Characterization of Gastrointestinal Hormone Dysfunction and Metabolic Pathophysiology in Experimental Spinal Cord Injury

Abstract

Cardiometabolic disease is a leading complication of spinal cord injury (SCI) that contributes to premature all-cause cardiovascular morbidity and early death. Despite widespread reports that cardioendocrine disorders are more prevalent in individuals with SCI than those without disability, a well-defined pathophysiology has not been established. Autonomic dysfunction accompanying disruption of autonomic spinal tracts may contribute to dysregulation of energy metabolism via uncoupling of integrated hunger and satiation signals. In governing human feeding behaviors, these signals are controlled by a network of enteroendocrine cells that line the gastrointestinal (GI) tract. These cells regulate GI peptide release and autonomic systems that maintain direct neuroendocrine communication between the GI tract and appetite circuitry of the hypothalamus and brainstem. Here we investigate gene-expression and physiological changes in GI peptides and hormones, as well as changes in physiological response to feeding, glucose and insulin challenge, and evaluate GI tissue cytoarchitecture after experimental SCI. Adult female mice (C57BL/6) were subjected to a severe SCI (65 kDyne) at T9, and a sham control group received laminectomy only. The SCI results in chronic elevation of fasting plasma glucose levels and an exaggerated glucose response after an oral glucose and insulin tolerance test. Mice with SCI also exhibit significant alteration in gut hormone genes, plasma levels, physiological response to prandial challenge, and cell loss and gross tissue damage in the gut. These findings demonstrate that SCI has widespread effects on the GI system contributing to component cardiometabolic disease risk factors and may inform future therapeutic and rehabilitation strategies in humans.

Introduction

Cardiovascular disease (CVD) and related neuroendocrine/metabolic disorders are a leading cause of morbidity and death in chronic traumatic spinal cord injury (SCI).^1

–4 Several component cardiometabolic risk factors, including obesity,^5,6 elevated fasting glucose and frank diabetes,^7,8 dyslipidemia,^3,9 and hypertension (depending on the extent and level of injury),^10
–12 are observed earlier in the life span and at increased frequencies after SCI.¹³

In particular, obesity is strikingly comorbid with glucose intolerance and insulin resistance,^14

–18 risk factors that are strongly associated with a “pro-inflammatory phenotype” that further accelerates atherogenesis¹⁶ and CVD.¹⁹ These hazards may explain, or provoke, premature pathology of the cardiovascular and neuroendocrine systems.²⁰

These cardiometabolic comorbidities after SCI are largely attributed to maladaptive metabolism related to physical deconditioning^19,21 and reduced whole-body energy expenditure, especially in injury above the spinal level (T6) of sympathetic nervous system outflow resulting in functional sympathectomy.^{15,19,22

–26} While these injury outcomes might serve as discernible explanations, they alone do not provide a unified pathophysiology explaining metabolic impairment accompanying SCI.

Recent investigations examining autonomic dysregulation after SCI^27,28 call into question whether autonomic imbalance, interrupted neurotransmission within the “gut-brain” axis, or neuroendocrine dysregulation after injury should be included as possible factors contributing to post-SCI metabolic disease risk factors.

What is described as the gut-brain axis involves a complexity of gastrointestinal (GI) signals, bidirectional communication between autonomic and neuroendocrine systems and modulatory feedback via higher order neural components, including cognitive and reward mechanisms.²⁹ Autonomic innervation to the GI tract includes the stomach, small and large intestine,³⁰ and targets the enteric nervous system (ENS), endocrine, and secretory cells.^30,31 The ENS, comprising distinct ganglionated plexuses,³² responds to nutrient intake and changes in luminal chemistry and mechanical distortion of the mucosa.^33

–36

Sympathetic reflexive actions involving enteric ganglia and splanchnic afferents,^37,38 also regulate motility, fluid exchange, and gut blood volume³⁹ in response to nutrient ingestion and absorption. Parasympathetic vagal afferents represent a direct link in gut-brain signaling⁴⁰ and relay information on the quality and quantity of ingested nutrients.⁴¹

Importantly, endocrine cell types secrete messenger molecules—GI peptides and hormones—into the GI tract,^33,42 which underlie both short- and long-term nutrient cycling and feeding behavior by transmitting nutrient-related gastric and intestinal signals.^43,44 For example, anorexigenic effects of the GI hormone ghrelin^45,46 and orexigenic effects of the GI hormones cholecystokinin (CCK),^44,47
–49 glucagon-like peptide-1 (GLP1),⁵⁰ and peptide tyrosine tyrosine (PYY)⁵¹ are mediated via autonomic ^{circuits52
–54} and direct effects in the central nervous system.^44,48,55,56

These, and other endocrine hormones including glucose-dependent insulinotropic polypeptide (GIP)^57,58 (also GI-released), the incretins insulin and amylin,⁵⁹ PP^60,61 (pancreas-released), and leptin⁶² (adipocyte-released) illustrate the robust mechanisms that contribute to energy metabolism regulation. Given the substantial importance of an integrated nervous system in nutrient cycling and feeding, it is reasonable to suspect that these functions are interrupted when the central and peripheral nervous systems are dissociated after SCI.

Although extensive research on autonomic dysfunction after SCI has focused on respiratory,⁶³ cardiac,⁶⁴ bowel,⁶⁵ and bladder⁶⁶ pathology, little is known about functional changes in the integrated signals and systems involved in the GI system as they relate to metabolism and component cardiometabolic disease risk factors.

In this study, we investigate differences in the physiological response to feeding, glucose and insulin challenge after experimental SCI compared with sham-control. We also examine gene expression and physiological changes in GI peptides and hormones and evaluate GI tissue cytoarchitecture. We demonstrate that after SCI, mice exhibit an exaggerated glucose response and insulin resistance, as well as a significantly altered gene expression profile and plasma levels of key GI peptides. In addition, we provide evidence of damaged autonomic innervation to the GI tract.

These novel findings suggest that impairment in enteroendocrine neurobiology and GI damage, in principle, are mechanisms that may contribute to impaired energy metabolism and component risks for cardiometabolic disease.

Methods

Experimental animals and grouping

All animal protocols were approved by the University of Miami Institutional Animal Care and Use Committee and are in accordance with National Research Council guidelines for the care and use of laboratory animals. Animals were group (socially) housed in a temperature- and humidity-controlled rodent vivarium, maintained on a reverse 12-h light/dark cycle, and given food and water ad libitum. No additional environmental enrichment was provided because of its reported effects on the nervous system, including neurogenesis and levels of inflammation, which may affect experimental outcomes. Animals were acclimated for seven days before study experiments, which included being handled daily to normalize human contact and minimize distress.

SCI and sham surgeries

As we described previously,^67

–70 contusion injury was induced with the Infinite Horizon Impactor device adapted to mice. The Infinite Horizon impactor device has been established in producing precise, graded contusion, with reproducible lesion volume and functional outcomes assessed using Basso, Beattie, Bresnahan (BBB) and Basso Mouse Scale (BMS)⁷¹ open-field locomotor rating scales.⁷²

In brief, adult female C57Bl/6 mice (12-weeks old, ∼20 g; Jackson Laboratories) were anesthetized with an intraperitoneal (ip) injection of ketamine (80 ± 100 mg/kg) and xylazine (10 mg/kg). Complete anesthetization was determined by the lack of a stereotypical retraction of the hindpaw in response to a nociceptive stimulus. Mice then underwent a laminectomy at vertebrae T9, and the exposed spinal cord was injured at a predetermined impact force of 70 kdynes (severe injury). Sham-operated (control) animals underwent all surgical procedures, including laminectomy, but their spinal cords were not injured.

After surgical procedures, animals (both SCI and sham) were housed and treated with subcutaneous lactated Ringer solution to prevent dehydration, buprenorphine (0.1 mg/kg) bid for three days post-surgery and prn thereafter; the prophylactic antibiotic gentamicin was administered daily for seven days to prevent urinary tract infections. Manual bladder expression was performed twice daily, and simultaneously examined for post-stress health status by observation of activity level, respiratory rate, and general physical condition.

Body weight was monitored daily for the first week post-surgery and every other day thereafter. Excessive weight loss (>20%) and decreased grooming behavior were considered as criteria for early exclusion from the study and/or euthanasia. Physical conditions such as moribund state, dehydration, and anorexia were also considered as criteria for early termination.

Euthanasia was performed by carbon dioxide inhalation according to the recommendations for euthanasia detailed in the 2007 Report of the American Veterinary Medical Association's Panel on Euthanasia. Euthanasia was performed in a manner to avoid animal distress. After animals were placed inside the chamber, CO₂ flow was gradually introduced and slowly increased until animals were in a deep state of sleep after 1 min, followed by an additional 4 min of increased CO² flow. After CO² exposure, confirmation of termination was accomplished by cervical dislocation.

Grouping

Sixty-four mice were randomized to independent experimental groups that either underwent contusion injury or sham procedure. A subset of animals (n = 8) from each group was sacrificed at 1-, 2-, 4- or 8-weeks post-surgical procedure. We note that only female mice were used in our study, because female neurogenic bladders after experimental SCI are more safely “expressed” than those of males.⁷³

Body mass

Body mass discriminated to 0.1 g was measured on a calibrated analytic balance (Data Weighing Systems) at 12 weeks of age (baseline: before survival surgery); 1-, 2-, 4- and 8-weeks post-surgery (before necropsy).

Tissue collection

The small intestines of mice were harvested after euthanasia in accordance with approved euthanasia procedures described above. In brief, the duodenum was dissected according to anatomical features, from the proximal limit— ∼0.5 cm distal to the pylorus—to the distal limit of the ligament of Treitz (∼4 cm in length). The luminal content was thoroughly and rapidly flushed with ice-cold phosphate-buffered saline (PBS), and the tissue was immediately either snap-frozen in liquid nitrogen and stored at -80°C until the time of assay, or post-fixed in 4% paraformaldehyde in PBS, then transferred to 20% sucrose in 0.1 M PBS until sectioned.

Total ribonucleic acid (RNA) isolation and quantitative real-time reverse transcription-polymerase chain reaction (RT-PCR)

Total RNA was isolated from mouse gut (duodenum) using TRIzol Reagent (Invitrogen) according to manufacturer's instructions. 1–10 μg of RNA were reverse transcribed using SuperScript III First Strand Synthesis System for RT-PCR (Invitrogen). Real-time PCR was performed with the Applied Biosystems 7300 Real-time PCR System on samples amplified with Rotor-Gene SYBR Green PCR Kit (Qiagen) and primers for ghrelin, CCK1, GLP1, and PYY (Table 1). Relative expression (as fold change) of target genes was calculated after normalization to beta-actin using the 2^-ΔΔCt method.

Table 1.

Gene Primers for Quantitative Reverse Transcription-Polymerase Chain Reaction

Gene	Genebank #	Primer pairs	Tm/Product
Ghrelin	NM_021488	Forward: 5' - GAAGCCACCAGCTAAACTGCAG	67/558
		Reverse: 5' - CTGACAGCTTGATGCCAACATCG
CCK1	NM_031161	Forward: 5' - TAGCGCGATACATCCAGCAGGT	69.7/707
		Reverse: 5' - GGTATTCGTAGTCCTCGGCACT
GLP1	NM_008100	Forward: 5' - CCTTCAAGACACAGAGGAGAACC	65.2/1112
		Reverse: 5' - CTGTAGTCGCTGGTGAATGTGC
PYY	NM_145435	Forward: 5' - GCCACTACCTCAACCTGGTCAC	66.9/511
		Reverse: 5' - TCGCTGTCGTCTGTGAAGAGCA

Plasma

Mice were euthanized as described above and immediately exsanguinated by cardiac puncture using a heparinized needle. Samples were transferred to an ethylenediaminetetraacetic acid-coated tube and centrifuged (5000 × g, 15 min). Plasma was collected either following a 4- or 6-hour fast or ∼30 min postprandial, aliquoted, and stored at -80˚C until assay. Fasting plasma glucose levels were assayed by enzymatic methods (Roche Diagnostics, Indianapolis, IN). Gut hormones were analyzed using a multiplex assay consisting of a panel of 14 hormone biomarkers involved in metabolic response (Milliplex MAP Mouse Metabolic Magnetic Bead Panel; EMD Millipore, Danvers, MA).

Immunohistochemistry

The mouse gut (duodenum) was post-fixed in 4% paraformaldehyde solution as described above. The distal half (∼2 cm) of the tissue was embedded in O.C.T. Compound (Tissue Tek^®) and processed by cryostat sectioning (Leica SM 2000R sliding microtome). Serial cross-sections (50 μm) were stored in free-floating cryostat media (30% ethylene glycol, 30% sucrose, 0.1 M PBS, pH 7.4) at -20°C then rinsed with 0.1 M PBS (pH 7.4). Tissue sections were blocked/permeabilized by treatment with 5% normal goat serum (Vector Laboratories Inc., Burlingame, CA) and 0.4% Triton X-100 (Sigma).

Sections were incubated for 48 h at 4°C with either tyrosine hydroxylase (TH) (1:1000; Abcam ab112) or neurofilament (NeuF) (1:500; Abcam ab8135) primary antibodies. Primary antibody binding was detected with Alexa Fluor secondary antibody conjugates (1:500, Molecular Probes, Eugene, OR). Controls lacking the primary antibody were run in parallel. Sections were counterstained with 4',6-diamino-2-phenylindole (DAPI), and cover-slipped with Vectashield mounting medium (Vector Laboratories Inc.).

Images of duodenum cross-sections were captured at 10X magnification with an Axiovert 200M Inverted Microscope (Zeiss; Göttingen, Germany), using consistent image acquisition settings (intensity, exposure time, numerical aperture). Quantification of staining was determined by mean fluorescence intensity (MFI) analysis of labeled proteins within area region of interest (ROI) using Image J 1.53m software similar to previous reports.^74

–77 Notably, MFI was measured using equivalent selected areas (pixels squared) within an ROI for a given tissue section and subsequently corrected for background fluorescence.

Metabolic testing

Postprandial challenge

After a 6-h fast,⁷⁸ mice were given normal chow ad libitum, at which point they began feeding immediately. Mice were allowed to eat for ∼10 min, after which they typically stopped eating, and the food was removed. At ∼30 minutes postprandially, plasma was collected as described above.

ip Glucose tolerance test (ipGTT)

This test was performed after a 6-h fast. Initial blood glucose levels were determined in unanesthetized mice, followed by ip injection of glucose (1.5 g/kg body weight). Blood glucose levels were measured from the tail vein at 15, 30, 60, 90, and 120 min after the glucose injection using an Accu-chek Advantage glucometer (Roche Diagnostics, Indianapolis, IN). The area under the curve during the ipGTT was calculated by the trapezoidal method with baseline correction for each animal to generate the area of the curve (AOC).⁷⁹

ip Insulin tolerance test (ipITT)

This test was performed after a 4-h fast.⁸⁰ Initial blood glucose levels were determined, followed by ip injection of human insulin (1.0 U/kg; Humulin R; Eli Lilly, Indianapolis, IN). Blood glucose levels were measured from tail vein blood at 15, 30, 60, 90, and 120 min after the insulin injection, and AOC was calculated as described above.

BMS for locomotor analysis

The BMS test of open-field locomotion was performed on experimental mice in an odor-free, non-transparent square arena as described previously.⁸¹ The arena was divided into three zones (wall, inter, and center), and mouse behavior was recorded over a 5-min period using a high resolution video camera. The total number of lines crossed, time spent in each zone, and stereotypical behaviors such as grooming and rearing were analyzed and expressed as the number of events. Mice that did not enter all three zones or cross a minimum of 50 lines during the 5-min trial were excluded from analysis.

Scoring of the locomotor hindlimb performance in the open field with the BMS is a 0–9 rating system based on a modification of the BBB and is specifically designed for the mouse.⁷¹ A team of two blinded investigators evaluated the mice over a 5-min period one day after surgery and weekly after that (data are summarized in Table 2).

Table 2.

Basso Mouse Scale Scores Plus Standard Error of the Mean

BMS Scores ^+/- SEM
Time Group	Sham	SCI
Post-Surgery:
[Baseline]	9 ^+/-0	9 ^+/-0
1-Day	9 ^+/-0	0 ^+/-0
1-Week	9 ^+/-0	2.77 ^+/-0.101
2-Weeks	9 ^+/-0	4.21 ^+/-0.11
4-Weeks	9 ^+/-0	4.55 ^+/-0.08
8-Weeks	9 ^+/-0	4.58 ^+/-0.102

SCI, spinal cord injury.

Statistical analysis

Group comparisons

Experimental groups are independent across time. Between group differences were analyzed using an unpaired t test, except for gene (mRNA) expression analyses that were performed using one-way analysis of variance, followed by Tukey post hoc comparison (GraphPad, Prism v9.3.1) and reflect fold change from sham control. Data are expressed as mean ± standard error of the mean. A significance level of p < 0.05 was accepted as different from sham control.

Exclusion criteria

The criteria were post-surgery mortality—primarily caused by the severity of the injury, most notably from cardiorespiratory failure or sepsis because of neurogenic bladder—or morbidity, including abnormal/unexpected weight loss and physical signs of distress, SCI-impactor variance (desired vs. actual), and experimental/procedural error. For each group, n = 4–8 depending on quality of sample analysis and/or survival, where last observation carryforward technique was used. Number of samples (n) are shown in figure legends for all results reported.

We note that our exclusion criteria may introduce attrition bias. Given that this is a pre-clinical study, however, per protocol analysis is particularly useful for interpreting the effects of group differences when taken in an optimal manner—i.e., in conjunction with randomization, ensuring groups being compared have similar characteristics.

Correlation analysis

Correlation analysis was performed to evaluate whether specific outcome measures have significant collinearity, quantified by the Pearson r correlation coefficient (R Studio v1.4.1106). Pairwise correlation coefficients between outcome variables are presented in a correlation matrix, and the value of the correlation range from -1 to +1, where +/-1 describes a perfect positive/negative correlation and 0 describes no correlation.

R corrplot function was used to (i) run hierarchical clustering to identify structure and pattern in the matrix and (ii) graph the correlation matrix (upper triangular). Positive correlations are displayed in blue and negative correlations in red, and color intensity is proportional to the correlation coefficients. The p values of correlations were computed (p < 0.05), and insignificant correlation coefficient values are left blank (statistics are summarized in supporting Fig. 1).

FIG. 1.

Analysis and comparison of body mass and plasma lipids across time in spinal cord injury (SCI) and sham mice. (A) The SCI significantly reduces body mass at 1- and 2-week time points and recovers at 4 and 8 weeks when compared with sham. (B) Fasting glucose is significantly decreased 1 week with SCI, then is significantly increased at 2, 4, and 8 weeks when compared with sham. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. 1 week: Sham, n = 7; SCI, n = 7. 2 weeks: Sham, n = 6; SCI, n = 6. 4 weeks: Sham, n = 6; SCI, n = 6. 8 weeks: Sham, n = 8; SCI, n = 8.

Results

Body mass and fasting glucose

Mean group differences for body mass and fasting plasma glucose are summarized in Table 3. Body mass was measured before surgical procedure to confirm that there were no differences in randomization. Group comparisons between SCI and sham mice were made at acute (1- and 2-week) and chronic (4- and 8-week) time points post-surgery. Acutely post-SCI, there was a significant decrease in body mass at both 1 and 2 weeks when compared with sham. SCI mice recovered body mass at chronic time points, no longer being statistically different than sham at both 4 and 8 weeks (Fig. 1A).

Table 3.

Mean Values of Body Mass and Fasting Glucose Across Time

Body mass (g) ^+/- SEM
Time Group	Sham	SCI
Post-SCI:
1-week	20.2 ^+/-0.69	16.1 ^+/-0.49^****
2-weeks	22.01 ^+/-1.08	19.4 ^+/-0.96^**
4-weeks	22.9 ^+/-1.37	21.8 ^+/-1.2
8-weeks	23.11 ^+/-0.11	22.93 ^+/-0.23

Fasting glucose (mg/dL) ^+/- SEM
Time Group	Sham	SCI
Post-SCI:
1-week	183 ^+/-29.46	138.42 ^+/-24.9^*
2-weeks	143.5 ^+/-13.85	159.5 ^+/-9.04^*
4-weeks	125.67 ^+/-12.32	159.5 ^+/-10.3^***
8-weeks	139.25 ^+/-23.23	171.5 ^+/-27.89^*

SCI, spinal cord injury.

Values are mean ± standard error of the mean (SEM).^* p < 0.05, ^** p < 0.01, ^*** p < 0.001, ^**** p < 0.0001.

Fasting plasma glucose was significantly reduced 1-week post-SCI compared with sham. Notably, there was a rise in fasting glucose in the SCI group that was significantly greater than time-matched sham at 2, 4, and 8 weeks (Fig. 1B). These data confirm previously demonstrated^69,70 acute body mass reduction with SCI, which is recovered chronically, and importantly, demonstrate a significant increase in fasting plasma glucose beginning acutely and sustained in chronic SCI.

ip Glucose and insulin tolerance test

The ipGTT and ipITT were performed in experimental groups at 1-, 2-, 4-, and 8-weeks post-surgery. In the ipGTT, plasma glucose increased to a maximum after 15 min of glucose injection in both groups, and this was observed at all time points post-surgery (Fig. 2). At 1-week post-injury, SCI mice exhibited decreased serum glucose levels at all time points measured after ip glucose injection, resulting in a significantly reduced AOC compared with sham (Fig. 2A) and indicative of greater insulin sensitivity in SCI compared with sham.

FIG. 2.

Intraperitoneal glucose tolerance test (ipGTT) and corresponding area of the curve (AOC) across time in spinal cord injury (SCI) and sham mice. (A) The glucose tolerance curve at 1 week illustrates that glucose levels are lower after ipGTT at all time points (left graph) and corresponding AOC is significantly reduced in SCI compared with sham (right graph). (B–D) The glucose tolerance curve at 2, 4, and 8 weeks illustrates that glucose levels are higher after ipGTT at all time points (left graph) and corresponding AOC is significantly greater in SCI at 2 and 4 weeks (B,C), but not significantly different at 8 weeks (D) compared with sham (right graph). *p < 0.05. 1 week: Sham, n = 7; SCI, n = 7. 2 weeks: Sham, n = 6; SCI, n = 6 . 4 weeks: Sham, n = 6; SCI, n = 6. 8 weeks: Sham, n = 8; SCI, n = 8.

Notably, at 2-, 4-, and 8-weeks post-injury, SCI mice exhibited insulin resistance compared with SCI evidenced by the increased serum glucose levels at all time points after ip glucose injection, resulting in a significantly greater AOC compared with sham at 2 and 4 weeks, although not significantly different by 8 weeks (Fig, 2B-D).

In the ipITT, a rapid decline in plasma glucose after 15 min of insulin administration was observed in the sham group, and this was observed at all time points post-surgery. The SCI mice, however, exhibited a markedly attenuated plasma glucose response at 15 min after insulin administration, observed at all time points post-SCI (Fig. 3). In addition, SCI mice exhibited increased serum glucose levels at all time points after ip insulin administration, resulting in a significantly greater AOC compared with sham at 2, 4, and 8 weeks (Fig. 3A–D). Taken together, these results indicate characteristic features of glucose intolerance and insulin resistance after SCI.

FIG. 3.

Intraperitoneal insulin tolerance test (ipITT) and corresponding area of the curve (AOC) across time in spinal cord injury (SCI) and sham mice. (A) The insulin tolerance curve at 1 week illustrates that glucose levels drop rapidly in sham and have a delayed response in SCI at 15 min after ipITT. Glucose levels at all time points thereafter are higher in SCI compared with sham (left graph), and corresponding AOC are not significantly different (right graph). (B–D) The insulin tolerance curve at 2, 4, and 8 weeks illustrates that glucose response remains delayed in SCI at 15 min after ipITT, remain higher at all time points thereafter, and corresponding AOC are significantly greater when compared with sham (right graph). **p < 0.01, ****p < 0.0001. 1 week: Sham, n = 7; SCI, n = 7. 2 weeks: Sham, n = 6; SCI, n = 6. 4 weeks: Sham, n = 6; SCI, n = 6. 8 weeks: Sham, n = 8; SCI, n = 8.

Gut hormone mRNA expression

To investigate long-term gene-expression changes in duodenum extracts from experimental groups, we examined mRNA expression levels of the gut hormones ghrelin, CCK, GLP1, and PYY, at 4- and 8-weeks post-injury (Fig. 4). Using quantitative RT-PCR, significant differences in mRNA expression were observed in all genes examined when comparing SCI with sham. Ghrelin, CCK, and PYY mRNA were all significantly reduced at 4 weeks in SCI mice compared with sham.

FIG. 4.

Messenger ribonucleic acid (mRNA) analysis of ghrelin, cholecystokinin (CCK), glucagon-like peptide-1 (GLP1), and PYY at chronic time points after spinal cord injury (SCI) and in sham mice. The mRNA expression of ghrelin, CCK, and peptide tyrosine tyrosin (PYY) were significantly reduced at 4 weeks post-SCI but no longer statistically different at 8 weeks post-SCI compared with sham (A,B,D). The mRNA expression of GLP1 was not statistically different at 4 weeks post-SCI but was significantly greater by 8 weeks post-SCI compared with sham (C). *p < 0.05, **p < 0.01, ***p < 0.001. n = 4 per experimental group.

The CCK and PYY mRNA remained significantly reduced at 8-weeks post-SCI, although ghrelin mRNA was no longer significant (Fig. 4A,B,D). Conversely, a non-significant increase in GLP1 mRNA expression was observed at 4-weeks post-SCI, which became significantly greater by 8-weeks post-SCI compared with sham (Fig. 4C). These results provide evidence that several key gut hormone genes associated with hunger, satiation, and metabolic regulation are significantly altered in the duodenum after chronic SCI.

Fasted and postprandial plasma levels of gut, pancreas, and adipocyte hormones

Plasma was analyzed under fasted and postprandial conditions as described for experimental groups at 8-weeks post-surgery. Specifically, we examined GI, pancreas, and adipocyte hormones that have an important role in the physiological response to feeding, hunger, and satiety. Importantly, maladaptive regulation of these hormones contributes to cardiometabolic risk factors such as insulin resistance, glucose intolerance, dyslipidemia, and obesity.

Of the 14 plasma gut hormones measured, nine provided consistent results within the detectable ranges across all groups. Group comparisons between SCI and sham mice were made for the following analytes: ghrelin_(active), CCK, GLP1_(active), PYY_(total), GIP_(total), insulin, amylin_(active), PP, and leptin (Fig. 5). In the fasted state, significant group differences at 8 weeks were observed for all analytes except PP. Notably, in SCI mice, plasma levels of ghrelin were significantly reduced (Fig. 5A), whereas CCK, GLP1, PYY, GIP, insulin, amylin, and leptin levels were all significantly increased (Fig. 5B-F,H,I) when compared with sham.

FIG. 5.

Analysis and comparison of plasma gut hormone levels in fasted and fed states at 8 weeks after SCI and in sham mice. Fasted plasma levels of ghrelin were significantly reduced (A), cholecystokinin (CCK), glucagon-like peptide-1 (GLP1), GYY, glucose-dependent insulinotropic polypeptide (GIP), insulin, amylin, and leptin were all significantly greater (B–G,I), and PP (H) was not statistically different in SCI compared with sham. Postprandial plasma levels of ghrelin (A), GLP1 (C), GIP, insulin, amylin (E-G), and leptin (I) were all significantly greater, and CCK (B), PYY (D), and PP (H) were all significantly less in SCI compared with sham. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. n = 8 per experimental group (some data points lost because of quality of sample analysis).

After postprandial challenge, significant group differences at 8 weeks were observed for all analytes examined. In SCI mice, plasma levels of ghrelin, GLP1, GIP, insulin, amylin, and leptin were significantly increased (Fig. 5A,C–F,I), whereas CCK, PYY, and PP (Fig. 5B,G,H) were significantly decreased when compared with sham. These data provide evidence for a strong injury effect on several important gut hormones, alterations that may disrupt physiological responses to feeding and contribute to aberrant energy homeostasis and metabolism.

Duodenal immunohistochemistry and cytoarchitecture

The GI tract is innervated, in part, by prevertebral ganglia that supply an extensive network of noradrenergic fibers to the smooth muscle wall, enteric ganglia, and arteries,³¹ all of which may be compromised after SCI. TH—the rate-limiting enzyme in the biosynthesis of noradrenaline—and NeuF—a major cytoskeletal element in neurons found in enteric ganglia—were used to selectively visualize innervation of the duodenum at 8-weeks post-surgery.

Here we illustrate widespread distribution and expression of positively immunostained DAPI (blue, nuclear counterstain), TH (green), and NeuF (red) in sham mice (Fig. 6A–C). The overlay and higher magnification images (Fig. 6D, I) show marked areas of colocalization of these neuronal markers (arrows, inset) and overall, demonstrate intact GI cytoarchitecture (Fig. 6A–D). After SCI DAPI, TH and NeuF immunoreactivity was markedly reduced (Fig. 6E–G), and extensive GI damage is evident (Fig. 6E–H, J) compared with sham. Semi-quantitative analysis of immunoreactivity by MFI (Fig. 6K) indicate a significant reduction in DAPI, TH, and NeuF immunostaining after SCI compared with sham, indicating neuronal damage and loss in the duodenum after injury.

FIG. 6.

Immunofluorescent images of tyrosine hydroxylase (TH) and neurofilament (NeuF) in the duodenum at 8 weeks after spinal cord injury (SCI) and in sham mice. Mouse duodenum cross sections (50 μm) were immunostained with TH (green), NeuF (red), as neuronal markers, and counterstained using 4',6-diamino-2-phenylindole (DAPI) (blue). Mean fluorescence intensity of immunoreactivity is significantly reduced in SCI for DAPI (A,E,J), TH (B,F,J), and NeuF (C,G,J) compared with sham. In sham, TH and NeuF are localized to the same regions in DAPI positive cells and along the duodenal wall (D,I_arrows ) and in SCI this immunostaining and localization is lost (H). Scale bars = 100 μM. *p < 0.05, **p < 0.01, ***p < 0.001. n = 6 per experimental group. (TH) (1:1000; Abcam ab112) or neurofilament (NeuF)

Discussion

This study demonstrates that SCI results in chronic insulin resistance demonstrated by the elevation of fasting plasma glucose levels, an exaggerated glucose response to both glucose and insulin challenge, and increased fasting and postprandial insulin levels. We also show SCI-induced alteration in several gut hormone genes, their plasma levels, and physiological response to prandial challenge. Further, we identify significant cell loss and gross tissue damage in the duodenum of the gut after SCI and concomitant decreases in the expression of key neuronal markers. These findings provide evidence for impaired metabolism and cardiometabolic disease risk factors and suggest duodenal dysregulation via SCI-induced interruption of autonomic neurotransmission.

It is evident that SCI contributes to the development of cardiometabolic risk factors and disease pathology. The long-term effects of SCI are linked to obesity, insulin resistance, and glucose intolerance, although human trials demonstrating these outcomes may be complicated by factors such as familial risks, pre-morbid health, socioeconomic status, and lifestyle (i.e., diet and exercise). We note that many cardiometabolic risk factors reflect observations reported in men⁸² because they represent the majority of cases of SCI.⁸³

Although our study included exclusively female mice, the disparities in prevalence rates between male and female reflect differences in behavior, activity, and lifestyle factors⁸⁴ that do not necessarily reflect biological differences in sex. Our model circumvents such confounds that often elude experimental control. Moreover, there has been a gradual increase in the population of women with SCI⁸⁵ and an accompanying “call to action” to improve knowledge gaps to facilitate research and interpretations of findings.⁸² Notwithstanding, we acknowledge that future studies should include sex-specific comparison.

In the current study, body mass at 1- and 2-weeks post-SCI is 20.4% and 11.9% less, respectively, and recovers by 4 and 8 weeks to the extent that any differences between groups is no longer discernable. Although seemingly contradictory to the chronic weight gain observed in humans with SCI, the rapid loss of body mass reflects acute-phase level-of-injury dependent hypokinesis (loss of muscle-mass^{86

–94} and bone^{95

–101}), which we have shown in our model.^69,70,102 Further, compared with able-bodied controls, individuals with SCI exhibit infiltration of intramuscular fat,^{94,103
–105} reported along with reduced overall lean mass.¹⁰²

These pathological features are evidence of an overall shift to an obesogenic body habitus, which may be a better indicator of cardiometabolic risk than mass standalone. We also demonstrate that fasting plasma glucose after SCI is 11%, 26.9%, and 23.15% greater at 2, 4, and 8 weeks, respectively. It is likely that sublesional muscle atrophy and impaired muscle oxidative phenotype promote increased circulating plasma glucose, which we show here (Fig. 1B) and indicate higher risk for type II diabetes.^106,107

Although fasting glucose at 1-week post-SCI was significantly less than time matched control, this likely reflects the substantial loss of total body mass acutely. We also note that fasting glucose in the sham “1-week cohort” appears uncommonly high, both in our experience and in the literature.⁷⁹ As such, it could be inferred that fasting glucose in sham at 2 and 4 weeks is reduced; however, the “independent group” study design limits repeated measures analysis and inference.

As an additional point, we cannot rule out that an acute stress-response—or stress hyperglycemia¹⁰⁸—to sham-operation may account for the elevated fasting glucose in sham at 1-week post-SCI. Although we have shown that sham and naïve mice have no significant differences in certain biological outcomes in chronic SCI,⁶⁸ we have not directly examined stress and glucose between these conditions at a time point as early as 1-week post-SCI. Nevertheless, the chronic effects of SCI demonstrate impaired fasting glucose, which promotes cardiometabolic risk and reflects what is observed in chronic SCI in the human condition.

Our current understanding suggests that fasting glucose alone may fail to provide a diagnosis in a large proportion of people with diabetes, to the extent that GTT is recommended by the World Health Organization¹⁰⁹ to identify people with impaired glucose tolerance. The time course of absolute plasma glucose concentration after administration of glucose is well characterized as the “tolerance curve” and frequently used clinically and experimentally as an index of efficiency of the mechanisms regulating the concentration of glucose in the blood.^78,110 Our data demonstrate that glucose AOC after ipGTT is significantly greater after SCI as early as 2-weeks post-SCI, which persists through 8 weeks compared with sham. These results are consistent with an exaggerated postprandial glycemia observed in humans with chronic SCI.^111,112

An exaggerated glucose AOC is used widely to diagnose impaired glucose tolerance, which our results demonstrate both acutely and chronically post-SCI. Notably, although glucose AOC is no longer statistically greater by 8-weeks post-SCI, this may represent an adaptation to locomotor recovery with respect to the injury model and strain, which peaks (∼2-weeks post-SCI) and plateaus thereafter at ∼4-weeks post-SCI.⁷¹

Similarly, the ITT is a standard test of insulin resistance, which determines whole body sensitivity of insulin receptors by measuring glucose level changes before and after insulin administration. We demonstrate a significantly greater AOC after ipITT following SCI, compared with time matched sham, beginning at 2 weeks post-SCI and remaining chronically at 4 and 8 weeks. Importantly, in all SCI groups, we observe a delayed glucose response at 15 min after insulin injection, indicating a thwarted ability to lower glucose levels compared with sham. Spinal cord injury has been reported as an independent risk factor for type II diabetes,^113,114 and these results support previous studies implying a role for insulin resistance in the development of extant disease.^115
–117

Although we have discussed several valid explanations for maladaptive metabolism associated with SCI, these findings taken together likely do not provide a complete pathophysiological model. Metabolic regulation requires expansive biological process, including GI signal integration, involving intrinsic (enteric) and extrinsic (sympathetic/parasympathetic) components. Importantly, enteric reflexes, reflexes involving sympathetic ganglia, and reflexes that directly pass from the gut through the CNS provide physiological feedback to hunger, satiation, and nutrient intake, contributing to regulation of energy balance and metabolism.³²

In fact, the GI tract is regarded as the largest endocrine organ of the body, expressing greater than 30 gut hormone genes and 100 bioactive peptides involved in the initiation and maintenance of food intake as well as termination of meals.^61,118 Notably, acylated ghrelin—originating primarily from the stomach and duodenum¹¹⁹—is the only known orexigenic gut hormone, stimulating appetite^45,46 via endocrine mechanisms that act on hypothalamic and brainstem centers.^44,48

Conversely, intestinal CCK suppresses food intake in response to luminal lipid and protein,^44,47
–49 and the incretin hormone GLP1 exerts sustained satiation^{48,49,120,121} in response to glucose,⁵⁰ both of which exert their effects on hypothalamic and brainstem regions.^{48,49,56,120,121} Satiety effects of PYY via vagal afferents are also thought to involve hypothalamic neural pathways, highlighting these hunger and satiation peptides in the central (CNS) integration and regulation of feeding behavior.

Currently, there is a paucity of literature regarding GI neuraxis dysfunction in SCI, especially relating to metabolic dysfunction. To our knowledge, our data show for the first time that chronic SCI induces a significant reduction in ghrelin, CCK, and PYY gene expression at 4-weeks post-SCI, and although no longer statistically significant after 8 weeks, each is still observably reduced compared with sham. Conversely, we show GLP1 gene expression is observably greater after 4 weeks, and this trend is significantly greater at 8-weeks post-SCI compared with sham. These data indicate that key gut hormone genes associated with feeding responses and behaviors are significantly dysregulated after SCI.

Germane to genetic dysregulation is the physiological response of gut hormone analytes to nutrient intake. Ghrelin, which drives the sensation of hunger, has peak plasma levels when fasted¹²² and is rapidly reduced to nadir levels by ∼30 min postprandially.¹²³ Conversely, CCK, GLP1, and PYY are all released to drive satiety, with peak levels observed at ∼30^123
–125 (CCK and GLP1) and ∼60¹²³ (PYY) min postprandially.

The incretin hormone GIP augments nutrient-induced insulin secretion, contributing equally to the effect as GLP1.^57,58 Insulin and amylin are postprandially cosecreted from pancreatic β-cells in a 20:1 ratio,^59,126,127 where insulin plays a major role in carbohydrate, fat, and protein metabolism and amylin functions as a synergistic partner in glucose regulation.

In addition, long-term anorexigenic effects have been demonstrated by the hormones PP^60,61 and leptin⁶² arising from pancreatic islet and adipocytes, respectively, both acting via hypothalamic and brainstem regions.^61,62 Circulating PP concentrations rise after a meal in proportion to caloric load,^60,61 and leptin response is significantly correlated with insulin response.¹²⁸ Here, we show significant group differences between SCI and sham for each of these serum analytes in both the fasted and fed state, except PP, which was not significantly different between groups in the fasted state.

Importantly, several of these changes are consistent with observations reported previously in cardiometabolic disorders. For example, differences in the fasted to feeding response show that ghrelin levels were lower in SCI in the fasted state and less effectively supressed after feeding. Our results indicating significantly lower fasted plasma ghrelin have been reported in obese individuals and are significantly related to insulin resistance.¹²⁹ Moreover, we show that postprandial reduction of circulating ghrelin levels is thwarted in SCI compared with sham, also previously demonstrated in response to nutrient intake in humans with obesity.¹³⁰

The PYY and CCK remained markedly lower in SCI postprandially compared with sham, and similarly reduced meal effects are reported in obesity.^131

–135 We also demonstrated an increased fasted and postprandial leptin and postprandial GIP secretion with SCI. Elevated circulating leptin is a hallmark biomarker of obesity¹³⁵ and some studies suggest GIP hypersecretion in people with obesity.^136,137

Importantly, insulin and amylin are both significantly increased in fasted and fed states with SCI. Chronically elevated insulin levels (both fasting and postprandially) are indicative of insulin resistance as shown in the ipGTT and ipITT results. Hyperinsulinemia is understood to participate in the metabolic dysregulation observed in obesity and type II diabetes,^138
–140 and amylin is shown to be elevated in obesity, believed to lead to the down-regulation of amylin receptors and lessen the impact of postprandial amylin secretion on satiety and gastric emptying.^136.141

Notably, we also show increased circulating fasting and postprandial GLP1 with SCI compared with sham, although these results are inconsistent with respect to previous literature examining cardiometabolic risk. A recent report suggests that fasting plasma GLP1 is increased proportionally with the number of associated metabolic risk factors¹⁴²; however, a separate report indicates that individuals with obesity have attenuated nutrient-stimulated GLP-1 circulating levels compared with normal weight individuals.¹³¹ Nonetheless, these data demonstrate that SCI induces wide-ranging dysregulation of GI hormone secretion affecting homeostatic levels and physiological response to prandial challenge, with many changes supporting previous literature related to cardiometabolic risks.

The impact of SCI on the GI system results in a number of complications, the most frequently reported being motility issues and bowel management^143
–145 with a recent review providing a broader discussion of GI symptoms.¹⁴⁶ More recently, gut dysbiosis has become an increasingly studied component of SCI-mediated GI pathophysiology. The gut microbiota are significantly altered after SCI^147,148 and have been shown to worsen neurological damage, directly impacting locomotor function.¹⁴⁹

Interestingly, changes in the gut microbiota have also been linked to obesity and metabolic dysfunction,^150,151 and several reports show that after SCI, dysbiosis of the gut microbiota is associated with serum lipid profiles¹⁵² and several pro-inflammatory cytokines in intestinal tissue,¹⁵³ both of which are hallmark features of metabolic diseases. Most notably, none of these reports establishes a causal relationship that would suggest gut dysbiosis is a contributing component to GI dysfunction and feasibly metabolic outcomes.

Although sympathetic components of the autonomic nervous system are directly damaged with SCI—likely disrupting GI structures and the homeostatic regulation of GI peptides—there has been little to no examination of the integrated function of the GI system in energy balance and metabolic regulation. Our results indicate substantial damage to the duodenum, including gross tissue disorganization and neuronal degeneration as illustrated in Figure 6, where loss of nuclear, TH, and NeuF staining is apparent.

While we acknowledge that there are limits and some subjectivity in what could be described as semi-quantitative immunofluorescence analysis, our methods are nevertheless consistent with several other reports using MFI analysis^74

–77; we believe the analyses are informative and supportive of our expected outcomes, particularly considering our controlled experimental design. Importantly, these observations provide the groundwork for more directed studies, which should include comprehensive quantitation and morphological analyses. Altogether, these data suggest that loss of cellularity and autonomic innervation to the GI tract may instigate the vast dysregulation of GI hormones, physiological response of the GI system, and contribute to cardiometabolic risks that we observe.

The current work builds on recent evidence examining autonomic dysregulation after SCI^27,28 and calls into question whether autonomic imbalance, interrupted neurotransmission within the GI system, and/or neuroendocrine dysregulation after injury are among contributing causes of cardiometabolic disease risks. Given our framework for understanding SCI pathophysiology, it is reasonable to speculate that disrupted innervation of GI (duodenal) tissue may contribute to the genetic, physiological, and metabolic outcomes shown.

In the scope of this study, we do not examine biological cause-effect mechanisms; however, preliminary correlation analysis was performed to identify statistical relationships between outcomes measured (Supplementary Fig. S1). Hierarchical clustering analysis indicates patterns of high and low correlation, with several notable observations. For example, BMS has a significant inverse relationship with fasting glucose (r = -0.76) and insulin (r = -0.99), respectively, which is understandable given reduced BMS is associated with injury, and we observe chronic increases in glucose and insulin after SCI.

The BMS also has a significant positive relationship with DAPI (r = 0.77) and NeuF (r = 0.92), respectively, and positive but insignificant relationship with TH (r = 0.22), suggesting that tissue damage from injury—i.e., reduced staining—is associated with concomitantly reduced BMS score. Similarly, mass has a significant positive relationship with DAPI (r = 0.76) and NeuF (r = 0.83), and positive but insignificant relationship with TH (r = 0.4), supporting loss of GI tissue and overall mass with injury.

In addition, tissue damage—as assessed by our immunostaining and illustrated in the correlogram matrix—has several significant correlations with changes we see in both GI genes and GI plasma hormones, demonstrating an association with these outcomes.

While we acknowledge that it is presumptive to overstate or draw conclusions from this preliminary analysis, these correlations may direct our future studies examining biological causality.

Conclusion and Future Directions

To our knowledge, ours is the first study to report on the consequences of SCI with respect to dissociation of autonomic neurotransmission, neuroendocrine dysfunction of hormones of the GI system, and their relationship, in part, to cardiometabolic disease risk factors. Importantly, the authors acknowledge that future experiments should evaluate severity and level of injury and provide a comprehensive stereological analysis across anatomical regions of the GI system, to better determine the relationship between SCI, GI dysfunction, and metabolic outcomes.

These data will help elucidate underlying mechanisms contributing to maladaptive nutrient-cycling and related metabolic dysfunction in SCI. Given the impact of cardiometabolic risks and extant disease on health and function in human SCI, studies of this kind may help identify therapeutic targets for intervention and rehabilitation and/or shape nutritional guidelines.

Footnotes

Authors' Contributions

All authors listed meet the criteria for authorship as defined by the International Committee of Medical Journal Editors.

Funding Information

This work was funded by The Miami Project to Cure Paralysis.

Author Disclosure Statement

No competing financial interests exist.

Supplementary Material

Supplementary Figure S1

References

Garshick

, Kelley

, Cohen

, et al. (2005). A prospective assessment of mortality in chronic spinal cord injury. Spinal Cord, 2005; 43:408–416

Myers

, Lee

, Kiratli

. Cardiovascular disease in spinal cord injury: an overview of prevalence, risk, evaluation, and management. Am J Phys Med Rehabil, 2007; 86:142–152

Nash

, Mendez

. (2007). A guideline-driven assessment of need for cardiovascular disease risk intervention in persons with chronic paraplegia. Arch Phys Med Rehabil, 2007; 88:751–757

Wahman

, Nash

, Lewis

, et al. Cardiovascular disease risk and the need for prevention after paraplegia determined by conventional multifactorial risk models: the Stockholm spinal cord injury study. J Rehabil Med, 2011; 43:237–242

Gorgey

, Gater Jr

. Prevalance of obesity after spinal cord injury. Top Spinal Cord Inj Rehabil, 2007; 12:1–7

Gater

Jr . Obesity after spinal cord injury. Phys Med Rehabil Clin N Am, 2007;18:333–351, vii

Bauman

, Spungen

. Coronary heart disease in individuals with spinal cord injury: assessment of risk factors. Spinal Cord, 2008; 46:466–476

Duckworth

, Solomon

, Jallepalli

, et al. Powers, A. Glucose intolerance due to insulin resistance in patients with spinal cord injuries. Diabetes, 1980; 29:906–910

Bauman

, Spungen

, Zhong

, et al. Depressed serum high density lipoprotein cholesterol levels in veterans with spinal cord injury. Paraplegia, 1992; 30:697–703

10.

Alan

, Ramer

, Inskip

, et al. Recurrent autonomic dysreflexia exacerbates vascular dysfunction after spinal cord injury. Spine J, 2010; 10:1108–1117

11.

Cragg

, Krassioukov

. Autonomic dysreflexia. CMAJ, 2012; 184:66

12.

Krassioukov

, Warburton

, Teasell

, et al. A systematic review of the management of autonomic dysreflexia after spinal cord injury. Arch Phys Med Rehabil, 2009; 90:682–695

13.

Nash

, Lewis

, Dyson-Hudson

, et al. Safety, tolerance, and efficacy of extended-release niacin monotherapy for treating dyslipidemia risks in persons with chronic tetraplegia: a randomized multicenter controlled trial. Arch Phys Med Rehabil, 2011; 92:399–410

14.

Bauman

, Spungen

. Metabolic changes in persons after spinal cord injury. Phys Med Rehabil Clin N Am, 2000; 11:109–140

15.

Gorgey

, Gater

. Regional and relative adiposity patterns in relation to carbohydrate and lipid metabolism in men with spinal cord injury. Appl Physiol Nutr Metab, 2011; 36:107–114

16.

Jialal

, Devaraj

, Venugopal

. C-reactive protein: risk marker or mediator in atherothrombosis?. Hypertension, 2004; 44:6–11

17.

Manns

, McCubbin

, Williams

. Fitness, inflammation, and the metabolic syndrome in men with paraplegia. Arch Phys Med Rehabil, 2005; 86:1176–1181

18.

Sturgeon

, Folsom

, Longstreth

Jr , et al. Hemostatic and inflammatory risk factors for intracerebral hemorrhage in a pooled cohort. Stroke, 2008; 39:2268–2273

19.

Nash

, Gater

. Exercise to reduce obesity in SCI. Top Spinal Cord Inj Rehabil, 2007; 12:76–93

20.

Hitzig

, Eng

, Miller

, et al. An evidence-based review of aging of the body systems following spinal cord injury. Spinal Cord, 2011; 49:684–701

21.

Nash

. Exercise as a health-promoting activity following spinal cord injury. J Neurol Phys Ther, 2005; 9:87–103,106

22.

Weaver

, Collins

, Kurichi

, et al. Prevalence of obesity and high blood pressure in veterans with spinal cord injuries and disorders: a retrospective review. Am J Phys Med Rehabil, 2007; 86:22–29

23.

Banerjea

, Sambamoorthi

, Weaver

, et al. Risk of stroke, heart attack, and diabetes complications among veterans with spinal cord injury. Arch Phys Med Rehabil, 2008; 89:1448–1453

24.

Buchholz

, Bugaresti

. A review of body mass index and waist circumference as markers of obesity and coronary heart disease risk in persons with chronic spinal cord injury. Spinal Cord, 2005; 43:513–518

25.

Groah

, Nash

, Ljungberg

, et al. Nutrient intake and body habitus after spinal cord injury: an analysis by sex and level of injury. J Spinal Cord Med, 2009; 32:25–33

26.

Knight

, Buchholz

, Martin Ginis

, et al. Leisure-time physical activity and diet quality are not associated in people with chronic spinal cord injury. Spinal Cord, 2011; 49:381–385

27.

Contributors: Krassioukov

, Biering-Sorensen

, Donovan

, et al. International Standards to document remaining Autonomic Function after Spinal Cord Injury (ISAFSCI), First Edition 2012. Top Spinal Cord Inj Rehabil, 2012; 18:282–296

28.

Krassioukov

, Biering-Sorensen

, Donovan

, et al. International standards to document remaining autonomic function after spinal cord injury. J Spinal Cord Med, 2012; 35:201–210

29.

Bigford

, Nash

. Nutritional health considerations for persons with spinal cord injury. Top Spinal Cord Inj Rehabil, 2017; 23:188–206

30.

Phillips

, Powley

. Innervation of the gastrointestinal tract: patterns of aging. Auton Neurosci, 2007; 136:1–19

31.

Furness

, Costa

. The adrenergic innervation of the gastrointestinal tract. Ergeb Physiol, 1974; 69:2–51

32.

Furness

JB.

The Enteric Nervous System. Blackwell: Oxford; 2006

33.

Furness

. The enteric nervous system and neurogastroenterology. Nat Rev Gastroenterol Hepatol, 2012; 9:286–294

34.

Bertrand

, Kunze

, Bornstein

, et al. Analysis of the responses of myenteric neurons in the small intestine to chemical stimulation of the mucosa. Am J Physiol, 1997; 273:G422–G435

35.

Furness

, Kunze

, Bertrand

, et al. Intrinsic primary afferent neurons of the intestine. Prog Neurobiol, 1998; 54:1–18

36.

Furness

, Jones

, Nurgali

, et al. Intrinsic primary afferent neurons and nerve circuits within the intestine. Prog Neurobiol, 2004; 72:143–164

37.

Berthoud

, Blackshaw

, Brookes

, et al. Neuroanatomy of extrinsic afferents supplying the gastrointestinal tract. Neurogastroenterol Motil, 2004; 16, Suppl 1:28–33

38.

Grundy

. Neuroanatomy of visceral nociception: vagal and splanchnic afferent. Gut, 2002; 51, Suppl 1:i2–i5

39.

Lomax

, Sharkey

, Furness

. The participation of the sympathetic innervation of the gastrointestinal tract in disease states. Neurogastroenterol Motil, 2010; 22:7–18

40.

Costanzo

. (2014). Physiologdy, 5th ed. Saunders: Philadelphia; 2014

41.

Morrison

, Berthoud

. Neurobiology of nutrition and obesity. Nutr Rev, 2007; 65:517–534

42.

Rehfeld

. A centenary of gastrointestinal endocrinology. Horm Metab Res, 2004; 36:735–741

43.

Read

, French

, Cunningham

. (1994). The role of the gut in regulating food intake in man. Nutr Rev, 1994; 52:1–10

44.

Naslund

, Hellstrom

. Appetite signaling: from gut peptides and enteric nerves to brain. Physiol Behav, 2007; 92:256–262

45.

Kojima

, Hosoda

, Date

, et al. Ghrelin is a growth-hormone-releasing acylated peptide from stomach. Nature, 1999; 402:656–660

46.

Murphy

, Bloom

. Gut hormones and the regulation of energy homeostasis. Nature, 2006; 444:854–859

47.

Lenard

, Berthoud

. Central and peripheral regulation of food intake and physical activity: pathways and genes. Obesity (Silver Spring), 2008; 16, Suppl 3:S11–S22

48.

Hellstrom

. Satiety signals and obesity. Curr Opin Gastroenterol, 2013; 29:222–227

49.

Buchan

, Polak

, Solcia

, et al. Electron immunohistochemical evidence for the human intestinal I cell as the source of CCK. Gut, 1978; 19:403–407

50.

Herrmann

, Goke

, Richter

, et al. Glucagon-like peptide-1 and glucose-dependent insulin-releasing polypeptide plasma levels in response to nutrients. Digestion, 1995; 56:117–126

51.

Batterham

, Cowley

, Small

, et al. Gut hormone PYY(3-36) physiologically inhibits food intake. Nature, 2002; 418:650–654

52.

Koda

, Date

, Murakami

, et al. The role of the vagal nerve in peripheral PYY3-36-induced feeding reduction in rats. Endocrinology, 2005; 146:2369–2375

53.

Smith

, Jerome

, Norgren

. Afferent axons in abdominal vagus mediate satiety effect of cholecystokinin in rats. Am J Physiol, 1985; 249:R638–R641

54.

Raybould

, Glatzle

, Freeman

, et al. Detection of macronutrients in the intestinal wall. Auton Neurosci, 2006; 125:28–33

55.

Chaudhri

, Small

, Bloom

. (2006). Gastrointestinal hormones regulating appetite. Philos Trans R Soc Lond B Biol Sci, 2006; 361:1187–1209

56.

Blevins

, Stanley

, Reidelberger

. (2000). Brain regions where cholecystokinin suppresses feeding in rats. Brain Res, 2000; 860:1–10

57.

Holst

. Incretin hormones and the satiation signal. Int J Obes (Lond), 2013; 37:1161–1168

58.

Vilsboll

, Krarup

, Madsbad

, et al. Both GLP-1 and GIP are insulinotropic at basal and postprandial glucose levels and contribute nearly equally to the incretin effect of a meal in healthy subjects. Regul Pept, 2003; 114:115–121.

59.

Pehling

, Tessari

, Gerich

, et al. Abnormal meal carbohydrate disposition in insulin-dependent diabetes. Relative contributions of endogenous glucose production and initial splanchnic uptake and effect of intensive insulin therapy. J Clin Invest, 1984; 74:985–991

60.

Murphy

, Dhillo

, Bloom

. (2006). Gut peptides in the regulation of food intake and energy homeostasis. Endocr Rev, 27, 719–727

61.

Suzuki

, Jayasena

, Bloom

. (2011). The gut hormones in appetite regulation. J Obes, 2011, 528401

62.

Klok

, Jakobsdottir

, Drent

. The role of leptin and ghrelin in the regulation of food intake and body weight in humans: a review. Obes Rev, 2007; 8:21–34

63.

Krassioukov

. Autonomic function following cervical spinal cord injury. Respir Physiol Neurobiol, 2009; 169:157–164

64.

Furlan

, Fehlings

. Cardiovascular complications after acute spinal cord injury: pathophysiology, diagnosis, and management. Neurosurg Focus, 2008; 25:E13

65.

Lynch

, Antony

, Dobbs

, et al. Bowel dysfunction following spinal cord injury. Spinal Cord, 2001; 39:193–203

66.

Benevento

, Sipski

. Neurogenic bladder, neurogenic bowel, and sexual dysfunction in people with spinal cord injury. Phys Ther, 2002; 82:601–612

67.

Bigford

, Bracchi-Ricard

, Keane

, et al. Neuroendocrine and cardiac metabolic dysfunction and NLRP3 inflammasome activation in adipose tissue and pancreas following chronic spinal cord injury in the mouse. ASN Neuro, 2013; 5:243–255

68.

Bigford

, Bracchi-Ricard

, Nash

, et al. Alterations in mouse hypothalamic adipokine gene expression and leptin signaling following chronic spinal cord injury and with advanced age. PLoS One, 2012; 7:e41073

69.

Bigford

, Darr

, Bracchi-Ricard

, et al. Effects of ursolic acid on sub-lesional muscle pathology in a contusion model of spinal cord injury. PLoS One, 2018; 13:e0203042

70.

Bigford

, Szeto

, Kimball

, et al. Cardiometabolic risks and atherosclerotic disease in ApoE knockout mice: effect of spinal cord injury and Salsalate anti-inflammatory pharmacotherapy. PLoS One, 2021; 16:e0246601

71.

Basso

, Fisher

, Anderson

, et al. Basso Mouse Scale for locomotion detects differences in recovery after spinal cord injury in five common mouse strains. J Neurotrauma, 2006; 23:635–659

72.

Nishi

, Liu

, Chu

, et al. Behavioral, histological, and ex vivo magnetic resonance imaging assessment of graded contusion spinal cord injury in mice. J Neurotrauma, 2007; 24:674–689

73.

Man

, Beckman

, Jaffe

. Sex as a biological variable in atherosclerosis. Circ Res, 2020:126:1297–1319

74.

Brazill

, Zhu

, Li

, et al. (2018). Quantitative cell biology of neurodegeneration in drosophila through unbiased analysis of fluorescently tagged proteins using ImageJ. J Vis Exp, 2018; 138:58041

75.

, Gao

, Feng

, et al. Evaluation of the protective potential of brain microvascular endothelial cell autophagy on blood-brain barrier integrity during experimental cerebral ischemia-reperfusion injury. Transl Stroke Res, 2014; 5:618–626

76.

Shihan

, Novo

, Le Marchand

, et al. A simple method for quantitating confocal fluorescent images. Biochem Biophys Rep, 2021; 25:100916

77.

Syvertsson

, Vischer

, Gao

, et al. When phase contrast fails: chaintracer and nuctracer, two Imagej methods for semi-automated single cell analysis using membrane or DNA staining. PLoS One, 2016; 11:e0151267

78.

Benede-Ubieto

, Estevez-Vazquez

, Ramadori

, et al. Guidelines and considerations for metabolic tolerance tests in mice. Diabetes Metab Syndr Obes, 2020; 13:439–450

79.

Virtue

, Vidal-Puig

. GTTs and ITTs in mice: simple tests, complex answers. Nat Metab, 2021; 3:883–886

80.

Vinue

, Gonzalez-Navarro

. (2015). Glucose and insulin tolerance tests in the mouse. Methods Mol Biol, 2015; 1339:247–254

81.

Basso

, Beattie

, Bresnahan

. A sensitive and reliable locomotor rating scale for open field testing in rats. J Neurotrauma, 1995; 12:1–21

82.

Raguindin

, Muka

, Glisic

. Sex and gender gap in spinal cord injury research: focus on cardiometabolic diseases. A mini review. Maturitas, 2021; 147:14–18

83.

McColl

, Charlifue

, Glass

, et al. (2004). Aging, gender, and spinal cord injury. Arch Phys Med Rehabil, 2004; 85:363–367

84.

National Spinal Cord Injury Statistical Center. (2018). 2018 Annual Statistical Report for the Spinal Cord Injury Model Systems. University of Alabama at Birmingham: Birmingham, Alabama; 2018

85.

Devivo

. Epidemiology of traumatic spinal cord injury: trends and future implications. Spinal Cord, 2012; 50:365–372

86.

Grimby

, Broberg

, Krotkiewska

, et al. Muscle fiber composition in patients with traumatic cord lesion. Scand J Rehabil Med, 1976; 8:37–42

87.

Scelsi

, Marchetti

, Poggi

, et al. (1982). Muscle fiber type morphology and distribution in paraplegic patients with traumatic cord lesion. Histochemical and ultrastructural aspects of rectus femoris muscle. Acta Neuropathol, 1982; 57:243–248

88.

Lotta

, Scelsi

, Alfonsi

, et al. Morphometric and neurophysiological analysis of skeletal muscle in paraplegic patients with traumatic cord lesion. Paraplegia, 1991; 29:247–252

89.

Round

, Barr

, Moffat

, et al. Fibre areas and histochemical fibre types in the quadriceps muscle of paraplegic subjects. J Neurol Sci, 1993; 116:207–211

90.

Burnham

, Martin

, Stein

, et al. Skeletal muscle fibre type transformation following spinal cord injury. Spinal Cord, 1997; 35:86–91

91.

Castro

, Apple

Jr , Hillegass

, et al. Influence of complete spinal cord injury on skeletal muscle cross-sectional area within the first 6 months of injury. Eur J Appl Physiol Occup Physiol, 1999; 80:373–378

92.

Castro

, Apple

Jr , Staron

, et al. Influence of complete spinal cord injury on skeletal muscle within 6 mo of injury. J Appl Physiol (1985), 1999; 86:350–358

93.

Modlesky

, Bickel

, Slade

, et al. Assessment of skeletal muscle mass in men with spinal cord injury using dual-energy X-ray absorptiometry and magnetic resonance imaging. J Appl Physiol (1985), 2004; 96:561–565

94.

Gorgey

, Dudley

. Skeletal muscle atrophy and increased intramuscular fat after incomplete spinal cord injury. Spinal Cord, 2007; 45:304–309

95.

Garland

, Stewart

, Adkins

, et al. Osteoporosis after spinal cord injury. J Orthop Res, 1992; 10:371–378

96.

Frey-Rindova

, de Bruin

, Stussi

, et al. Bone mineral density in upper and lower extremities during 12 months after spinal cord injury measured by peripheral quantitative computed tomography. Spinal Cord, 2000; 38:26–32

97.

Zehnder

, Luthi

, Michel

, et al. Long-term changes in bone metabolism, bone mineral density, quantitative ultrasound parameters, and fracture incidence after spinal cord injury: a cross-sectional observational study in 100 paraplegic men. Osteoporos Int, 2004; 15:180–189

98.

Giangregorio

, McCartney,N. Bone loss and muscle atrophy in spinal cord injury: epidemiology, fracture prediction, and rehabilitation strategies. J Spinal Cord Med, 2006; 29:489–500

99.

Maimoun

, Fattal

, Micallef

, et al. Bone loss in spinal cord-injured patients: from physiopathology to therapy. Spinal Cord, 2006; 44:203–210

100.

Qin

, Bauman

, Cardozo

. (2010). Bone and muscle loss after spinal cord injury: organ interactions. Ann N Y Acad Sci, 2010; 1211:66–84

101.

Battaglino

, Lazzari

, Garshick

, et al. Spinal cord injury-induced osteoporosis: pathogenesis and emerging therapies. Curr Osteoporos Rep, 2012; 10:278–285

102.

Bigford

, Donovan

, Webster

, et al. Selective myostatin inhibition spares sublesional muscle mass and myopenia-related dysfunction after severe spinal cord contusion in mice. J Neurotrauma, 2021; 38:3440–3455

103.

Spungen

, Adkins

, Stewart

, et al. (2003). Factors influencing body composition in persons with spinal cord injury: a cross-sectional study. J Appl Physiol, 2003; 95:2398–2407

104.

Maggioni

, Bertoli

, Margonato

, et al. (2003). Body composition assessment in spinal cord injury subjects. Acta Diabetol, 2003;40, Suppl 1:S183–S186

105.

Khalil

, Gorgey

, Janisko

, et al. The role of nutrition in health status after spinal cord injury. Aging Dis, 2013; 4:14–22

106.

Park

, Goodpaster

, Lee

, et al. Excessive loss of skeletal muscle mass in older adults with type 2 diabetes. Diabetes Care, 2009; 32:1993–1997

107.

Petersen

, Befroy

, Dufour

, et al. Mitochondrial dysfunction in the elderly: possible role in insulin resistance. Science, 2003; 300:1140–1142

108.

Dungan

, Braithwaite

, Preiser

. Stress hyperglycaemia. Lancet, 2009; 373:1798–1807

109.

World Health Organization. (2006). Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia. Available from: https://apps.who.int/iris/handle/10665/43588 [Last accessed: October, 16, 2022].

110.

Baird

, Duncan

. The glucose tolerance test. Postgrad Med J, 1959; 35; 308–314

111.

Elder

, Apple

, Bickel

, et al. Intramuscular fat and glucose tolerance after spinal cord injury–a cross-sectional study. Spinal Cord, 2004; 42:711–716

112.

Ellenbroek

, Kressler

, Cowan

, et al. Effects of prandial challenge on triglyceridemia, glycemia, and pro-inflammatory activity in persons with chronic paraplegia. J Spinal Cord Med, 2015; 38:468–475

113.

Cragg

, Noonan

, Dvorak

, et al. (2013). Spinal cord injury and type 2 diabetes: results from a population health survey. Neurology, 2013; 81:1864–1868

114.

Hagen

EM.

Comment: Increased risk for type 2 diabetes in spinal cord injury. Neurology, 2013:81:1867

115.

Bauman

, Spungen

. Disorders of carbohydrate and lipid metabolism in veterans with paraplegia or quadriplegia: a model of premature aging. Metabolism, 1994; 43:749–756

116.

Duckworth

, Jallepalli

, Solomon

. Glucose intolerance in spinal cord injury. Arch Phys Med Rehabil, 1983; 64:107–110

117.

Lavela

, Weaver

, Goldstein

, et al. Diabetes mellitus in individuals with spinal cord injury or disorder. J Spinal Cord Med, 2006; 29:387–395

118.

Rehfeld

JF.

The new biology of gastrointestinal hormones. Physiol Rev, 1998, 78:1087–1108

119.

Sakata

, Sakai

. (2010). Ghrelin cells in the gastrointestinal tract. Int J Pept, 2010; 2010:945056

120.

Tang-Christensen

, Larsen

, Goke

, et al. Central administration of GLP-1-(7-36) amide inhibits food and water intake in rats. Am J Physiol, 1996; 271:R848–R856

121.

Turton

, O'Shea

, Gunn

, et al. A role for glucagon-like peptide-1 in the central regulation of feeding. Nature, 1996; 379:69–72

122.

Sanger

, Lee,K. Hormones of the gut-brain axis as targets for the treatment of upper gastrointestinal disorders. Nat Rev Drug Discov, 2008; 7:241–254

123.

Zwirska-Korczala

, Konturek

, Sodowski

, et al. (2007). Basal and postprandial plasma levels of PYY, ghrelin, cholecystokinin, gastrin and insulin in women with moderate and morbid obesity and metabolic syndrome. J Physiol Pharmacol, 2007;58, Suppl 1:13–35

124.

Pilichiewicz

, Little

, Brennan

, et al. (2006). Effects of load, and duration, of duodenal lipid on antropyloroduodenal motility, plasma CCK and PYY, and energy intake in healthy men. Am J Physiol Regul Integr Comp Physiol, 2006; 290:R668–R677

125.

Brubaker

. The glucagon-like peptides: pleiotropic regulators of nutrient homeostasis. Ann N Y Acad Sci, 2006; 1070:10–26

126.

Martin

. The physiology of amylin and insulin: maintaining the balance between glucose secretion and glucose uptake. Diabetes Educ, 2006; 32, Suppl 3:101S–104S

127.

Kruger

, Gatcomb

, Owen

. Clinical implications of amylin and amylin deficiency. Diabetes Educ, 1999; 25:389–397

128.

Romon

, Lebel

, Velly

, et al. Leptin response to carbohydrate or fat meal and association with subsequent satiety and energy intake. Am J Physiol, 199; 277:E855–E861

129.

Papandreou

, Karavolias

, Arvaniti

, et al. Fasting ghrelin levels are decreased in obese subjects and are significantly related with insulin resistance and body mass index. Open Access Maced J Med Sci, 2017; 5:699–702

130.

English

, Ghatei

, Malik

, et al. Food fails to suppress ghrelin levels in obese humans. J Clin Endocrinol Metab, 2002; 87:2984

131.

Steinert

, Beglinger

, Langhans

. Intestinal GLP-1 and satiation: from man to rodents and back. Int J Obes (Lond), 2016; 40:198–205

132.

Manning

, Batterham

. The role of gut hormone peptide YY in energy and glucose homeostasis: twelve years on. Annu Rev Physiol, 2014; 76:585–608

133.

Dockray

. Cholecystokinin. Curr Opin Endocrinol Diabetes Obes, 2012; 19:8–12

134.

Brennan

, Luscombe-Marsh

, Seimon

, et al. Effects of fat, protein, and carbohydrate and protein load on appetite, plasma cholecystokinin, peptide YY, and ghrelin, and energy intake in lean and obese men. Am J Physiol Gastrointest Liver Physiol, 2012; 303:G129–G140

135.

Izquierdo

, Crujeiras

, Casanueva

, et al. Leptin, obesity, and leptin resistance: where are we 25 years later?. Nutrients, 2019; 11:2704

136.

Mok

, Makaronidis

, Batterham

. The role of gut hormones in obesity. Curr Opin Endocrine Metabol Res, 2019; 4:4–13

137.

Nauck

, Meier

. Incretin hormones: Their role in health and disease. Diabetes Obes Metab, 2018; 20, Suppl 1:5–21

138.

Chiasson

, Rabasa-Lhoret

. Prevention of type 2 diabetes: insulin resistance and beta-cell function. Diabetes, 2004; 53, Suppl 3:S34–S38

139.

Shanik

, Xu,Y, Skrha

, et al. Insulin resistance and hyperinsulinemia: is hyperinsulinemia the cart or the horse?. Diabetes Care, 2008; 31, Suppl 2:S262–S268

140.

Rachdaoui

. Insulin: the friend and the foe in the development of type 2 diabetes mellitus. Int J Mol Sci, 2020; 21:1770

141.

Reda

, Geliebter

, Pi-Sunyer

. Amylin, food intake, and obesity. Obes Res, 2002; 10:1087–1091

142.

Seon

, Hwang

, Son

, et al. Circulating GLP-1 levels as a potential indicator of metabolic syndrome risk in adult women. Nutrients, 2021; 13:865

143.

Gore

, Mintzer

, Calenoff

. Gastrointestinal complications of spinal cord injury. Spine (Phila Pa 1976), 1981; 6:538–544

144.

Cosman

, Stone

, Perkash,I. Gastrointestinal complications of chronic spinal cord injury. J Am Paraplegia Soc, 1991; 14:175–181

145.

Ebert

. Gastrointestinal involvement in spinal cord injury: a clinical perspective. J Gastrointestin Liver Dis, 2012; 21:75–82

146.

Holmes

, Blanke

. Gastrointestinal dysfunction after spinal cord injury. Exp Neurol, 2019; 320:113009

147.

Gungor

, Adiguzel

, Gursel

, et al. Intestinal microbiota in patients with spinal cord injury. PLoS One, 2016; 11:e0145878

148.

Zhang

, Zhang

, et al. Gut microbiota dysbiosis in male patients with chronic traumatic complete spinal cord injury. J Transl Med, 2018; 16:353.

149.

Kigerl

, Hall

, Wang

, et al. Gut dysbiosis impairs recovery after spinal cord injury. J Exp Med, 2016; 213: 2603–2620

150.

Baothman

, Zamzami

, Taher

, et al. The role of Gut Microbiota in the development of obesity and diabetes. Lipids Health Dis, 2016; 15:108

151.

Tilg

, Kaser

. Gut microbiome, obesity, and metabolic dysfunction. J Clin Invest, 2011; 121:2126–2132.

152.

Zhang

, Jing

, Zhang

, et al. (2019). Dysbiosis of gut microbiota is associated with serum lipid profiles in male patients with chronic traumatic cervical spinal cord injury. Am J Transl Res, 2019; 11:4817–4834

153.

O'Connor

, Jeffrey

, Madorma

, et al. Investigation of microbiota alterations and intestinal inflammation post-spinal cord injury in rat model. J Neurotrauma, 2018; 35:2159–2166

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.95 MB