export Citation
  • 16

    Copinschi G, De Laet MH, Brion JP, Leclercq R, L’Hermite M, Robyn C, Virasoro E & Van Cauter E Simultaneous study of cortisol, growth hormone and prolactin nyctohemeral variations in normal and obese subjects. Influence of prolonged fasting in obesity. Clinical Endocrinology 1978 9 1526. (https://doi.org/10.1111/j.1365-2265.1978.tb03568.x)

    • Search Google Scholar
    • Export Citation
  • 17

    Kok P, Roelfsema F, Frolich M, Meinders AE & Pijl H Prolactin release is enhanced in proportion to excess visceral fat in obese women. Journal of Clinical Endocrinology and Metabolism 2004 89 44454449. (https://doi.org/10.1210/jc.2003-032184)

    • Search Google Scholar
    • Export Citation
  • 18

    Camhi SM, Must A, Gona PN, Hankinson A, Odegaard A, Reis J, Gunderson EP, Jacobs DR & Carnethon MR Duration and stability of metabolically healthy obesity over 30 years. International Journal of Obesity 2019 43 18031810. (https://doi.org/10.1038/s41366-018-0197-8)

    • Search Google Scholar
    • Export Citation
  • 19

    Melmed S, Casanueva FF, Hoffman AR, Kleinberg DL, Montori VM, Schlechte JA, Wass JAEndocrine Society. Diagnosis and treatment of hyperprolactinemia: an Endocrine Society clinical practice guideline. Journal of Clinical Endocrinology and Metabolism 2011 96 273288. (https://doi.org/10.1210/jc.2010-1692)

    • Search Google Scholar
    • Export Citation
  • 20

    Macotela Y, Triebel J & Clapp C Time for a new perspective on prolactin in metabolism. Trends in Endocrinology and Metabolism 2020 31 276286. (https://doi.org/10.1016/j.tem.2020.01.004)

    • Search Google Scholar
    • Export Citation
  • 21

    Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF & Turner RC Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985 28 412419. (https://doi.org/10.1007/BF00280883)

    • Search Google Scholar
    • Export Citation
  • 22

    Gastaldelli A, Gaggini M & DeFronzo RA Role of adipose tissue insulin resistance in the natural history of type 2 diabetes: results from the San Antonio metabolism study. Diabetes 2017 66 815822. (https://doi.org/10.2337/db16-1167)

    • Search Google Scholar
    • Export Citation
  • 23

    Ortega FB, Lavie CJ & Blair SN Obesity and cardiovascular disease. Circulation Research 2016 118 17521770. (https://doi.org/10.1161/CIRCRESAHA.115.306883)

    • Search Google Scholar
    • Export Citation
  • 24

    Karelis AD & Rabasa-Lhoret R Obesity: can inflammatory status define metabolic health? Nature Reviews: Endocrinology 2013 9 694695. (https://doi.org/10.1038/nrendo.2013.198)

    • Search Google Scholar
    • Export Citation
  • 25

    van Beek L, Lips MA, Visser A, Pijl H, Ioan-Facsinay A, Toes R, Berends FJ, Willems van Dijk K, Koning F & van Harmelen V Increased systemic and adipose tissue inflammation differentiates obese women with T2DM from obese women with normal glucose tolerance. Metabolism: Clinical and Experimental 2014 63 492501. (https://doi.org/10.1016/j.metabol.2013.12.002)

    • Search Google Scholar
    • Export Citation
  • 26

    Xu H, Barnes GT, Yang Q, Tan G, Yang D, Chou CJ, Sole J, Nichols A, Ross JS & Tartaglia LA et al. Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. Journal of Clinical Investigation 2003 112 18211830. (https://doi.org/10.1172/JCI19451)

    • Search Google Scholar
    • Export Citation
  • 27

    Lumeng CN, Bodzin JL & Saltiel AR Obesity induces a phenotypic switch in adipose tissue macrophage polarization. Journal of Clinical Investigation 2007 117 175184. (https://doi.org/10.1172/JCI29881)

    • Search Google Scholar
    • Export Citation
  • 28

    Ling C, Svensson L, Oden B, Weijdegard B, Eden B, Eden S & Billig H Identification of functional prolactin (PRL) receptor gene expression: PRL inhibits lipoprotein lipase activity in human white adipose tissue. Journal of Clinical Endocrinology and Metabolism 2003 88 18041808. (https://doi.org/10.1210/jc.2002-021137)

    • Search Google Scholar
    • Export Citation
  • 29

    Ponce AJ, Galvan-Salas T, Lerma-Alvarado RM, Ruiz-Herrera X, Hernandez-Cortes T, Valencia-Jimenez R, Cardenas-Rodriguez LE, Martinez de la Escalera G, Clapp C & Macotela Y Low prolactin levels are associated with visceral adipocyte hypertrophy and insulin resistance in humans. Endocrine 2020 67 331343. (https://doi.org/10.1007/s12020-019-02170-x)

    • Search Google Scholar
    • Export Citation
  • 30

    Carre N & Binart N Prolactin and adipose tissue. Biochimie 2014 97 1621. (https://doi.org/10.1016/j.biochi.2013.09.023)

  • 31

    Hansen L, Netterstrom MK, Johansen NB, Ronn PF, Vistisen D, Husemoen LLN, Jorgensen ME, Rod NH & Faerch K Metabolically healthy obesity and ischemic heart disease: a 10-year follow-up of the Inter99 study. Journal of Clinical Endocrinology and Metabolism 2017 102 19341942. (https://doi.org/10.1210/jc.2016-3346)

    • Search Google Scholar
    • Export Citation
  • 32

    Partsch CJ, Lerchl A & Sippell WG Characteristics of pulsatile and circadian prolactin release and its variability in men. Experimental and Clinical Endocrinology and Diabetes 1995 103 3343. (https://doi.org/10.1055/s-0029-1211327)

    • Search Google Scholar
    • Export Citation
  • 33

    Romagnolli C, Bensenor IM, Santos IS, Lotufo PA & Bittencourt MS Impact of metabolically healthy obesity on carotid intima-media thickness – the Brazilian Longitudinal Study of Adult Health. Nutrition, Metabolism, and Cardiovascular Diseases 2020 30 915921. (https://doi.org/10.1016/j.numecd.2020.02.012)

    • Search Google Scholar
    • Export Citation
  • 34

    Catoi AF & Busetto L Metabolically healthy obesity and bariatric surgery. Obesity Surgery 2019 29 29893000. (https://doi.org/10.1007/s11695-019-03964-8)

    • Search Google Scholar
    • Export Citation

 

     European Society of Endocrinology

     Society for Endocrinology

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    Serum PRL levels of the control, MHO and MUHO groups. Values were expressed as a box and whisker with minimum and maximal value. PRL, prolactin; MHO, metabolically healthy obesity; MUHO, metabolically unhealthy obesity.

  • 1

    Flegal KM, Kit BK, Orpana H & Graubard BI Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. Journal of American Medical Association 2013 309 7182. (https://doi.org/10.1001/jama.2012.113905)

    • Search Google Scholar
    • Export Citation
  • 2

    Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, Hu FB, Hubbard VS, Jakicic JM & Kushner RF et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Obesity Society. Journal of the American College of Cardiology 2014 63 29853023. (https://doi.org/10.1016/j.jacc.2013.11.004)

    • Search Google Scholar
    • Export Citation
  • 3

    Emerging Risk Factors Collaboration, Wormser D, Kaptoge S, Di Angelantonio E, Wood AM, Pennells L, Thompson A, Sarwar N, Kizer JR & Lawlor DA et al. Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies. Lancet 2011 377 10851095. (doi:10.1016/S0140-6736(11)60105-0)

    • Search Google Scholar
    • Export Citation
  • 4

    Schulze MB Metabolic health in normal-weight and obese individuals. Diabetologia 2019 62 558566. (https://doi.org/10.1007/s00125-018-4787-8)

    • Search Google Scholar
    • Export Citation
  • 5

    Stefan N, Haring HU, Hu FB & Schulze MB Metabolically healthy obesity: epidemiology, mechanisms, and clinical implications. Lancet: Diabetes and Endocrinology 2013 1 152162. (doi:10.1016/S2213-8587(13)70062-7)

    • Search Google Scholar
    • Export Citation
  • 6

    Tsatsoulis A & Paschou SA Metabolically healthy obesity: criteria, epidemiology, controversies, and consequences. Current Obesity Reports 2020 9 109120. (https://doi.org/10.1007/s13679-020-00375-0)

    • Search Google Scholar
    • Export Citation
  • 7

    Bluher M Metabolically healthy obesity. Endocrine Reviews 2020 41 405420. (https://doi.org/10.1210/endrev/bnaa004)

  • 8

    Bernard V, Young J & Binart N Prolactin – a pleiotropic factor in health and disease. Nature Reviews: Endocrinology 2019 15 356365. (https://doi.org/10.1038/s41574-019-0194-6)

    • Search Google Scholar
    • Export Citation
  • 9

    Ben-Jonathan N & Hugo E Prolactin (PRL) in adipose tissue: regulation and functions. Advances in Experimental Medicine and Biology 2015 846 135. (https://doi.org/10.1007/978-3-319-12114-7_1)

    • Search Google Scholar
    • Export Citation
  • 10

    Ruiz-Herrera X, de Los Rios EA, Diaz JM, Lerma-Alvarado RM, Martinez de la Escalera L, Lopez-Barrera F, Lemini M, Arnold E, Martinez de la Escalera G & Clapp C et al. Prolactin promotes adipose tissue fitness and insulin sensitivity in obese males. Endocrinology 2017 158 5668. (https://doi.org/10.1210/en.2016-1444)

    • Search Google Scholar
    • Export Citation
  • 11

    Holstad M & Sandler S Prolactin protects against diabetes induced by multiple low doses of streptozotocin in mice. Journal of Endocrinology 1999 163 229234. (https://doi.org/10.1677/joe.0.1630229)

    • Search Google Scholar
    • Export Citation
  • 12

    Zhang P, Ge Z, Wang H, Feng W, Sun X, Chu X, Jiang C, Wang Y, Zhu D & Bi Y Prolactin improves hepatic steatosis via CD36 pathway. Journal of Hepatology 2018 68 12471255. (https://doi.org/10.1016/j.jhep.2018.01.035)

    • Search Google Scholar
    • Export Citation
  • 13

    Li J, Rice MS, Huang T, Hankinson SE, Clevenger CV, Hu FB & Tworoger SS Circulating prolactin concentrations and risk of type 2 diabetes in US women. Diabetologia 2018 61 25492560. (https://doi.org/10.1007/s00125-018-4733-9)

    • Search Google Scholar
    • Export Citation
  • 14

    Corona G, Wu FC, Rastrelli G, Lee DM, Forti G, O’Connor DB, O’Neill TW, Pendleton N, Bartfai G & Boonen S et al. Low prolactin is associated with sexual dysfunction and psychological or metabolic disturbances in middle-aged and elderly men: the European Male Aging Study (EMAS). Journal of Sexual Medicine 2014 11 240253. (https://doi.org/10.1111/jsm.12327)

    • Search Google Scholar
    • Export Citation
  • 15

    Chirico V, Cannavo S, Lacquaniti A, Salpietro V, Mandolfino M, Romeo PD, Cotta O, Munafo C, Giorgianni G & Salpietro C et al. Prolactin in obese children: a bridge between inflammation and metabolic-endocrine dysfunction. Clinical Endocrinology 2013 79 537544. (https://doi.org/10.1111/cen.12183)

    • Search Google Scholar
    • Export Citation
  • 16

    Copinschi G, De Laet MH, Brion JP, Leclercq R, L’Hermite M, Robyn C, Virasoro E & Van Cauter E Simultaneous study of cortisol, growth hormone and prolactin nyctohemeral variations in normal and obese subjects. Influence of prolonged fasting in obesity. Clinical Endocrinology 1978 9 1526. (https://doi.org/10.1111/j.1365-2265.1978.tb03568.x)

    • Search Google Scholar
    • Export Citation
  • 17

    Kok P, Roelfsema F, Frolich M, Meinders AE & Pijl H Prolactin release is enhanced in proportion to excess visceral fat in obese women. Journal of Clinical Endocrinology and Metabolism 2004 89 44454449. (https://doi.org/10.1210/jc.2003-032184)

    • Search Google Scholar
    • Export Citation
  • 18

    Camhi SM, Must A, Gona PN, Hankinson A, Odegaard A, Reis J, Gunderson EP, Jacobs DR & Carnethon MR Duration and stability of metabolically healthy obesity over 30 years. International Journal of Obesity 2019 43 18031810. (https://doi.org/10.1038/s41366-018-0197-8)

    • Search Google Scholar
    • Export Citation
  • 19

    Melmed S, Casanueva FF, Hoffman AR, Kleinberg DL, Montori VM, Schlechte JA, Wass JAEndocrine Society. Diagnosis and treatment of hyperprolactinemia: an Endocrine Society clinical practice guideline. Journal of Clinical Endocrinology and Metabolism 2011 96 273288. (https://doi.org/10.1210/jc.2010-1692)

    • Search Google Scholar
    • Export Citation
  • 20

    Macotela Y, Triebel J & Clapp C Time for a new perspective on prolactin in metabolism. Trends in Endocrinology and Metabolism 2020 31 276286. (https://doi.org/10.1016/j.tem.2020.01.004)

    • Search Google Scholar
    • Export Citation
  • 21

    Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF & Turner RC Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985 28 412419. (https://doi.org/10.1007/BF00280883)

    • Search Google Scholar
    • Export Citation
  • 22

    Gastaldelli A, Gaggini M & DeFronzo RA Role of adipose tissue insulin resistance in the natural history of type 2 diabetes: results from the San Antonio metabolism study. Diabetes 2017 66 815822. (https://doi.org/10.2337/db16-1167)

    • Search Google Scholar
    • Export Citation
  • 23

    Ortega FB, Lavie CJ & Blair SN Obesity and cardiovascular disease. Circulation Research 2016 118 17521770. (https://doi.org/10.1161/CIRCRESAHA.115.306883)

    • Search Google Scholar
    • Export Citation
  • 24

    Karelis AD & Rabasa-Lhoret R Obesity: can inflammatory status define metabolic health? Nature Reviews: Endocrinology 2013 9 694695. (https://doi.org/10.1038/nrendo.2013.198)

    • Search Google Scholar
    • Export Citation
  • 25

    van Beek L, Lips MA, Visser A, Pijl H, Ioan-Facsinay A, Toes R, Berends FJ, Willems van Dijk K, Koning F & van Harmelen V Increased systemic and adipose tissue inflammation differentiates obese women with T2DM from obese women with normal glucose tolerance. Metabolism: Clinical and Experimental 2014 63 492501. (https://doi.org/10.1016/j.metabol.2013.12.002)

    • Search Google Scholar
    • Export Citation
  • 26

    Xu H, Barnes GT, Yang Q, Tan G, Yang D, Chou CJ, Sole J, Nichols A, Ross JS & Tartaglia LA et al. Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. Journal of Clinical Investigation 2003 112 18211830. (https://doi.org/10.1172/JCI19451)

    • Search Google Scholar
    • Export Citation
  • 27

    Lumeng CN, Bodzin JL & Saltiel AR Obesity induces a phenotypic switch in adipose tissue macrophage polarization. Journal of Clinical Investigation 2007 117 175184. (https://doi.org/10.1172/JCI29881)

    • Search Google Scholar
    • Export Citation
  • 28

    Ling C, Svensson L, Oden B, Weijdegard B, Eden B, Eden S & Billig H Identification of functional prolactin (PRL) receptor gene expression: PRL inhibits lipoprotein lipase activity in human white adipose tissue. Journal of Clinical Endocrinology and Metabolism 2003 88 18041808. (https://doi.org/10.1210/jc.2002-021137)

    • Search Google Scholar
    • Export Citation
  • 29

    Ponce AJ, Galvan-Salas T, Lerma-Alvarado RM, Ruiz-Herrera X, Hernandez-Cortes T, Valencia-Jimenez R, Cardenas-Rodriguez LE, Martinez de la Escalera G, Clapp C & Macotela Y Low prolactin levels are associated with visceral adipocyte hypertrophy and insulin resistance in humans. Endocrine 2020 67 331343. (https://doi.org/10.1007/s12020-019-02170-x)

    • Search Google Scholar
    • Export Citation
  • 30

    Carre N & Binart N Prolactin and adipose tissue. Biochimie 2014 97 1621. (https://doi.org/10.1016/j.biochi.2013.09.023)

  • 31

    Hansen L, Netterstrom MK, Johansen NB, Ronn PF, Vistisen D, Husemoen LLN, Jorgensen ME, Rod NH & Faerch K Metabolically healthy obesity and ischemic heart disease: a 10-year follow-up of the Inter99 study. Journal of Clinical Endocrinology and Metabolism 2017 102 19341942. (https://doi.org/10.1210/jc.2016-3346)

    • Search Google Scholar
    • Export Citation
  • 32

    Partsch CJ, Lerchl A & Sippell WG Characteristics of pulsatile and circadian prolactin release and its variability in men. Experimental and Clinical Endocrinology and Diabetes 1995 103 3343. (https://doi.org/10.1055/s-0029-1211327)

    • Search Google Scholar
    • Export Citation
  • 33

    Romagnolli C, Bensenor IM, Santos IS, Lotufo PA & Bittencourt MS Impact of metabolically healthy obesity on carotid intima-media thickness – the Brazilian Longitudinal Study of Adult Health. Nutrition, Metabolism, and Cardiovascular Diseases 2020 30 915921. (https://doi.org/10.1016/j.numecd.2020.02.012)

    • Search Google Scholar
    • Export Citation
  • 34

    Catoi AF & Busetto L Metabolically healthy obesity and bariatric surgery. Obesity Surgery 2019 29 29893000. (https://doi.org/10.1007/s11695-019-03964-8)

    • Search Google Scholar
    • Export Citation