Effects of colchicine on lipolysis and adipose tissue inflammation in adults with obesity and metabolic syndrome

Abstract

Objective:

To examine if colchicine’s anti-inflammatory effects would improve measures of lipolysis and distribution of leukocyte populations in subcutaneous adipose tissue (SAT).

Methods:

We conducted a secondary analysis of a double-blind, randomized, placebo-controlled pilot study in which 40 adults with obesity and metabolic syndrome (MetS) were randomized to colchicine 0.6mg or placebo twice-daily for 3 months. Noninsulin-suppressible (l₀), insulin-suppressible (l₂), and maximal (l₀+l₂) lipolysis rates were calculated by minimal model analysis. Body composition was determined by dual-energy x-ray absorptiometry. SAT leukocyte populations were characterized by flow cytometry analysis from biopsied samples obtained pre- and post-intervention.

Results:

Colchicine treatment significantly decreased l₂ and l₀+l₂ versus placebo (p’s <0.05). These changes were associated with a significant reduction in markers of systemic inflammation, including high-sensitivity C-reactive protein, resistin, and circulating monocytes and neutrophils (p’s <0.01). Colchicine did not significantly alter SAT leukocyte population distributions (p’s >0.05).

Conclusions:

In adults with obesity and MetS, colchicine appears to improve insulin regulation of lipolysis and reduce markers of systemic inflammation independent of an effect on local leukocyte distributions in SAT. Further studies are needed to better understand the mechanisms by which colchicine affects adipose tissue metabolic pathways in adults with obesity and MetS.

Introduction

Obesity is the third most common cause of preventable death in the United States and is a major risk factor for the development of insulin resistance, metabolic syndrome (MetS), type 2 diabetes (T2D), and cardiovascular disease (CVD) [1, 2]. Among adults in the United States, 25% (84 million) have pre-diabetes, and an estimated 70% of those will progress to type 2 diabetes (T2D) during their lifetimes [3]. For this reason, it is of great importance to identify potential approaches to prevent diabetes onset in those with obesity or prediabetes.

Obesity-associated chronic inflammation contributes to the development of insulin resistance, adipose tissue (AT) dysfunction, and the progression to T2D [4, 5]. Inflammatory cytokines promote insulin resistance in adipose tissue by phosphorylating and inactivating insulin receptor substrate-1 and other downstream signaling molecules [6]. This in turn impairs insulin’s ability to stimulate glucose uptake and to suppress lipolysis, thereby promoting excessive free fatty acid (FFA) release from adipocytes [7]. Elevated circulating FFAs further contribute to systemic insulin resistance and atherosclerosis by activating additional pro-inflammatory pathways (e.g. NF-kβ, oxidative stress, Toll-like receptors, etc.) [8]. Mouse studies have shown that suppressing the obesity-induced inflammatory state may prevent or reverse obesity-associated metabolic complications [9, 10]; however, human studies have produced mixed results [11, 12].

Colchicine, a well-studied microtubule inhibitor traditionally used to treat gout, suppresses inflammation by inhibiting leukocyte locomotion and diapedesis, inflammasome formation, and cytokine production [4, 13]. Recently, colchicine has gained renewed interest for its potential metabolic and cardiovascular benefits [14, 15]. A retrospective study in adults with gout suggested that colchicine users may possibly have a reduced incidence of T2D [16], while large prospective clinical trials demonstrated that colchicine reduced major adverse cardiovascular events among individuals with CVD [17, 18].

Because AT plays a key role in inciting and sustaining the chronic inflammatory state associated with obesity, we sought to examine colchicine’s effects on measures of AT inflammation and metabolic function. Herein, we describe the results of a secondary analysis of a pilot randomized controlled trial (RCT) of colchicine in adults with obesity and MetS [14] that found suggestive evidence for improvements in insulin sensitivity in those treated with colchicine versus placebo. The primary aim of the current analysis was to evaluate colchicine’s effect on measures of lipolysis. A secondary aim was to investigate colchicine’s effects on leukocyte populations in subcutaneous adipose tissue (SAT). To our knowledge, colchicine’s effects on measures of lipolysis and SAT leukocyte population distributions are not known and may represent key mechanisms by which colchicine improves metabolic and cardiovascular outcomes.

Methods

Study Population

The detailed methods of the pilot RCT have been published previously [14]. Briefly, we recruited participants to attend visits at the NIH Clinical Research Center (CRC) in Bethesda, MD between 2014 and 2018. Eligibility requirements were age ≥ 18 years; body mass index (BMI) ≥ 30 kg/m²; MetS as defined in the 2005 American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement [19]; evidence of chronic inflammation (hsCRP ≥ 2.0 mg/L) and insulin resistance (homeostatic model of insulin resistance [HOMA-IR] ≥ 2.6). Subjects with significant medical conditions such as diabetes mellitus, uncontrolled hypertension, or congestive heart failure were excluded from the study. Individuals taking medications affecting glycemia (e.g. metformin, insulin), body weight, inflammation (e.g. steroids, NSAIDs), or lipids/cholesterol (e.g. statins, fibrates) were also excluded from the study. Exclusion criteria for premenopausal female participants included irregular menses, pregnancy, breast-feeding, planning pregnancy in the next 6 months, or being unwilling to use contraception during their participation in the study.

Study Design

In this randomized, double blind, single center, placebo-controlled trial, 40 subjects were randomly assigned in a 1:1 ratio to 3 months of oral colchicine 0.6mg (Spectrum Chemical MFG Corp, New Brunswick, NJ) or identically-appearing placebo capsules, twice daily. The primary outcome was change in insulin sensitivity. No subject, investigator, or other medical or nursing staff interacting with study subjects was aware of study group assignments during the trial.

The experimental protocol was approved by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Institutional Review Board and was registered at ClinicalTrials.gov (NCT02153983). The study was conducted in compliance with the principles of the Declaration of Helsinki. The study was overseen by a Data and Safety Monitoring Board convened by NICHD. All participants provided written informed consent prior to study participation.

Study Procedures

Assessments were performed at baseline and after 3 months of study drug. Subjects were seen as outpatients at the NIH CRC and were asked to fast after 10 PM the previous evening. All subjects underwent body composition assessment using a whole body dual-energy x-ray absorptiometry (GE Lunar iDXA, GE Healthcare, Madison WI; software GE enCore 15 with CoreScan algorithm), height using a calibrated stadiometer, weight using a calibrated digital scale, and BMI was calculated as weight/height² (kg/m²).

Concentrations of insulin and FFA were measured by an electrochemiluminescent immunoassay using a Roche Cobas e601analyzer (Roche Diagnostics, Indianapolis, IN). Blood glucose and hsCRP were measured on a Roche Cobas 6000 analyzer (Roche Diagnostics, Indianapolis, IN). Fasting samples were used to estimate whole-body insulin resistance by the HOMA-IR index (insulin [μU/mL] x glucose [mg/dL]/405). The Adipo-IR index, a previously described [20] surrogate measure of adipose insulin resistance (i.e. ability of insulin to suppress lipolysis), was calculated as insulin (μU/mL) x FFA (mEq/L). Complete blood count and differential was performed using a Sysmex XN-3000 analyzer (Sysmex, Lincolnshire, IL). Neutrophil-lymphocyte ratio was calculated by dividing the absolute circulating neutrophil count by the absolute lymphocyte count. GlycA concentrations were measured with a Vantera Clinical Analyzer using the LipoProfile-3 algorithm (LabCorp, Burlington, NC) as described previously [21, 22]. Serum samples were analyzed for 1305 circulating factors using the SomaScan 1.3k Assay (SomaLogic, Boulder, CO) as previously described [15].

An insulin-modified 3-hour frequently sampled intravenous glucose tolerance test (FSIVGTT) was performed pre- and post-treatment as described previously [14]. Briefly, a glucose load of 50% dextrose 0.3 g/kg given as a smooth bolus over 2 minutes was administered at time 0 and a bolus of 0.05 U/kg insulin was given just before minute 20. Blood samples were collected at times −15, −10, −5, −1 (averaged as the baseline), then at +2, 3, 4, 5, 6, 8, 10, 14, 19, 22, 25, 30, 40, 50, 70, 100, 140 and 180 minutes after glucose injection for the measurement of plasma glucose, plasma FFA, and serum insulin concentrations. Samples were collected on ice, centrifuged within 1 hour of collection, and measured at the NIH Department of Laboratory Medicine on the day of sample collection.

Acute insulin response to glucose (AIR_G) was calculated as the insulin area under the curve using the trapezoidal rule obtained during the first 14 minutes. Insulin sensitivity (S_I) was estimated using minimal model analysis (SAAM II, The Epsilon Group, Charlotte, VA).

In the fasted state, insulin concentrations are typically at their lowest, and therefore AT lipolysis rates and FFA appearance in circulation are typically at their highest. Conversely, in a high insulin state, either due to robust endogenous secretion or exogenous insulin administration, lipolysis is suppressed to a minimal, but typically non-zero, rate termed the “non-insulin suppressible lipolysis rate.” The difference between the fasting, maximal rate and the minimal, non-insulin suppressible rate is the “insulin-suppressible rate.” Non-insulin-suppressible (l₀), insulin-suppressible (l₂), and maximal (l₀+l₂) lipolysis rates were calculated from the FSIVGTT via the minimal model as previously described [22, 23].

SAT Biopsy Procedure

Percutaneous adipose tissue biopsy was performed by mini-liposuction technique, as previously described [24]. Briefly, after skin anesthesia at the biopsy site (lateral to the periumbilical abdominal area) with 1% lidocaine injection, a 3 mm incision was made in the abdominal skin via a scalpel. Approximately 2g specimens of SAT were collected by manual suction using a liposuction mini-cannula (MicroAire Surgical Instruments, Charlottesville, VA) connected to a 20 ml syringe. Adipose tissue was immediately placed on ice, washed three times in 50mL phosphate-buffered saline, and regions containing obvious blood vessels were removed prior to transporting tissue. Adipose tissue was then rapidly prepared for flow cytometry analysis; the time from tissue isolation to fixation was routinely less than 1 hour.

The SAT biopsy was an optional part of the study, and in some instances participants declined to undergo the procedure, and in other cases, insufficient adipose tissue was obtained. Therefore pre-post-SAT biopsy data were available for only 24 participants.

SAT Flow Cytometry

SAT leukocyte populations were characterized and analyzed by flow cytometry of the stromal vascular fraction obtained after collagenase digestion and centrifugation to isolate the stromovascular fraction from biopsied SAT samples. The staining panel used 12 unique surface markers for the detection of cell populations. The following antibodies were used: CD14 (BD Biosciences, Franklin Lakes, NJ, #560349), HLA-DR (BD Biosciences, Franklin Lakes, NJ, #561224), CD206 (BD Biosciences, Franklin Lakes, NJ, #551135), CD123 (BD Biosciences, Franklin Lakes, NJ, #558714), CD56 (BD Biosciences, Franklin Lakes, NJ, # 555516), Live/Dead fixable dead cell stain (Invitrogen, Waltham, MA, #L34972), CD11c (BD Biosciences, Franklin Lakes, NJ, #561356), CD16 (BD Biosciences, Franklin Lakes, NJ, #561304), CD64 (BD Biosciences, Franklin Lakes, NJ, #561188), CD3 (BD Biosciences, Franklin Lakes, NJ, #560176), CD19 (BD Biosciences, Franklin Lakes, NJ, #643078), and CD20 (BD Biosciences, Franklin Lakes, NJ, #560734) [25]. After staining the cells with the cocktail of antibodies mentioned above, the cells were washed with PBS containing 1% fetal bovine serum (FBS). Cell pellets were then resuspended in 300mL of PBS containing 1% paraformaldehyde. These stained and fixed cells were then acquired on BD LSR fortessa x20 equipped with 355, 407, 488, 532, and 633mm laser lines; flow cytometry standard (FCS) data files were generated using BD FACSDiva™ Software. These FCS files were analyzed by using FlowJo software 9.6.2 (FlowJo, LLC, Ashland, OR). Initially, doublets and debris were removed using physical properties of the cells (forward scatter and side-scatter properties), and then the SAT leukocyte populations were expressed as a percentage of total live cells. Figure 1 shows the staining panel (A) and the gating strategies (B, C) used to analyze the SAT leukocyte populations.

Figure 1: Staining panel and gating strategies for flow cytometry characterization of leukocyte populations in subcutaneous adipose tissue.

(A) 10 color staining panel for high parameter flow cytometry analysis, (B) gating scheme, and (C) gating example for cell populations identified by the staining panel.

Statistical Analysis

This secondary analysis assessed differences at the final visit as compared to baseline between treatment arms using repeated measures ANOVA. Baseline differences were analyzed with Student’s t test and correlations between variables using Pearson’s correlation coefficient. Data were transformed as necessary to maintain assumptions of normality. Variables that remained nonnormally distributed even after transformation were denoted as such in the manuscript and were assessed using the Mann-Whitney U test or Spearman correlation coefficient, respectively. Given the exploratory nature of this study, no correction for multiple comparisons was applied, and a nominal P < 0.05 was considered significant. SPSS v25.0 (IBM Corp, Armonk, NY) was used for all statistical analyses.

Results

40 subjects were randomized to colchicine (n=21) or placebo (n=19). Three subjects did not complete the trial, and one subject was withdrawn because of non-compliance with study procedures. As a result, data from 18 subjects in the colchicine group (Mean ± SD: age 48.4 ± 13.5 y; BMI 39.3 ± 6.3 kg/m2; sex: female 72.2%; race: non-black 72.2%) and 18 subjects in the placebo group (age 44.7 ± 10.2 y; BMI 41.8 ± 8.2 kg/m2; sex: female 77.8%; race: non-black 77.8%) were available for this study. Pre- and post- SAT biopsy data were available only for 14 colchicine (age 48.5 ± 12.8 y; BMI 39.2 ± 6.7 kg/m2; sex: female 71.4%) and 10 placebo (age 41.2 ± 8.3 y; BMI 40.8 ± 8.9 kg/m2; sex: female 70%) subjects, respectively. The baseline characteristics of the subjects studied are shown in Table 1. The baseline characteristics of the subjects studied who supplied pre- and post-intervention adipose biopsy data are shown in Table 2. The individuals studied without complete adipose biopsy data did not significantly differ in sociodemographic characteristics from those with complete data.

Table 1: Baseline Participant Characteristics.

Data are reported as the unadjusted Mean ± SD except where otherwise indicated. l₀, non-insulin-suppressible lipolysis rate; l₂, insulin-suppressible lipolysis rate; l₀+l₂, maximal lipolysis rate; S_I, insulin sensitivity; AIR_G, acute insulin response to glucose; HOMA-IR, homeostatic model assessment of insulin resistance; Adipo-IR, adipose insulin resistance index; FFA, free fatty acids; hsCRP, high sensitivity C-reactive protein; WBC, white blood cell count; DXA, dual-energy x-ray absorptiometry; RFU, relative fluorescence units. P-values shown are for t-tests comparing the colchicine and placebo groups unless otherwise indicated.

Variable	Colchicine (n=18)	Placebo (n=18)	p-value
Age (y)	48.4 ± 13.5	44.7 ± 10.3	0.36
Race, n (%)			0.99
Black	5 (27.8)	4 (22.2)
Non-Black	13 (72.2)	14 (77.8)
Sex (Female), n (%)	13 (72.2)	14 (77.8)	0.99
Body mass index (kg/m2)	39.3 ± 6.3	41.8 ± 8.2	0.31
DXA Body fat (%)	48.3 ± 4.0	48.6 ± 5.8	0.86
DXA Subcutaneous adipose tissue (kg)*	47.4 ± 10.4	46.8 ± 8.4	0.63
DXA Visceral adipose tissue (kg)*	1.95 ± 0.89	1.96 ± 0.94	0.94
l0 (x10−3mEq/L*min−1)	3.80 ± 0.96	3.80 ± 1.23	0.99
l2 (x10−3mEq/L*min−1)	87.93 ± 63.27	59.42 ± 31.92	0.27
l0+l2 (x10−3mEq/L*min−1)	91.73 ± 63.0	63.22 ± 31.77	0.24
SI (x10−5min−1mU/mL)	9.51 ± 3.86	9.73 ± 4.48	0.95
AIRG (mU/mL*min)	1254 ± 647	2,058 ± 3,144	0.72
Fasting Glucose (mg/dL)	106.2 ± 9.9	100.8 ± 7.3	0.07
HOMA-IR	6.66 ± 3.00	6.08 ± 2.75	0.55
FFA (mEq/L)	0.76 ± 0.29	0.78 ± 0.21	0.73
Adipo-IR (mU*mEq/L)	17.9 ± 8.0	18.5 ± 7.8	0.81
Hemoglobin A1c (%)	5.64 ± 0.39	5.49 ± 0.45	0.29
hsCRP (mg/L)	7.51 ± 7.71	6.45 ± 4.23	0.62
GlycA (mmol/L)	424.48 ± 62.30	421.17 ± 40.60	0.85
WBC (x1000/μL)	7.31 ± 1.91	6.36 ± 1.76	0.10
Whole Blood Neutrophils (x1000/μL)	4.41 ± 1.52	3.46 ± 1.16	0.04
Whole Blood Monocytes (x1000/μL)	0.59 ± 0.19	0.51 ± 0.16	0.20
Whole Blood Lymphocytes (x1000/μL)	2.03 ± 0.52	2.13 ± 0.53	0.58
Whole Blood Neutrophil-Lymphocyte Ratio	2.24 ± 0.85	1.63 ± 0.33	0.003†
Adiponectin (RFU)‡	2,244.0 ± 661.6	2,253.9 ± 525.0	0.90
Leptin (RFU)‡	19,284.2 ± 5,689.4	20,487.7 ± 7,441.2	0.60
Soluble Leptin Receptor (RFU)‡	2,227.8 ± 473.7	2,291.4 ± 656.3	0.75
Resistin (RFU)‡	4,262.6 ± 945.4	3,495.1 ± 832.4	0.02

^*DXA data were not available for all subjects (colchicine: n=13, placebo: n=14).

^‡Adipokine data were not available for one subject (colchicine: n=17, placebo n=18).

^†non-parametric analysis.

Table 2: Baseline Participant Characteristics for Flow Cytometry Analysis.

Data are reported as the unadjusted Mean ± SD except where otherwise indicated. SAT, subcutaneous adipose tissue; NK, natural killer cell; mDC, myeloid dendritic cell; pDC; plasmacytoid dendritic cell.

Variable	Colchicine (n=14)	Placebo (n=10)	p-value
Age (y)	48.5 ± 12.8	41.2 ± 8.3	0.13
Race, n (%)			0.99
Black	4 (28.6)	2 (20)
Non-Black	10 (71.4)	8 (80)
Sex (Female), n (%)	10 (71.4)	7 (70)	0.99
Body mass index (kg/m2)	39.2 ± 6.7	40.8 ± 8.9	0.62
Body fat (%)	47.7 ± 4.2	46.8 ± 4.8	0.65
Subcutaneous adipose tissue (kg)*	46.9 ± 10.0	43.1 ± 6.7	0.21
Visceral adipose tissue (kg)*	1.98 ± 1.0	1.57 ± 0.77	0.33
SAT Monocyte (%)	34.3 ± 18.3	33.1 ± 16.6	0.87
SAT Neutrophil (%)	21.4 ± 23.6	19.7 ± 11.9	0.83
SAT NK Cell (%)	15.4 ± 13.5	13.7 ± 13.2	0.77
SAT mDC (%)	1.2 ± 0.94	1.5 ± 1.3	0.37
SAT pDC (%)	0.36 ± 0.49	0.22 ± 0.20	0.64
SAT M1 Macrophage (%)	3.2 ± 2.0	3.7 ± 1.4	0.50
SAT M2 Macrophage (%)	3.0 ± 1.8	3.3 ± 2.9	0.80
SAT M1/M2 Ratio	1.5 ± 1.1	1.7 ± 1.0	0.70

^*n=10 for colchicine arm and n=8 for placebo arm. P-values shown are for t-tests comparing the colchicine and placebo groups.

The changes in measures of lipolysis among treatment groups are shown in Table 3 and Figure 2. By repeated measures ANOVA analysis, change from baseline to the 3-month timepoint in maximal (l₀+l₂) lipolysis rate was significantly different between colchicine and placebo (p=0.047). Change from baseline of the insulin-suppressible (l₂) lipolysis rate was also significantly different between the colchicine and placebo groups (p=0.048). There was no statistically significant difference in the change of non-insulin-suppressible lipolysis rate (l₀) by treatment group (p=0.59). Changes in the distributions of SAT leukocyte populations were not significantly different between groups (p’s > 0.05) (Table 3).

Table 3: Pre- and post-treatment outcomes.

Data are reported as the unadjusted Mean ± SD except where otherwise indicated. l₀, non-insulin-suppressible lipolysis rate; l₂, insulin-suppressible lipolysis rate; l₀+l₂, maximal lipolysis rate; S_I, insulin sensitivity; AIR_G, acute insulin response to glucose; HOMA-IR, homeostatic model assessment of insulin resistance; Adipo-IR, adipose insulin resistance index; FFA, free fatty acids; hsCRP, high sensitivity C-reactive protein; WBC, white blood cell count; DXA, dual-energy x-ray absorptiometry; RFU, relative fluorescence units.

	Colchicine (n=18)		Placebo (n=18)		Interaction p-value
Variable	Pre	Post	Pre	Post
Body mass index (kg/m2)	39.3 ± 6.3	39.4 ± 6.6	41.8 ± 8.2	42.3 ± 7.9	0.11
DXA Body fat (%)	48.3 ± 4.0	48.6 ± 3.9	48.6 ± 5.8	48.8 ± 5.5	0.83
l0 (x10−3mEq/L*min−1)	3.80 ± 0.96	3.51 ± 0.10	3.80 ± 1.23	3.71 ± 1.18	0.59
l2 (x10−3mEq/L*min−1)	87.93 ± 63.27	76.86 ± 37.74	59.42 ± 31.92	87.70 ± 42.70	0.048
l0+l2 (x10−3mEq/L*min−1)	91.73 ± 63.0	80.37 ± 37.37	63.22 ± 31.77	91.41 ± 42.26	0.047
SAT Monocyte (%)#	34.3 ± 18.3	27.3 ± 13.9	33.1 ± 16.6	27.5 ± 8.7	0.87
SAT Neutrophil (%)#	21.4 ± 23.6	14.3 ± 14.9	19.7 ± 11.9	18.1 ± 14.9	0.44
SAT NK Cell (%)#	15.4 ± 13.5	13.6 ± 11.3	13.7 ± 13.2	9.6 ± 6.1	0.98
SAT mDC (%)#	1.2 ± 0.94	0.95 ± 0.78	1.5 ± 1.3	1.3 ± 1.1	0.77
SAT pDC (%)#	0.36 ± 0.49	0.25 ± 0.40	0.22 ± 0.20	0.43 ± 0.85	0.62
SAT M1 Macrophage (%)#	3.2 ± 2.0	3.5 ± 2.3	3.7 ± 1.4	4.2 ± 1.8	0.82
SAT M2 Macrophage (%)#	3.0 ± 1.8	1.1 ± 0.8	3.3 ± 2.9	1.6 ± 1.5	0.83
SAT M1/M2 Ratio#	1.5 ± 1.1	8.2 ± 12.3	1.7 ± 1.0	7.6 ± 8.7	0.68
SI (x10−5min−1mU/mL)	9.51 ± 3.86	9.86 ± 3.67	9.73 ± 4.48	9.95 ± 5.05	0.85
AIRG (mU/mL*min)	1,254 ± 647	1,194 ± 631	2,058 ± 3,144	1,765 ± 2,248	0.92
Fasting Glucose (mg/dL)	106.2 ± 9.9	104.3 ± 9.4	100.8 ± 7.3	103.9 ± 11.0	0.14
HOMA-IR	6.66 ± 3.00	6.48 ± 4.58	6.08 ± 2.76	7.44 ± 3.73	0.02
FFA (mEq/L)	0.76 ± 0.29	0.74 ± 0.22	0.78 ± 0.21	0.70 ± 0.21	0.26
Adipo-IR (mU*mEq/L)	17.9 ± 8.0	16.8 ± 9.8	18.5 ± 7.8	20.3 ± 10.9	0.32
Hemoglobin A1c (%)	5.64 ± 0.39	5.62 ± 0.44	5.49 ± 0.45	5.56 ± 0.41	0.40
hsCRP (mg/L)	7.51 ± 7.71	3.00 ± 1.85	6.45 ± 4.23	8.89 ± 8.30	<0.001
GlycA (mmol/L)	424.48 ± 62.30	393.01 ± 60.48	421.17 ± 40.60	439.67 ± 38.06	<0.001
WBC (x1000/μL)	7.31 ± 1.91	6.04 ± 1.45	6.36 ± 1.76	6.72 ± 1.67	<0.001
Whole Blood Neutrophils (x1000/μL)	4.41 ± 1.52	3.18 ± 1.02	3.46 ± 1.16	3.65 ± 1.07	<0.001
Whole Blood Monocytes (x1000/μL)	0.59 ± 0.19	0.51 ± 0.20	0.51 ± 0.16	0.57 ± 0.21	0.002
Whole Blood Lymphocytes (x1000/μL)	2.03 ± 0.52	2.10 ± 0.51	2.13 ± 0.53	2.24 ± 0.60	0.69
Whole Blood Neutrophil-Lymphocyte Ratio	2.24 ± 0.85	1.55 ± 0.47	1.63 ± 0.33	1.67 ± 0.50	<0.001†
Adiponectin (RFU)‡	2,244.0 ± 661.6	2,258.6 ± 547.7	2,253.9 ± 525.0	2,136.5 ± 520.2	0.12†
Leptin (RFU)‡	19,284.2 ± 5,689.4	18,489.5 ± 6,571.6	20,487.7 ± 7,441.2	22,408.4 ± 8,210.6	0.03
Soluble Leptin Receptor (RFU)‡	2,227.8 ± 473.7	2,392.8 ± 546.0	2,291.4 ± 656.3	2,267.2 ± 681.6	0.03
Resistin (RFU)‡	4,262.6 ± 945.4	3,479.4 ± 739.3	3,495.1 ± 832.4	3,616.1 ± 851.9	<0.001

^*DXA data were not available for all subjects (colchicine: n=13, placebo: n=14).

^‡Adipokine data were not available for one subject (colchicine: n=17, placebo n=18).

^#Adipose leukocyte data were not available for all subjects (colchicine: n=14, placebo: n=10). P-values shown calculated using repeated measures ANOVA interaction (group x time point).

^†non-parametric data used in analysis.

Figure 2: Effects of colchicine treatment on lipolysis rates.

Changes (Δ) in (A) non-insulin-suppressible (l₀), (B) insulin-suppressible (l₂), and (C) maximal (l₀+l₂) lipolysis rates after 3 months of study treatment in individuals randomized to colchicine (n=18) or placebo (n=18). Data are shown as Mean ± SEM.

Table S1 and Figure S1 show the correlation analyses of changes in lipolysis variables (l₂ and l₀+l₂) with changes in markers of systemic inflammation and metabolic parameters for all study participants. Changes in l₂ and l₀+l₂ were positively associated with changes in multiple biomarkers of systemic inflammation. Specifically, Δl₂ was positively associated with ΔhsCRP (r = +0.42; p = 0.01), ΔMonocytes (r = +0.46; p = 0.005), ΔNeutrophils (r = +0.45; p = 0.006), ΔNLR (r = +0.46; p = 0.005), and ΔWhite Blood Cell count (r = +0.40 ; p = 0.015). Δ l₀+l₂ was also positively associated with ΔhsCRP (r = +0.43; p = 0.009), ΔMonocytes (r = +0.46; p = 0.004), ΔNeutrophils (r = +0.45; p = 0.006), ΔNLR (r = +0.65; p = 0.0001), and ΔWhite Blood Cell count (r = +0.41; p = 0.014). Changes in l₂ and l₀+l₂ were positively associated with changes in resistin concentrations (r = +0.41; p = 0.013 and r = +0.42; p = 0.012, respectively) in the total cohort. Δl₂ and Δl₀+l₂ were not significantly associated with changes in other metabolic variables, adipokine concentrations, or SAT leukocyte populations. (Table S1). Figure S1 demonstrates the associations of Δl₂ with ΔhsCRP, ΔMonocytes, ΔNeutrophils, and ΔResistin in the total cohort.

Discussion

We examined the effects of colchicine treatment on measures of lipolysis and SAT leukocyte populations in adults with obesity and MetS. Although colchicine is well-studied as an anti-inflammatory agent, its effects on obesity-associated adipose metabolic dysfunction remain unclear. We report the novel finding that colchicine treatment significantly decreased insulin-suppressible (l₂) and maximal (l₀+l₂) lipolysis rates. Notably, the improvements in these lipolysis rates were strongly correlated with reductions in biomarkers of systemic inflammation, such as hsCRP, absolute neutrophil and monocyte count, the neutrophil-lymphocyte ratio, and total white blood cell count. The improvements in lipolysis rates were also significantly associated with reductions in resistin, a key immune-metabolic regulator secreted by macrophages and adipocytes that has been shown to induce insulin resistance [26]. The findings from the present study suggest that colchicine’s anti-inflammatory action may potentially exert beneficial metabolic effects in AT by improving insulin regulation of FFA release.

FFA homeostasis is defined as the balance of FFA appearance in circulation and FFA uptake. Lipolysis is the primary method by which stored triglycerides become mobilized to a circulating form (e.g. FFA) that can be utilized by other tissues. Insulin readily suppresses adipose triglyceride lipase (ATGL) and hormone-sensitive lipase (HSL), which are the main enzymes driving adipocyte triglyceride breakdown. However, even with complete suppression of ATGL and HSL, a minimal rate of FFA entry into the circulation is maintained, potentially in part due to continued action of monoacylglycerol lipase (MGL) [27, 28].

Excess adiposity is associated with dysregulated lipolysis and chronically elevated levels of circulating FFA, which in turn contributes to ectopic fat deposition, insulin resistance, and inflammation [29]. Infusion of palmitate, a saturated fatty acid, was found to directly promote NLRP3 inflammasome formation and IL-1β production in rodents fed a high-fat diet [30]. This inflammatory cascade is known to cause AT leukocyte recruitment, local destruction, and aberrant phosphorylation of adipocyte intracellular secondary messengers, further impairing adipocyte insulin action [31]. Moreover, experimentally increasing plasma FFA concentrations in vivo increases intramyocellular triglyceride content and promotes insulin resistance in skeletal muscle [32]. When examined longitudinally, adipocyte insulin resistance, as measured by Adipo-IR, is associated with progressive decline in beta cell function and the progression of T2D [33].

Taken together, the data suggest that insulin’s inability to suppress lipolysis in AT further drives obesity-associated inflammatory signaling, thereby promoting insulin resistance in skeletal muscle and impaired insulin secretion from the pancreas. Therefore, we expected that the improvements in insulin regulation of FFA release by colchicine would be associated with beneficial metabolic effects for peripheral tissues and for pancreatic insulin secretion. We previously described that colchicine significantly reduced HOMA-IR as compared to placebo [14]. However, in this secondary analysis we did not find any significant associations of improvements in insulin-suppressible (l₂) and maximum (l₀+l₂) lipolysis rates with improvements in other glucoregulatory variables, such as HOMA-IR or AIR_G. This may have been due to lack of power, short duration of study (three months), or potentially that colchicine’s effects on lipolysis are not directly linked to insulin resistance in non-adipose tissues.

Dysregulated lipolysis and elevated FFAs in obesity stimulate resident AT macrophage differentiation to a pro-inflammatory M1 phenotype [34, 35]. Because we previously found that colchicine significantly reduced circulating monocytes and neutrophils in individuals with obesity and MetS [14], in the current study, we hypothesized that colchicine would also have significant effects on the distribution of SAT leukocyte populations, particularly suppressing SAT neutrophil and M1 macrophage infiltration. Surprisingly, we did not find any effects by treatment group on any SAT leukocyte population in our analyses. Given the small sample size, the lack of effect could have been secondary to lack of power. Alternatively, we may have seen no effect due to the adipose depot studied. Although SAT represented about 95% of total fat mass in our cohort, VAT is more closely related to obesity-induced inflammation and has been shown to contain significantly more leukocytes and pro-inflammatory cytokines [36, 37]. It is plausible that colchicine may have significant salutary effects on leukocyte populations in VAT; unfortunately we were unable to obtain pre- post-treatment VAT samples in our study.

Moreover, SAT leukocyte populations were examined by cell surface markers by flow cytometry; therefore, intracellular functional parameters of the cells were not captured by this analysis. Because of colchicine’s beneficial effects on lipolysis, it is plausible that colchicine improved SAT leukocyte functionality without affecting cell surface markers. Functional parameters that are related to, for example, preadipocyte and macrophage plasticity, SAT leukocyte activity, and macrophage-specific gene expression in SAT were not examined but warrant further investigation. For this reason, it remains unclear whether colchicine’s effects on systemic inflammation, rather than local AT anti-inflammatory effects, were more consequential in improving insulin regulation of FFA in AT.

A strength of our study was a racially diverse cohort of adults with elevated inflammation and a broad range of ages and degrees of obesity. Another strength was the randomized, double-blinded placebo-controlled study design. A limitation of our study is the use of measures of FFA flux by mathematically modeling data from an insulin-modified FSIVGTT instead of using tracers during a hyperinsulinemic-euglycemic clamp. Additionally, our smaller sample size may have impaired our ability to uncover significant differences or associations, specifically for examining colchicine’s effects on SAT leukocyte populations. Also, we evaluated only the frequency, but not the functional activity, of SAT leukocyte populations. Another limitation was the use of SAT as the specimen in this study instead of VAT, which may play a more important role in the pathogenesis of obesity-induced inflammation and metabolic complications versus SAT. However, obtaining VAT in human subjects inherently carries with it much greater risk. While we assessed the correlations of insulin-suppressible (l₂) and maximal (l₀+l₂) lipolysis rates with circulating inflammatory biomarkers, due to the limited quantity of SAT tissue obtained in our study, we were unable to examine colchicine’s effects on inflammatory cytokines concentrations or NLRP3 activation within SAT itself. Furthermore, future studies are needed to assess colchicine’s effects on markers of lipolysis (e.g., phosphorylated HSL) or adipocyte insulin sensitivity (phosphorylated AKT) in AT. Finally, the cohort was over 70% female; though no significant sex-related differences in outcomes were found, larger studies are needed to determine if colchicine’s impact on lipolysis varies between men and women.

Conclusion

In adults with obesity and MetS, colchicine appears to improve insulin regulation of lipolysis and reduce markers of systemic inflammation independent of an effect on local leukocyte distribution in subcutaneous adipose tissue. Further studies are warranted to elucidate colchicine’s effects on adipose inflammation and function in human obesity.

Study Importance:

• Colchicine is a well-studied anti-inflammatory drug that has shown benefit in cardiometabolic disease; however, colchicine’s effects on adipose inflammation or function remain unclear.

• In adults with obesity and metabolic syndrome, colchicine appears to improve insulin regulation of lipolysis and reduce markers of systemic inflammation independent of an effect on local leukocyte distribution in subcutaneous adipose tissue.