Positive and negative energy surpluses in persons with chronic spinal cord injury

Abstract

Total energy expenditure (TEE) is estimated as the product of BMR and a spinal cord injury (SCI)-specific factor. The agreement between TEE and total energy intake (TEI) was just established. The findings suggested the existence of positive and negative energy balance distributions. Forty-two males with chronic SCI underwent BMR followed by a detailed metabolic profile after an overnight fast. TEI and macronutrients of 3-d dietary logs were analysed using the Nutrition Data System for Research software. Energy surplus was calculated as TEE minus TEI. Body composition assessment was conducted using dual-energy X-ray absorptiometry. 57 % of SCI participants were classified as negative energy surplus with an average TEI of 1284 (SD 422) compared with 2197 (SD 553) kcal/d in the positive energy group (P = 0·0002). Negative energy group had a higher BMR (9 %; P = 0·02), greater body weight (P = 0·03) and greater total body lean mass (P = 0·03) and consumed a greater percentage of protein compared with the positive energy group. Percentage macronutrients of protein explained 27 % of the variance of energy surplus in a multivariate regression model (r² 0·27; P = 0·008). TEI adjusted to fat-free mass explained 87 % of the variance in energy surplus, and an intake of 34·7 kcal/kg per d was recommended to balance TEI with TEE. Persons with SCI are either classified into negative or positive energy surplus groups. Larger body weight and greater protein intakes are among the major characteristics of the negative energy group. Clinicians may need to consider the spectrum of energy balance before starting a dietary regimen after SCI.

Neurogenic obesity is an escalating pandemic with alarming prevalence after spinal cord injury (SCI)^(1,2). This is confirmed by previous reports that suggested that the prevalence of neurogenic obesity may exceed 1/3 of the entire SCI population and contributes to cardiometabolic co-morbidities^(1,3–5). According to the WHO, the definition of obesity depends on establishing a specific BMI of equal to or greater than 30 kg/m^2(6,7). However, the WHO cut-off criteria underestimates the percentage fat mass (FM) after SCI⁽⁸⁾. An adjusted BMI of 22 kg/m² has been recommended to offset this problem and has been considered as one of the guidelines to combat cardiometabolic risks after SCI^(8–11). A percentage FM equal to or greater than 20 % has also been suggested as a criterion to define the risk of obesity and has been adopted after SCI^(10,11). The use of the percentage FM definition would result in a higher prevalence of obesity than what has been previously estimated within the SCI population^(1,2,10,11). The problem of obesity is multi-factorial as a product of energy imbalance, poor dietary habits, reduced level of physical activity and altered parameters of body composition in this population^(2,12). Several studies have addressed each component separately^(12–16); however, it is still unclear the relevant contribution of each component to the entire problem.

Energy balance is the process of maintaining internal body homeostasis and non-exercise physical activity relative to daily total energy intake (TEI)^(1,12). The high prevalence of obesity after SCI is attributed to the imbalance between daily TEI and total energy expenditure (TEE). A higher TEI relative to TEE would result in positive energy balance or surplus (i.e. hyperenergetic diet) and subsequently weight gain^(1,12). On the contrary, higher TEE compared with daily TEI would result in negative energy balance or negative energy surplus (i.e. hypoenergetic diet) and eventually weight loss^(13,17). Longitudinally, the latter is recognised as a process of caloric restriction or a medical condition that may lead to malnourishment, other associated co-morbidities and eventually mortality^(7,13,17,18). The challenge of balancing TEI and TEE is considered problematic as no accurate methods currently exist to measure both energy components after SCI^(19,20). Furthermore, conflicting findings have been previously reported regarding whether persons with SCI consume a hyperenergetic or hypoenergetic diet^{(12,13,16,17)}. Most of the studies suggest that a hyperenergetic diet is the primary reason for the high prevalence of obesity after SCI^(1,2,12). Therefore, attaining a balance between TEI and TEE may serve as a key rehabilitation approach to combat obesity or malnutrition in persons with SCI. This can be pursued by identifying predictors that are likely to minimise energy surplus to its nadir in persons with SCI.

Self-reported dietary logs were previously used to estimate TEI and macronutrients in persons with SCI^(13–15). Participants were encouraged to record their daily dietary intake over a period of a week or two to estimate their daily TEI^(13–15). On the other hand, BMR was used to accurately calculate total TEE after multiplying by an SCI-specific factor (1·15) that represents the level of physical activity in this population⁽²¹⁾. BMR is defined as the minimum energy required to maintain whole body energy homeostasis and function of different systems during rest or at basal conditions^(19,20). Recently, Farkas et al. successfully determined the agreement between TEI and TEE. The findings indicated that the SCI-specific equation accurately predicted TEI in persons with SCI compared with the utilisation of the Long method or the Institute of Medicine equations⁽²²⁾. However, the Bland–Altman analysis clearly suggested that there is a wide distribution (1135 to −1443 kcal/d) between TEI and TEE even with the utilisation of the SCI-specific equation⁽²²⁾. This may highlight the fluctuation of energy balance in a range that is determined by either positive or negative energy surplus. Another important observation is that 24 % of the SCI population have measured BMR values greater than 1600 kcal/d, while 42 and 33 % have BMR values lower than 1370 kcal/d and 1600 kcal/d, respectively⁽²³⁾. This may suggest that energy requirements vary greatly based on the measured BMR and are likely to be an important determinant for narrowing the gap between positive and negative energy surpluses. Therefore, the cross-sectional study was designed to examine the characteristics of SCI persons with positive and negative energy surpluses as determined by calculating the difference between TEI and TEE. A second objective was to determine whether differences in energy surplus may influence metabolic profile in persons with SCI. Finally, the study identified the predictors that may minimise energy surplus following SCI.

Materials and methods

Participants

Forty-two male participants with chronic SCI participated in the present study. Participants were recruited as part of two randomised clinical trials (NCT01652040 and NCT02660073) that were conducted to examine the effects of electrical stimulation exercises on cardiometabolic risk factors after SCI^(24,25). This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects/patients were approved by the Richmond Institute for Veterans Research ethics committee with specific ethics no. 01699 and 02152. Written informed consent was obtained from all participants, which was approved by the local ethics committee. Each participant had an opportunity to read and sign a consent form to indicate their understanding of the entire procedure prior to participation.

All participants were at least 1-year post-injury and had a neurological level of injury of C5-L1 with an American Spinal Injury Association Impairment Scale (AIS) classification of A, B or C. Participants included in the study were between the ages of 18 and 65. Placing the maximum age at 65 avoided any potential confounding effects of nutritional deficiencies associated with aging^(24,25). Participants were excluded if they had pre-existing conditions associated with vitamin D deficiency, metabolic diseases or uncontrolled type II diabetes. They were also excluded if they were treated with insulin, had uncontrolled hypertension, CVD, a stage 3 pressure ulcer or greater or the diagnosis of osteoporosis. All participants underwent a physical examination by a qualified physiatrist followed by dual-energy X-ray absorptiometry (DXA) assessment. Each participant received dietary logs to record their food and drink intake for 2 weeks^(24,25).

Body weight

Participants’ height and weight were obtained at enrolment using a PW-630U weighing scale. They were weighed in light clothing without shoes while in their wheelchairs before transferring to an adjustable mat to have their empty wheelchairs weighed. The difference between the two measurements represented the participant’s weight in kilogram (kg). The participants were placed supine on a flat mat, with the trunk aligned with the head, legs and knees fully extended to obtain their heights. The length between two smooth boards placed at the head and heels while the participant was supine represented the participant’s height in centimetres (cm). Each participant’s BMI was calculated by dividing the body weight in kg by the square of the height in metres (m), after converting the height from cm to m^(24–28).

Dual-energy X-ray absorptiometry

Total body and regional DXA scans were performed using iLunar DXA (Lunar Inc.) bone densitometer at the Richmond, VA Medical Center^(28,29). Calibration was performed using a phantom calibration box, simulating human tissues, to certify scan reliability and precision^(28,29). All metals were removed from all participants before placing them supine on the scanning table^(28,29). After a 20-min rest on a padded table, scans were performed and analysed by a trained, certified DXA operator using the iLunar system. Total and regional lean masses were determined using total and regional DXA software.

Appendicular lean mass (arms lean mass + legs lean mass, kg) was adjusted to the height-square (height²; m²) of each participant to calculate the sarcopenia index^(30,31). Previously, the sarcopenia index was used as a predictor of energy intake adjusted to BMR in the Korean population⁽³²⁾.

BMR

Participants were instructed to refrain from exercise, caffeine, nicotine and alcohol at least 12 h prior to the testing period in accordance with minimal criteria for best practice BMR guidelines^(13,20). Participants were escorted to either a clinical research centre or a local hotel to spend the night before measuring BMR in the hotel room prior to getting out of bed. Following a 12-h overnight fast, participants were woken up gently at ~06.30 to undergo a BMR assessment. All measurements were completed in a dark, quiet thermoneutral environment (temperature between 20°C and 25°C). BMR was measured using a portable metabolic system (COSMED K4b²), and the unit was calibrated in a thermoneutral condition according to manufacturer guidelines.

Following calibration, a canopy was placed over the participant’s head while they were in a supine position. Oxygen consumption was measured with continuous breath-by-breath measurements for over a 20-min period. Participants were monitored to ensure they did not fall asleep during data collection, and no interruptions occurred. Data were discarded for the initial 5-min period, and BMR (kcal/d) was then averaged over the remaining 15 min. Energy expenditure was determined using the Weir equation. If RER (carbon dioxide production/oxygen used) values were < 0·70 or > 1·00, participants were excluded from analysis, as such values are indicative of inaccurate gas measurements or protocol violation⁽²⁰⁾. Total energy expenditure (TEE) was measured as the product of BMR and (1·15; SCI-specific factor)⁽²¹⁾.

Metabolic profile

Fasting glucose and insulin (2 ml), lipid profile (HDL-cholesterol, LDL-cholesterol, total cholesterol and TAG; 2 ml), inflammatory biomarkers (C-reactive protein, TNF-α, IL-6, free-fatty acid; 2 ml) and anabolic growth factors (testosterone, insulin growth factors-1 and insulin growth factors binding protein-3) were assessed immediately following BMR measurement^(14,24,25). A Teflon catheter was inserted into an antecubital vein of one arm or a dorsal hand vein for blood sampling, and the samples were sent for analysis at the Clinical Pathology Laboratory. Fasting samples were captured three times every 30 min. After allowing the blood sample to clot for 30 min, the blood was centrifuged at 3000 RPM for 10 min, and the serum was transferred for analysis.

Dietary intake

All participants met with a registered dietitian before enrolling in the study. The meeting lasts for 30–45 min at the clinical research centre^(13–15,23). Participants were instructed on how to adequately fill dietary logs with clear instructions on how to provide logs on daily intakes of foods, snacks and drinks. Each participant was provided with weekly sheets and was instructed to fill 3 d of dietary logs for 2 subsequent weeks for the purpose of the present study. Data from the first 2 weeks were considered for analysis because participants are less likely to change their dietary habits within the initial 2 weeks of the study. Caloric intake and macronutrients using the Nutritional software (Nutritional Data System for Research, version 2018) were measured^(13–15,23). Dietary logs of thirty participants were also considered for weeks 5–6 and 7–8. The average caloric intakes of these weeks were used to determine the pattern and the mean absolute error compared with caloric intake from weeks 1–2.

Statistical analysis

Participants were stratified according to the difference between TEI and TEE into either the negative energy surplus group or the positive energy surplus group. All statistical analyses were performed using the IBM Statistical Package for the Social Sciences version 29.0 (IBM Corp.). Statistical analysis included descriptive statistics (mean (SD) for all the study variables. All variables were checked for normality using the Shapiro–Wilk tests. If Shapiro–Wilk tests were significant, the study variable was checked for outliers, and then the entire analysis was rerun after removal of the outliers. If there are no outliers, the Q–Q plots and the histograms were visually inspected to ensure normal distribution. The primary independent outcome variables of the study were TEI and TEE.

Independent t tests were used to compare the differences in the outcome variables (caloric intake, energy surplus, BMR, body composition assessment and metabolic profile) between groups of negative (n 24) and positive (n 18) energy surpluses. A 95 % limits of agreement analysis was performed (mean difference (SD 1·96)) comparing TEI and TEE, with data displayed using Bland–Altman plots. Comparison statistics included mean absolute error, which is presented as a percentage (Percentage error = (estimated TEI_{week 5–6} – TEI_{week 1–2})/TEI_{week 1–2} × 100). Linear regression analyses and Pearson’s correlations were examined to determine the variance and association between percentage macronutrients and energy surpluses.

Results

The physical and SCI characteristics of the forty-two participants are presented in Table 1. Twenty-four (57 %) were classified as negative energy surplus participants with an average caloric deficit relative to TEE of 526 (SD 423) kcal. The remaining eighteen participants were classified as positive energy surplus with excess caloric intake of 552 (SD 552) kcal. The difference in energy balance between both groups was statistically different (P < 0·001). Body weight was positively related to energy surplus (n 42; r = 0·39; P = 0·01), as well as % total body FM (n 42; r = 0·37; P = 0·015), but not lean mass. Finally, the energy surplus was positively related to BMR (n 42; r = 0·40; P = 0·01), absolute total body FM (n 42; r = 0·41; P = 0·007) and percentage FM (n 42; r = 0·37; P = 0·015), as well as absolute leg FM (n 42; r = 0·34; P = 0·027) and percentage leg FM (n 42; r = 0·34; P = 0·029), but not lean mass.

Table 1.

Participant physical and spinal cord injury characteristics, mean (SD) and percentages (n 42)

	Positive energy surplus		Negative energy surplus		Independent t tests
	Mean	SD	Mean	SD
Sample size, %	n 18, 43 %		n 24, 57 %
Age (years)	38	13	38	11·5	P = 0·8
Weight (kg)	69	13·4	78·5	16	P = 0·040
Height (cm)	177·5	7·0	179	5·0	P = 0·37
BMI (kg/m2)	22·0	4·7	24·5	4·8	P = 0·09
	n	%	n	%
Race/ethnicity (%)
Caucasian African	11	61 %	14	58 %
American	7	39 %	10	42 %
Level of injury (%)
Paraplegia	10	56 %	16	67%
Tetraplegia	8	44 %	8	33 %
Completeness of injury
Complete	10	55 %	17	71 %
Incomplete	8	45 %	7	29 %
Classification of injury (%)
A	10	55 %	17	71 %
B	6	34 %	5	21 %
C	2	11%	2	8%
	Mean	SD	Mean	SD
Time since injury (years)	12	12	10	18	0·5

Figure 1 highlights the Bland–Altman analysis between TEI and TEE in forty-two persons with SCI. The mean bias between TEI and TEE was −155 (SD 640) kcal/d. BMR was 9 % higher (P = 0·02) in the negative energy group (1574 (SD 185) kcal/d) compared with the positive energy group (1431 (SD 184) kcal/d) (Table 2). Body weight was also greater (P = 0·03) in the negative energy group (78·5 (SD 15) kg) compared with the positive energy group (69·0 (SD 13) kg). Energy surplus relative to body weight indicated that the negative energy group consumed 6·6 (SD 4·3) kcal/kg compared with 8·7 (SD 9·3) kcal/kg in the positive energy group (P < 0·0001). However, the estimated energy requirements (TEE/body weight) were not different (P = 0·6) between the positive (24·5 (SD 3·7) kcal/kg) and negative (24·0 (SD 3·6) kcal/kg) energy groups (Table 2).

Figure 1.

Bland–Altman plots measuring the level of agreement against estimated total energy intake (TEI) and the SCI-specific total energy expenditure (TEE). Solid block circles represent individual study participants (n 42). The solid line represents the mean difference between the two measurements, while the dashed lines represent the 95 % CI (mean (1·96 SD above and below the mean difference)).

Table 2.

Energy intake and expenditure assessment in the positive energy and negative energy groups

	Positive energy surplus		Negative energy surplus		Independent t tests
	Mean	SD	Mean	SD
Total energy intake (TEI; kcal/d)	2197	553	1284	422	P = 0·0001
BMR (kcal/d)	1431	184	1574	185	P = 0·02
Total energy expenditure (TEE; kcal/d)	1646	212	1810	212·5	P = 0·02
Energy surplus (kcal/d)	−551·5	551·6	526	423	P = 0·0001
Energy Surplus adjusted to TEE*	0·35	0·36	0·29	0·21	P = 0·0001
Energy surplus/body weight (kcal/ kg per d)	−8·6	9·3	6·6	4·3	P = 0·0001
TEI: BMR	1·55	0·4	0·82	0·2	P = 0·0001
TEI: body weight (kcal/ kg per d)	33	10·6	17	6·5	P = 0·0001
BMR: body weight (kcal/ kg per d)	213	32	21·0	3·2	P = 0·6
BMR: body lean mass (kcal/kg per d)	31·4	4·0	32·4	3·3	P = 0·4
AIS A-Compete SCI	Energy surplus:		Energy surplus:
	−361	311	561	404	P = 0·0001
	Energy surplus/body weight:		Energy surplus/body weight:
	−5·1	4·6	7·0	4·4	P = 0·0001
	Energy surplus/lean mass:		Energy surplus/lean mass:
	−7·5	62	110	8·3	P = 0·0001
AIS B-C Incomplete SCI	Energy surplus:		Energy surplus:
	−790	706	473	384	P = 0·001
	Energy surplus/body weight:		Energy surplus/body weight:
	−13	12	6·0	4·0	P = 0·002
	Energy surplus/lean mass:		Energy surplus/lean mass:
	−17·6	15·5	9·6	7·0	P = 0·001
Sarcopenia index (kg/m2) **	6·4	1·3	6·6	1·3	P = 0·6

AIS, American Spinal Injury Association Impairment Scale; SCI, spinal cord injury. Energy surplus (kcal/d) = TEE-TEI.

^*Energy surplus adjusted to TEE indicated that the positive energy group consumed 35 % above their daily caloric needs, compared with the negative energy group who consumed on average 29 % below their daily caloric needs.

^**Sarcopenia index was calculated as the appendicular lean mass measured by dual-energy X-ray absorptiometry (arm lean mass + leg lean mass)/ height².

The results of absolute or adjusted energy surplus were independent of the AIS classifications and maintained regardless of whether the classification was either complete SCI AIS A or incomplete SCI AIS B and C (Table 2). Furthermore, the sarcopenia index, based on appendicular lean mass, was not different (P = 0·6) between both groups (Table 2).

Caloric intake and macronutrients

Total energy intake was 71 % lower (1284 (SD 422) kcal v. 2197 (SD 553) kcal; P < 0·0001) in the negative energy group (n 24) compared with the positive energy group (n 18) (Table 2). The negative energy group had a greater percentage of macronutrients of protein intake (19·8 (SD 3·9) v. 16·7 (SD 3·5) %; P = 0·01) compared with the positive energy group. However, the percentage of macronutrients of fats was not different between groups, with a tendency of greater carbohydrate intake in the positive energy group compared with the negative energy group (46·4 (SD 6·0) v. 41·7 (SD 9·6) %; P = 0·06).

Body composition assessment

The negative energy group has a greater total body lean mass (50·4 (SD 7·3) v. 46 (SD 6·3) kg; P = 0·032) and a tendency of greater total body FM (25·0 (SD 10·7) v. 19·7 (SD 9·0) kg; P = 0·09) compared with the positive energy group (Table 3). There was a tendency of greater leg FM in the negative energy group compared with the positive energy group (7·7 (SD 3·8) v. 5·9 (SD 2·5) kg; P = 0·06). Total and regional fat mass index (FM/height²) were not different between groups (Table 3).

Table 3.

Anthropometrics, body composition assessment and whole thigh cross-sectional area in the positive and negative energy groups

		Positive energy surplus		Negative energy surplus		Independent t tests
		Mean	SD	Mean	SD
DXA	Leg-FMI (kg/m2)	3·3	1·2	4·3	2·0	P = 0·07
	Leg-FM (kg)	5·9	2·3	7·7	3·8	P = 0·08
	Leg lean mass (kg)	136	31	14·5	3·3	P = 0·30
	Trunk-FMI	6·0	4·0	8·0	4·0	P = 0·18
	Trunk-FM (kg)	20·0	9·0	25	11	P = 0·09
	Trunk lean mass (kg)	22·0	2·7	24·0	2·9	P = 0·01
	Total-FMI (kg/m2)	11·0	5·2	14·0	6·5	P = 0·11
	Total-FM (kg)	11·0	7·0	14·0	7·0	P = 0·14
	Total lean mass (kg)	46·0	6·3	50·4	7·3	P = 0·035

DXA, dual-energy X-ray absorptiometry; FM, fat mass; FMI, fat mass index (fat mass/height²).

Furthermore, trunk lean mass (24·2 (SD 2·9) v. 22·0 (SD 2·8) kg; P = 0·01), android lean mass (36·2 (SD 0·54) v. 31·4 (SD 0·5) kg; P = 0·005) and gynoid lean mass (6·5 (SD 1·0) v. 5·75 (SD 0·8) kg; P = 0·01) followed the same pattern in the negative energy group compared with the positive energy group (Table 3). Finally, gynoid FM was greater (3·9 (SD 1·9) v. 2·9 (SD 1·3) kg; P = 0·04) in the negative energy group compared with the positive energy group.

Metabolic profile

Circulating fasting plasma insulin was 2× lower (P = 0·050) in the negative energy group compared with the positive energy group. Fasting insulin was not related to percentage protein, lean mass or muscle cross-sectional area. The circulating fasting plasma glucose, lipid panel, anabolic profile and inflammatory biomarkers were not different between groups (Table 4).

Table 4.

Metabolic profile assessment in the positive and negative energy groups

		Positive energy surplus		Negative energy surplus		Independent t tests
		Mean	SD	Mean	SD
Lipid panel	LDL-cholesterol (mg/dl)	93	33	94	21	P = 0·26
	HDL-cholesterol (mg/dl)	36	9·7	39	9·0	P = 0·20
	Total cholesterol (mg/dl)	151	41	154	21	P = 0·27
	Total cholesterol: HDL-cholesterol ratio	4·4	1·3	4·2	11	P = 0·7
Fasting glucose profile	Fasting plasma glucose (mg/dl)	88·5	9·6	92·0	16·0	P = 0·34
	Fasting plasma insulin (μu/l)	7·0	6·2	3·8	28	P = 0·05
Anabolic profile	Testosterone (ng/dl)	421·0	156	460	177	P = 0·5
	IGF-1 (ng/ml)	131	71	153	86	P = 0·37
	IGFBP-3 (ng/ml)	1695	698	1927	589	P = 0·26
Inflammatory biomarkers	IL-6 (pg/ml)	5·6	5·1	4·6	5·7	P = 0·57
	TNF-α (pg/ml)	18·7	7·9	17·2	7·4	P = 0·53
	CRP (ng/ml)	9339	14 827	9413	11 787	P = 0·90
	FFA (μu/ml)	313	179	361	270	P = 0·51

IGF-1, insulin growth factors-1; IGFBP-3, insulin growth factors binding protein-3; CRP, C-reactive protein; FFA, free-fatty acids.

Predictors of energy surplus

Linear regression analyses indicated that percentage macronutrients (n 41) of protein (r² 0·24; P = 0·001; Figure 2(a)), of fats (r² 0·10; P 0·04) and of carbohydrates (r² = 0·18; P = 0·007) independently explain variance in energy surpluses. However, only percentage macronutrients of protein explained 27 % of the variance in energy surplus in a multivariate regression model (r² 0·27; P = 0·008), but not fats or carbohydrates. Strong negative correlations were noted between percentage macronutrients of protein and TEI (n 41; r = –0·51, P = 0·0001; Figure 2(b)) and percentage macronutrients of carbohydrates (r = –0·67, P = 0·0001; Figure 2(c)). On the contrary, the percentage macronutrients of carbohydrates were positively related to TEI (n 41; r = 0·38, P = 0·014; Figure 3(a)) and negatively related to percentage macronutrients of fats (Figure 3(b)).

Figure 2.

Linear regression analyses (a) percentage macronutrients of protein and energy surplus; (b) percentage macronutrients of protein and total energy intake (TEI; kcal/d) and (c) percentage macronutrients of protein and carbohydrates.

Figure 3.

Linear regression analyses (a) percentage macronutrients of carbohydrates and total energy intake (TEI; kcal/d); (b) percentage macronutrients of carbohydrates and percentage macronutrients of fats.

Furthermore, TEI adjusted to fat-free mass (FFM) explained 87 % of the variance in energy surplus (r² 0·87, P < 0·001; Figure 4). The results indicated that a TEI adjusted to FFM of 34·7 kcal/kg per d is likely to balance TEI and TEE and results in zero energy surplus (Energy Surplus = –47·24 (TEI adjusted to FFM) + 1638). The sarcopenia index was not related to energy surplus in persons with SCI.

Figure 4.

Linear regression analysis of TEI adjusted to fat-free mass (FFM) and energy surplus. Based on the regression analysis, a 34·7 kcal/kg per d hypothetical value was recommended to balance TEE and TEI.

Regardless of the group assignments, the caloric intakes of thirty participants out of the forty-two participants were determined over time for weeks 5–6 and weeks 7–8. Repeated-measures ANOVA indicated that there was no difference (P = 0·80) in caloric intakes in weeks 1–2 (1753 (SD 681)), weeks 5–6 (1750 (SD 1154)) and weeks 7–8 (1675 (SD 646)). Linear regression analysis indicated that caloric intake in weeks 1–2 explained 42 % in weeks 5–6 (r² 0·42; P < 0·001; Figure 5(a)) with a mean bias of 0·22 %; Figure 5(b) and 52 % in weeks 7–8 (r² 0·52; P < 0·001; Figure 4(c)) with a mean bias of 0·27 % (Figure 5(d)).

Figure 5.

Represents the agreement (n 30 participants) between total energy intake (TEI) at weeks (1–2), weeks (5–6) and weeks (7–8). (a) linear regression analysis between TEI at weeks 1–2 and weeks 5–6; (b) Bland–Altman analysis of the TEI between weeks 1–2 and weeks 5–6 (SEM is 0·24 %); (c) linear regression analysis between TEI at weeks 1–2 and weeks 7–6; (d) Bland–Altman analysis of the TEI between weeks 1–2 and weeks 6–7 (SEM is 0·22 %). The results highlight the variability in caloric intake in participants with SCI.

Discussion

Persons with SCI experience a wide spectrum of energy balance as characterised by the difference in TEI and total TEE. This resulted in the classification of forty-two participants with chronic SCI based on their energy surpluses into either negative or positive energy surpluses. Notably, 57 % of the negative energy participants had lower caloric intake than their estimated TEE. TEE was calculated as the product of BMR × 1·15 (SCI-specific factor)⁽²¹⁾. The negative energy group was heavier than the positive energy group with favourable body composition as characterised by greater total body lean mass, trunk and leg lean masses. Additionally, the negative energy group consumes a greater % macronutrients of protein with a lower fasting insulin level compared with the positive energy group. There were clear independent associations of the percentage macronutrients of protein, carbohydrates and fats with energy surplus. However, a multiple regression model indicated that only the percentage macronutrients of protein explained 27 % of the variance in energy surplus. Finally, simple regression analysis indicated that TEI adjusted to FFM explained 87 % of the energy surplus. A recommended TEI of 34·7 kcal/kg per d is likely to balance TEE with TEI and results in an energy surplus of zero.

Rationale of conducting the trial

Dietary guidelines for Americans 2020–2025 indicated that positive energy surplus is a continuous problem that leads to the pandemic of obesity in the US population⁽³³⁾. Despite these recommendations, adherence of the US population to the dietary guidelines did not exceed 59 % over a 10-year period. As a result, 74 % of the US adults were either overweight or obese. In our study, 44 % (8/18) and 71 % (17/24) were considered obese (BMI > 22 kg/m²) in the positive and negative energy groups, respectively. In 2009–2010, the unadjusted mean caloric intake for men was approximately 2565 kcal/d⁽³⁴⁾. This comparison may suggest that the positive and energy surplus groups with SCI consumed approximately 14 and 50 %, respectively, lower daily caloric intake than the average American adults. On the other hand, a positive energy surplus of 50–100 kcal/d may gradually accumulate to cause obesity in adults⁽³⁵⁾. Energy surplus in our cohort is approximately 5× more in the positive energy group. Therefore, the extrapolation of able-bodied dietary trends to adults with SCI may be misleading and clinically problematic.

Maintenance of adequate caloric balance is considered a successful approach to combating obesity and malnutrition in persons with SCI^(1,12). Surprisingly, 57 % of the participants in the present study were classified as being on a negative energy diet (i.e. TEE > TEI). This is measured by the difference in the average daily caloric intake (TEI) of the first 2 weeks compared with their TEE. This pattern resembles caloric-deficit diet studies that have previously been shown to effectively manage obesity and reduce body weight⁽¹⁸⁾. However, it is established that lower caloric intake or caloric restriction negatively impacts BMR and may lead to induced cachexia^(18,36). A previous review indicated that caloric restriction may lead to a 15 % decrease in BMR⁽¹⁸⁾. Caloric restriction also results in a loss in body weight, with a 30 % loss in FFM⁽¹⁸⁾. Similarly, a previous retrospective case report indicated that a caloric deficit of 445 kcal/d (25 %) resulted in an 8 % reduction in body weight, accompanied by decreases of 29 % in total body fat and 31 % in thigh subcutaneous adipose tissue⁽³⁷⁾. Finally, trunk visceral adipose tissue and subcutaneous adipose tissue cross-sectional area decreased by 12 and 22 % following 16 weeks⁽³⁷⁾. A Mediterranean low-fat diet with lifestyle changes in the form of circuit resistance training for 6 months resulted in 7 % decrease in body weight with improvement in cardiometabolic risk factors in three persons with SCI⁽³⁸⁾. Others have also shown that caloric deficit via conducting gastric sleeve surgery in a person with C5 AIS D SCI resulted in decreasing BMI by 34 % and improvement in gait parameters as measured by 6-min walking test and 10-m walking test after 52 weeks⁽³⁹⁾.

The findings in the present study are in line with a previous trial that indicated that participants with perceived higher BMI consumed fewer calories than participants who perceived themselves with a lower BMI⁽⁴⁰⁾. However, the current findings should be considered with caution because an extensive caloric deficit may induce malnutrition and other co-morbidities^(17,37). Malnutrition is a common problem that occurs early in the acute stage because of associated anxiety and depression after SCI⁽¹⁷⁾. Therefore, it is important to ensure that these participants did not experience malnutrition, which may induce catabolic effects on lean mass and result in fatigue as well as a reduction of activities of daily living⁽³⁶⁾. Additionally, our findings indicated that balancing TEI with TEE may combat obesity and overcome the problem of malnutrition after SCI. A recommended TEI of 34·7 kcal/kg per d has mathematically equated to zero surplus in our model. This model may need accurate calculations of FFM either via DXA or body weight using previously proposed predicted equations in persons with SCI⁽⁴¹⁾. However, this may need additional validation in a separate cohort with SCI.

The use of DXA as a three-compartment assessment technique provides an opportunity to measure fat mass, lean mass and bone mass^(32,42). Compared with the gold standard technique, the use of DXA may introduce an error close to 5 % in measuring fat mass in persons with SCI⁽²⁸⁾. In the current trial, fat mass index was used compared with %FM as a part of the nutritional science guidelines⁽⁴³⁾. The use of %FM as an index of adiposity may be statistically problematic⁽⁴⁴⁾. This may result from dividing fat mass by body weight as fat mass may exist in both the numerator and denominator⁽⁴⁴⁾. Additionally, the increase in absolute FM may eventually trend towards an asymptote at 60 % fat mass⁽⁴⁴⁾. Despite this limitation, %FM may allow comparisons across different studies independent of the body composition assessment techniques in persons with SCI.

Total body lean mass explained more than 65 % of the variance in BMR after SCI⁽²³⁾. The negative energy group has 9 % higher BMR (~143 kcal/d) than the positive energy group. The difference in BMR is simply explained by the difference in body weight and primarily explained by the difference in lean mass. The precision of measuring BMR is highly important for the outcomes of TEE. Indirect calorimetry is considered the gold standard of measuring BMR^(12,19,20). Therefore, we have adopted strict procedures including an overnight fast for 10–12 h immediate calibration prior to conducting the test according to the manufacturer’s guidelines and conducting the exam in a quiet, dark, thermoneutral room after awakening our participant⁽²⁰⁾. Additionally, the SCI-specific factor of 1·15 was used to ensure accurate reflection of the level of physical activity⁽²¹⁾.

The role of caloric intake and macronutrients on BMR is not well established after SCI. However, different macronutrient components may partially explain the variance in body composition parameters after SCI^(13–15). Percentage macronutrients of fat explained more than 1/3 of the variance in body composition, especially fat mass in persons with SCI⁽¹³⁾. Total protein intake (g) was negatively correlated with trunk visceral adiposity and positively related to fasting insulin after accounting for trunk muscles⁽¹⁴⁾. Furthermore, geometric framework analysis suggested that carbohydrate intake is important for body composition after SCI. Persons with SCI who consume lower than 150 g/d of carbohydrate intake are more likely to have greater subcutaneous adipose tissue⁽¹⁵⁾. This may suggest that the dynamic of adipose tissue storage is influenced by carbohydrate intakes⁽¹⁵⁾. In the same report, either macronutrients of protein or fats did not appear to influence the parameters of body composition after SCI⁽¹⁵⁾. Therefore, the current trial expanded our knowledge on how different macronutrients may influence energy balance and TEI after SCI.

High percentage macronutrients of protein explained 27 % of the variance in energy surplus compared with macronutrients of carbohydrates and fats. Furthermore, % macronutrients of protein negatively correlated with TEI. Previous work indicated that higher protein consumption is associated with increased satiety and increased thermogenesis and helps in the maintenance of FFM^(45,46). We presented macronutrients as a percentage of total caloric intake to account for the large differences in caloric intake between the negative and positive energy groups. The positive energy surplus group had a 1·7× greater caloric intake than the negative energy surplus. Another systematic review indicated that protein intake is likely to suppress appetite with either positive or no effects on total caloric intake in older adults⁽⁴⁷⁾. Compared with carbohydrate intake, protein consumption seems to suppress appetite in persons with SCI and reduce caloric intake^(16,37). Previously, we noted that heavier persons with SCI experience greater muscle hypertrophy following surface neuromuscular electrical stimulation-resistance training⁽⁴⁸⁾. However, there were no differences in percentage macronutrients of protein between heavier and leaner persons with SCI⁽⁴⁸⁾. We also noted that body weight mediates the relationship between serum testosterone and bone mineral density in persons with SCI⁽⁴⁹⁾. Accordingly, it is plausible that protein may serve as an anorexigenic stimulus to the brain centres of appetite control and decrease total caloric intake. Although the exact mechanism is still unclear, it may involve postprandial release of glucagon-like peptide 1 and peptide YY following protein consumption⁽⁵⁰⁾.

Several reports have questioned the accuracy and validity of dietary logs and indicated that caloric intakes are either overestimated or underestimated^(51,52). In the current trial, we relied on the average of the first 2 weeks from two different clinical trials. In these trials, participants had originally met with a dietitian to provide them with clear instructions on how to accurately enter dietary logs and maintain percentage macronutrients of 45 % carbohydrates, 30 % fats and 25 % protein^{(13–15,24,25)}. We have also measured the average of total caloric intake at weeks 5 and 6 and the average of weeks 7 and 8 to account for any potential underestimation in caloric intake in the negative energy group (see Figure 5). We noted that the caloric intake used to estimate TEE in the present study explained 42 and 52 % of the total caloric intake used in weeks 5 and 6 and weeks 7 and 8, respectively. Previously, Nightingale et al. estimated the inter-individual variability in TEI and TEE in persons with SCI⁽⁵²⁾. They noted that 1 d is reasonable to measure TEE and 4 d to measure TEI⁽⁵²⁾. The variability in measuring TEE is only 7 % (6–8 %) compared with 26 % (22–30 %) in the estimation of total TEI despite using 7-d dietary logs⁽⁵²⁾. We have previously found that using 3-d dietary logs is as reasonable as 5-d dietary logs when estimating TEI and macronutrients in persons with SCI⁽¹³⁾. Therefore, measuring TEI in persons with SCI appears to induce considerable variation that may have influenced the outcomes of the current trial.

Metabolic profiles have been examined between groups. Fasting insulin was 2× lower in the negative energy group compared with the positive energy group. The negative energy group is characterised by heavier body weight, greater abdominal circumferences (data not shown) and larger leg lean mass compared with the positive energy group. The dissociation between markers of central obesity and hyperinsulinemia may be explained by the fact that both groups have circumferential measurements below or within previously established cut-offs^(26,27). Circumferential cut-offs were previously established to distinguish those who are at risk of developing obesity and cardiometabolic disorders after SCI⁽²⁷⁾. However, the negative group had lower caloric intake and larger leg lean mass, which may have masked the risk of developing hyperinsulinemia independent of visceral adiposity after SCI^(26,27). Hyperinsulinemia is an independent risk factor of cardiometabolic disorders after SCI and in other clinical populations^(10,11,28). The results may shed light on the significance of monitoring TEI and providing rehabilitation regimens that are likely to evoke lower extremity muscle hypertrophy in persons with SCI^{(12,24,25,38,39)}.

Limitations

The primary limitation is the small number of participants enrolled in the current trial. SCI is a heterogeneous population with wide physical and injury characteristics that may influence the outcomes of findings. As mentioned, participants were enrolled in two separate clinical trials, and the instructions provided by the dietitian may have influenced their caloric intakes or percentage macronutrients^(24,25). Therefore, future trials may consider a non-convenient community-based sample to verify the findings of the current trial.

The current cross-sectional design is unlikely to assume causality, and a longitudinal trial with a longer follow-up is highly warranted to understand the dispersed energy balance in this population. Dietary habits and meal frequencies are not well studied in persons with SCI. Furthermore, reliance on fast-food v. home-based meals may have influenced the outcomes of the current trial. Koutrakis et al. examined 174 community-based participants and noted that level and completeness of SCI, time since injury, mobility, percentage total body fat, and other comorbid diseases were not associated with plasma 25(OH)D⁽⁵³⁾. We also noted that in forty-eight persons with SCI, 97 % failed to achieve the required daily allowance for vitamin D, which may suggest the need to make dietary coaching by a registered dietitian throughout the life span of the persons with SCI⁽²³⁾. Failure to control these factors may have confounded the outcomes of the current trial. Finally, we have intentionally excluded females from analysis because of the effect of gender differences on body composition and metabolic profile following SCI⁽⁵⁴⁾.

Conclusions

Based on the difference between TEE and estimated TEI, persons with SCI may be classified into either negative or positive energy surplus groups. Those in the negative energy group tended to have higher body weight with greater consumption in percentage macronutrients of protein, as well as lower fasting insulin. The findings may indicate that greater protein intake may reduce the total caloric intake relative to total energy expenditure. Unlike other macronutrients, the percentage macronutrient of protein appears to influence the variance in energy surplus. Additionally, TEI adjusted to FFM is considered the best predictor for manipulating energy surplus after SCI. Collectively, the findings may underscore the widespread variability in energy spectrum among persons with SCI and provide healthcare specialists with clinical implications for tailoring dietary programmes in this population.

Abbreviations:

AIS

American Spinal Injury Association Impairment Scale

DXA

dual-energy X-ray absorptiometry

FM

fat mass

FFM

fat-free mass

SCI

spinal cord injury

TEE

total energy expenditure

TEI

total energy intake