Investigating a relationship between body composition and spinal curvature in farmed adult New Zealand king salmon (Oncorhynchus tshawytscha): A novel application of dual-energy X-ray absorptiometry

Aquaculture 502 (2019) 48–55

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Investigating a relationship between body composition and spinal curvature in farmed adult New Zealand king salmon (Oncorhynchus tshawytscha): A novel application of dual-energy X-ray absorptiometry

T

Bailey A. Lovetta, , Elwyn C. Firthb, Lindsay D. Plankc, Jane E. Symondsd, Mark A. Preecee, Neill A. Herbertf ⁎

a

Institute of Marine Science, Science Centre 302, Room 394, The University of Auckland, 23 Symonds Street, Auckland 1050, New Zealand Liggins Institute, The University of Auckland, 85 Park Road, Grafton, Auckland 1023, New Zealand Department of Surgery, The University of Auckland, Auckland City Hospital, 2 Park Road, Grafton, Auckland 1023, New Zealand d Cawthron Institute, 98 Halifax Street, Nelson 7010, New Zealand e The New Zealand King Salmon Company Ltd., 43 Dublin Street, Picton 7220, New Zealand f Institute of Marine Science, Leigh Marine Laboratory, The University of Auckland, 160 Goat Island Road, Leigh 0985, New Zealand b c

ARTICLE INFO

ABSTRACT

Keywords: Fish pathology Spinal curvature King salmon Oncorhynchus tshawytscha DXA Body composition

Characterising the pathology of skeletal anomalies in farmed finfish is key to elucidating the underlying causes. Spinal curvature is frequently observed in farmed New Zealand king salmon (Oncorhynchus tshawytscha), but its cause is currently unknown and knowledge about its pathology is limited. Dual-energy X-ray absorptiometry (DXA) was used to investigate the relationship between spinal curvature and body composition in farmed adult New Zealand king salmon. Differences in fat mass (FM), lean mass (LM) and bone mineral content (BMC) between harvest-sized fish affected and unaffected by spinal curvature were evaluated. As there is presently no evidence of an association between spinal curvature and vertebral mineral loss, between-group differences in BMC were not anticipated. Conversely, as spinal anomalies can alter the swimming and feeding of affected fish, and existing research suggests a role for soft tissues in spinal curvature development, differences in FM and LM were expected. Whole-body and regional (cranial, trunco-cranial, trunco-caudal and caudal spine) measurements of percent FM, LM and BMC were obtained from lateral DXA scans of size-matched adult female king salmon (1890–6903 g) affected (n = 31) and unaffected (n = 31) by spinal curvature. Repeatability was comparable to previous DXA studies. Measurements of FM and LM by DXA and chemical carcass analysis (CCA) were highly similar. As frequently reported in previous animal-based studies, DXA-derived BMC values were significantly lower than those obtained from CCA. Contrary to the overarching hypothesis, there were no significant between-group differences in whole-body or regional FM, LM or BMC, suggesting that there was no relationship between body composition and spinal curvature. Though the relationship between soft tissue changes and spinal curvature remains to be determined, DXA appears to be a viable tool for non-invasive assessment of king salmon body composition. However, studying end-stage disease has recognised limitations, so future investigations should utilise longitudinal designs.

1. Introduction Spinal anomalies are frequently observed in finfish aquaculture and can negatively affect profitability (Vågsholm and Djupvik, 1998; Michie, 2001; Silverstone and Hammell, 2002; Waagbø et al., 2005; Sullivan et al., 2007; Branson and Turnbull, 2008). Affected individuals

are less acceptable to consumers so are typically downgraded, and incur further costs through increased processing labour (Silverstone and Hammell, 2002; Helland et al., 2005; Larssen and Djupvik, 2005; Branson and Turnbull, 2008; Fjelldal et al., 2012). Swimming and feeding, and thus growth, may also be compromised in fish affected by spinal anomalies, constituting an ethical concern (Toften and Jobling,

Corresponding author at: Institute of Marine Science, Science Centre 302, Room 394, The University of Auckland, 23 Symonds Street, Auckland 1050, New Zealand. E-mail addresses: [email protected] (B.A. Lovett), [email protected] (E.C. Firth), [email protected] (L.D. Plank), [email protected] (J.E. Symonds), [email protected] (M.A. Preece), [email protected] (N.A. Herbert). ⁎

https://doi.org/10.1016/j.aquaculture.2018.12.017 Received 5 July 2018; Received in revised form 9 October 2018; Accepted 6 December 2018 Available online 07 December 2018 0044-8486/ © 2018 Elsevier B.V. All rights reserved.

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1996; Kvellestad et al., 2000; Silverstone and Hammell, 2002; Fjelldal et al., 2007; Branson and Turnbull, 2008; Powell et al., 2009; Hansen et al., 2010; Fjelldal et al., 2012). However, spinal anomalies often arise from the action and interaction of multiple factors, including nutrition, genetics, rearing temperature, pollutants, growth rate, infectious agents, salinity, vaccination, light regime and parasitism (Vågsholm and Djupvik, 1998; Madsen and Dalsgaard, 1999; Silverstone and Hammell, 2002; Aunsmo et al., 2008; Witten et al., 2009). It is therefore often difficult to identify cause(s) and implement appropriate measures to optimise spinal health. Characterising the pathophysiology of spinal anomalies can provide important clues about their underlying drivers and requires examination of both mineralised and soft tissues. In farmed finfish, X-ray is most frequently used to diagnose spinal anomalies (Witten et al., 2009) but is only able to assess skeletal health (Bohndorf and Kilcoyne, 2002; Pergantou et al., 2006). Other methods such as histology can assess both bony and non-mineralised tissues, but are invasive and lethality is typically associated with sampling. An alternative is dual-energy X-ray absorptiometry (DXA), which non-invasively measures bone mineral, fat and lean mass (Slosman et al., 1992; Grier et al., 1996; Jebb, 1997; Lee and Gallagher, 2008; Stone and Turner, 2012). It has a high degree of accuracy and reproducibility and is easier to operate, more widely accessible and less expensive than more advanced imaging techniques such as MRI and quantitative CT (Lee and Gallagher, 2008). Although DXA was originally developed for diagnosing osteoporosis in humans (Albanese et al., 2003), technological advances have broadened its application to many non-human vertebrates, including rodents, pigs, monkeys, cats and dogs, birds, and reptiles (Grier et al., 1996; Stone and Turner, 2012). However, DXA has only recently been successfully applied to fish (Wood, 2004; Johnson et al., 2017). In farmed New Zealand king salmon (O. tshawytscha), spinal curvature is detected late in seawater production, but its cause is currently unknown (Munday et al., 2016; Perrott et al., 2018). The condition manifests as three phenotypes: lordosis (downwards deviation), kyphosis (upwards deviation) and scoliosis (lateral deviation), which can develop alone or in combination within an individual (Munday et al., 2016; Perrott et al., 2018). The curvatures typically increase in severity over time, thus by harvest affected individuals often have a highly distorted appearance, and are on average smaller than unaffected conspecifics (Perrott et al., 2018). Environmentally-induced skeletal malformations are widely considered to develop from either disruption of skeletal processes, in which the biochemical integrity of bone is compromised, or via neuromuscular pathways, where deviations develop in absence of vertebral chemical changes (Divanach et al., 1996; Lall and Lewis-McCrea, 2007; Witten et al., 2009). Presently, it remains to be determined whether spinal curvature in farmed New Zealand king salmon has a skeletal or neuromuscular origin. A recent histological study by Munday et al. (2016) found spinal curvature to be associated with perispinal muscle fibrosis but not with vertebral degeneration or mineral loss. Additionally, whilst Perrott et al. (2018) found that spinal curvature most frequently co-existed with vertebral compression, over 25% of affected fish possessed spinal curvature with no other confounding vertebral anomalies. Thus, whilst a skeletal basis for spinal curvature cannot be disregarded, existing observations are suggestive of a neuromuscular aetiology (Divanach et al., 1997; Lall and Lewis-McCrea, 2007). However, given the paucity of knowledge regarding spinal curvature pathology, particularly the role of soft tissue changes in its development, such inferences are speculative at this stage. In a novel application of DXA the present study investigated a relationship between body composition and spinal curvature by testing for differences in whole-body and regional fat mass (FM), lean mass (LM) and bone mineral content (BMC) between affected and unaffected fish. As there is presently no evidence of an association between spinal curvature and changes to vertebral structure and mineral content (Munday et al., 2016), between-group differences in BMC were not

anticipated. However, as spinal anomalies are known to alter the swimming and feeding capabilities of affected fish (Toften and Jobling, 1996; Branson and Turnbull, 2008; Powell et al., 2009), and there appears to be a role for soft tissues in the development of spinal curvature (Munday et al., 2016), differences in FM and LM were expected. These differences were predicted to be greatest in the trunk regions, where most of the swimming musculature is concentrated. 2. Materials & methods 2.1. Rearing and selection of experimental fish Sixty-two adult (2–2.5 years old) female king salmon (sourced from commercial all-female populations) were collected across six sampling events between April 2016 and November 2017 from three commercial sea cages, two located at Ruakaka Bay (41°11′38.121″ S 174°6′51.74″ E) (n = 56) and the third at Te Pangu Bay (41°15′19.369″ S 174°14′20.661 E) (n = 6), Marlborough Sounds, New Zealand. At both locations, fish were reared under a standard commercial production regime and fed to satiation on a commercially available pellet-based diet (per 100 g of feed: 44.21 g protein, 23.99 g fat, 16.86 g carbohydrate, 31.86% fishmeal, 7.1% fish oil, 1.24 mg/kg phosphorus (P), 1000 mg vitamin C). Fish were fed 10 meals per day for the first month of seawater production, 5 per day up to 250 g average body weight (BW), 3 per day from 250 g-1 kg BW, and 2 per day from 1 kg BW through to harvest. At each sampling event, fish were examined visually and palpated for detection of spinal curvature in the form of lordosis, kyphosis and/ or scoliosis (Fig. 1). Based on this assessment, fish were assigned to either an “affected” or “unaffected” category. An equal number of affected and unaffected fish were collected at each sampling event. Thus, at the culmination of sampling, the overall experimental population consisted of 31 affected and 31 unaffected fish (N = 62, 1890–6903 g body weight). Affected individuals were selected first, euthanized with an overdose of the aquatic anaesthetic Aqui-S® in accordance with standard industry protocols by trained on-farm staff, weighed (Tru-Test™ XR5000 digital scales), measured (fork length), labelled and placed on ice. All affected individuals had moderate-severe spinal curvatures, although observed phenotypes and their location along the spinal column varied among individuals. Curvature presence was assessed in four spinal regions: cranial, trunco-cranial, trunco-caudal, and caudal (Table 1), which corresponded anatomically to those previously used for radiographic skeletal anomaly scoring in farmed salmonids (Kacem et al., 1998; Aubin et al., 2005; Munday et al., 2016; Perrott et al., 2018). Each affected fish was then size-matched with an unaffected individual by weight (not length, since the distorted shape of affected fish could lead to inaccuracies in fork length measurements). Unaffected fish were euthanized, following which all fish were transported overnight on ice for DXA scanning. 2.2. DXA scanning Each fish was scanned in lateral (left-side down) and ventro-dorsal (VD) projections in small animal mode set to “medium” (100 kV, 0.188 mA, 10 μGy, model iDXA, software version 15, GE-Lunar, Madison, WI). All fish were scanned in both projections but, due to their fusiform shape, positioning fish upright for scanning in the VD projection was very difficult, an observation also made by Johnson et al. (2017). Anatomical boundaries were also very difficult to discern in VD scans, thus regions of interest (ROIs) were difficult to define. VD scanning was therefore abandoned and analysis was carried out on lateral scans only. Scanner repeatability, expressed as mean coefficients of variation (CV) from five repeated scans of three affected and three unaffected size-matched fish (n = 6, 5175–5420 g), was 0.15%, 0.24%, 49

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Fig. 1. Radiographic phenotype of adult king salmon unaffected (A) and affected (B) by spinal curvature. Fish B possesses serial repetition of lordosis (downwards curvature), kyphosis (upwards curvature) and scoliosis (lateral curvature) along the cranio-caudal axis of the spine.

0.67% and 7.67% for total mass (TM), lean mass (LM), fat mass (FM) and bone mineral content (BMC) respectively.

at −20 °C until analysis (n = 6, 1482–1685 g sample weight). Chemical carcass analysis was carried out according to standardised methods (AOAC, 2016). Tissue samples corresponding to ROI 7 from each fish were thawed, blended, freeze-dried, ground and ground subsamples were weighed. After heating at 105 °C overnight (16–18 h) and reweighing, moisture content of each subsample was determined as the difference between the initial and dehydrated weights (925.10/ 930.15, AOAC, 2016). After removal of all organic material at 550 °C, the weight of the remaining non-volatised material (ash content) was the mineral content of the sample (942.05, AOAC, 2016). Fat content was determined by the Soxtec procedure (991.36, AOAC, 2016). Subsamples were repeatedly washed with petroleum ether by reflux to dissolve the fat, which was then collected in a distillation flask and weighed. The increase in flask weight was taken as the fat content of the subsample. Moisture, fat and ash were reported as percentages of original mass of the subsample due to detection of a small but significant difference in TM of samples between DXA and CCA. Percent lean mass was subsequently calculated for each sample by subtracting the sum of % ash and % fat from 100. Results of the carcass analysis were then correlated with the corresponding DXA values to assess scanner accuracy.

2.3. DXA outcome measures Six custom ROIs (Fig. 2) applied to each scan provided whole-body and regional measures of TM, FM, LM and BMC. These six ROIs corresponded to major anatomical landmarks and to previous salmonid radiographic skeletal anomaly scoring protocols (Kacem et al., 1998; Aubin et al., 2005; Munday et al., 2016; Perrott et al., 2018), where 1 = whole body, 2 = head and pectoral fin girdle and rays and 3 = cranial (CR, vertebra V1–V8), 4 = trunco-cranial (T-CR,V9–V31), 5 = trunco-caudal (T-CA, V32–V49) and 6 = caudal (CA, V49 – most posterior vertebra) spinal regions. An additional seventh ROI (7, Fig. 2) encompassing the area between the dorsal fin and the articulation of the skull with the first vertebra was later added for comparison to chemical analysis values. 2.4. Comparison of DXA and chemical carcass analysis (CCA) measurements Accuracy was assessed using three affected and three unaffected size-matched fish (n = 6, 5175–5420 g body weight) by comparing DXA-derived measurements with those obtained from chemical carcass analysis (CCA). Following scanning, the area of tissue corresponding to ROI 7 (Fig. 2) was dissected from each fish, weighed, frozen and stored

2.5. Statistical analyses Statistical analyses were conducted using IBM SPSS Statistics 25. Significance was accepted at p < .05. Where normality was violated,

Table 1 Incidence of visually-detectable spinal curvature phenotypes. Numbers 1–4 in bold are indicative of cranial, trunco-cranial, trunco-caudal and caudal spinal regions. Incidence values represent the number of observations of each phenotype in each region for affected fish (n = 31). Spinal Curvature Phenotype

Lordosis

Kyphosis

Spinal region

CR

T-CR

T-CA

CA

CR

T-CR

T-CA

CA

CR

T-CR

T-CA

CA

Incidence (# of observations)

13

15

22

6

2

22

2

26

3

24

25

1

50

Scoliosis

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Fig. 2. Regions of interest (ROIs) for DXA-derived whole-body (1) and regional (2–6) measurements of percent FM, LM and BMC, and for comparison to chemical analysis values (7, highlighted).

non-parametric tests were performed. Five data points (one whole-body BMC, two trunco-cranial and two caudal BMC) were identified as outliers using boxplots and extreme value outputs, and removed prior to statistical analysis. Whole-body values for FM, LM and BMC for each fish were standardised and expressed as percentages of its TM prior to statistical analysis, to remove potential confounding effects of any between-group variations in TM. Similarly, regional percent FM, LM and BMC were calculated by standardising by the TM of each of the cranial (CR), trunco-cranial (T-CR), trunco-caudal (T-CA) and caudal (CA) spinal regions. Regional percent TM was calculated by dividing by whole-body TM. Independent t-tests were conducted to detect differences in wholebody FM, LM and BMC between affected and unaffected groups. Subsequently, to evaluate body composition differences specifically associated with the spine, t-tests were run using combined data from the four spinal regions, thus excluding any possible influence of the viscera or the head and pectoral fins (Region 2). Non-parametric MannWhitney U tests were then conducted on each of the four spinal regions separately to detect regional differences in TM, FM, LM and BMC between groups. To assess scanner accuracy, paired t-tests were used to compare DXA-derived LM, FM and BMC values with those obtained from chemical analysis. Independent samples t-tests were used to detect differences in DXA-CCA comparisons for TM and percent LM, FM and BMC/ ash between unaffected and affected fish.

Fig. 3. Percent whole-body FM (A), LM (B) and BMC (C) of fish affected (grey) and unaffected (white) by spinal curvature (N = 62, mean ± 95% CI).

3. Results 3.1. DXA whole-body analyses There were no between-group differences in FM, LM or BMC (Fig. 3; t%FM = 0.50, p = .62; t%LM = −0.50, p = .62; U%BMC = 420.00, p = .39). Combining all four spinal regions, and thus excluding region 2 and the viscera, did not alter the results; there were no between-group differences in FM, LM or BMC (t% FM = 0.59, p = .56; t% LM = −0.60, p = .55; t% BMC = 0.86, p = .40). 3.2. DXA regional analyses A between-group difference in percent contribution to TM existed in the cranial region (p = .03; Fig. 4), however no other regional 51

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original absolute data, and the results were unchanged – there were no significant differences in LM, FM or BMC between affected and unaffected fish. 3.3. Comparison of DXA and CCA measurements Differences between DXA measurements and CCA values for FM and LM were negligible, but DXA-derived values of BMC in both affected and unaffected fish were significantly lower (p = .01, 0.02 respectively) than ash values obtained from chemical analysis (Table 2).There were no significant differences in the DXA-CCA comparison for TM or percent LM, FM or BMC/ash between affected and unaffected fish. When affected and unaffected groups were pooled, DXA-derived TM and BMC were significantly lower than the corresponding CCA values (p = .01, < 0.01 respectively). 4. Discussion The results suggest that there was no relationship between spinal curvature and body composition in farmed adult New Zealand king salmon in the current study. Although the lack of between-group differences in BMC was expected, contrary to the original hypothesis there were no observed differences in FM and LM. s. TM in the cranial (CR) region was significantly higher in affected than unaffected fish (p = .03), and cranial BMC of affected fish was almost significantly lower than that of the unaffected group (p = .05). However, the absolute differences were < 0.5% (μTM = 0.40% and μBMC = 0.11%). Cranial curvatures are also often visually undetectable, even when severe, and the size of fish exclusively affected by curvatures in this region is generally similar to that of unaffected individuals. Thus, it is unclear if the observed differences are relevant to spinal curvature. The lack of between-group whole-body and regional differences in BMC supports the histological findings of Munday et al. (2016), who reported that spinal curvature was not associated with vertebral degradation or mineral loss. Additionally, Perrott et al. (2018) observed that while spinal curvature frequently co-existed with vertebral compression in harvest-sized king salmon, radiographically over 25% of individuals with spinal deviations possessed spinal curvature alone with no other confounding vertebral anomalies. While the former may reflect a possible skeletal basis for spinal curvature arising from vertebral compression, the frequent lack of co-incident vertebral anomalies appears to fit existing criteria for spinal anomalies with a neuromuscular origin. Indeed, spinal deviations which occur in the absence of chemical or secondary vertebral alterations are often considered to have a neuromuscular aetiology (Divanach et al., 1997; Lall and Lewis-McCrea, 2007; Witten et al., 2009). Observations of king salmon spinal anomalies from previous research (Munday et al., 2016; Perrott et al., 2018) and the current study therefore lend further support to the argument that spinal curvature could be a neuromuscular condition. However, existing published research has focused on characterising spinal curvature only at particular time-points in the seawater cycle (i.e. seawater transfer, grading) or exclusively at end-stage disease (harvest). Therefore, much remains unknown about its development throughout seawater production, particularly during the early stages which are critical periods for skeletal development. Additionally, with the exception of Munday et al. (2016), associations between spinal curvature development and changes to vertebral ultrastructure and the condition of non-mineralised perispinal tissues (i.e. skeletal muscle, nerves, ligaments, notochord) have yet to be investigated. Thus, until such detailed histological and longitudinal studies of both soft and mineralised tissues are conducted, suggestions about the aetiology of spinal curvature can only remain speculative. A neuromuscular aetiology for spinal curvature has been suggested but this is not yet confirmed, so it is important to remain open to all feasible possibilities as future lines of research are developed. Indeed, the absence of between-group differences in BMC is important because

Fig. 4. Percent contribution to TM (A) for cranial (CR), trunco-cranial (T-CR), trunco-caudal (T-CA) and caudal (CA) spinal regions, and percent FM (B), LM (C) and BMC (D) within each region, in fish affected (grey) and unaffected (white) by spinal curvature. (N = 62, mean ± 95% CI). *p < .05 indicates significant difference.

differences in TM (p = .19–0.99), LM (p = .41–0.66), FM (p = .49–0.59) or BMC (p = .05–0.70) were detected. Whole-body and regional analyses were also conducted using the 52

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3.91, 0.01* 0.43, 0.68 −1.21, 0.28 12.01, < 0.01* 162.23 ± 41.39 0.44 ± 1.00 −1.23 ± 1.02 0.81 ± 0.07 1694.03 ± 47.60 30.73 ± 1.36 68.03 ± 1.35 1.23 ± 0.07 1531.8 ± 39.90 30.29 ± 1.55 69.29 ± 1.58 0.41 ± 0.04 0.95, 0.40 0.45, 0.68 −0.42, 0.70 −0.16, 0.88 79.60 ± 83.57 0.98 ± 2.20 −0.93 ± 2.25 −0.03 ± 0.17 2.24, 0.16 −0.03, 0.98 −0.39, 0.74 6.74, 0.02* 122.43 ± 54.71 −0.05 ± 2.00 −0.78 ± 2.02 0.83 ± 0.12

Mean CCA Mean DXA Mean difference (Unaffected – Affected) Significance t, p Mean difference (CCA – DXA)

Significance t, p

All fish (n = 6) DXA-CCA Unaffected vs. Affected Affected

it could feasibly be explained by under-mineralisation during early production. The hatchery and early seawater phases are critical for salmon skeletal development and failure to achieve sufficient mineralisation during these periods has been associated with the onset of spinal anomalies in later life (Fjelldal et al., 2009; Fjelldal et al., 2012). However, it is important to note that under-mineralisation may not necessarily be evident at harvest (Baeverfjord et al., 1998). Indeed, Baeverfjord et al. (1998) found that feeding Atlantic salmon parr and post-smolts a P-deficient diet early in production resulted in the development of scoliosis-like spinal deviations several weeks later. Subsequently feeding the fish a P-sufficient diet boosted whole-body mineral levels to that of the control group, but the spinal anomalies persisted (Baeverfjord et al., 1998). Despite the absence of changes to bone mineral levels at harvest, Baeverfjord et al., 1998 were able to link the development of the spinal anomalies with under-mineralisation because they assessed the fish early in the experiment, when the undermineralisation occurred. In this study, fish were only assessed at harvest. Thus, whilst no between-group differences in BMC were detected, under-mineralisation at an earlier stage of production could still plausibly underpin the development of spinal curvature in king salmon. Future longitudinal studies will likely prove fruitful. Impairment of swimming and feeding is commonly observed in fish affected by spinal anomalies (Toften and Jobling, 1996; Branson and Turnbull, 2008). Consequently, between-group differences in FM and LM were expected, particularly in the trunco-cranial and trunco-caudal spinal regions (T-CR and T-CA in the current study) where the bulk of the anaerobic musculature is located. However, contrary to this hypothesis, no such differences were detected at either a whole-body or regional level. It is tempting to speculate that the feeding and swimming of affected fish were not impacted by spinal curvature but, in reality, this currently cannot be confirmed as the swimming behaviour of affected vs. unaffected fish was not monitored and the study had one time point. Alternative explanations should therefore be considered. Size-matching of affected and unaffected fish was carried out to reduce the confounding effect of body size differences, but this process may have inadvertently led to the lack of between-group differences in FM and LM. Reduced growth is commonly observed in king salmon affected by spinal curvature (Perrott et al., 2018), but often only following onset of the condition, suggesting it may be a secondary effect (authors' unpublished data). As such, individuals whose growth was compromised by spinal curvature were not represented in this study. However, examination of such affected individuals is important for elucidating why their growth (and potentially body composition) is impacted when that of other equally-deformed individuals, such as those in the current study, is not. Assessment at harvest alone cannot resolve this query, but future longitudinal studies may provide valuable insight. As external examination, not X-ray, was used to select the experimental fish, it is possible that some of the unaffected group had mild spinal curvature. Despite the research staff being highly experienced in diagnosing spinal curvature, mild malformations are known to pass through external assessments undetected (Fjelldal et al., 2012). However, even if individuals with slight spinal curvature were included, this is unlikely to be responsible for the lack of between-group body composition differences. Affected individuals possessed deformities of profound severity, which are known to adversely impact swimming and feeding (Toften and Jobling, 1996; Branson and Turnbull, 2008; Powell et al., 2009), and consequently growth and body condition (Hansen et al., 2010; Perrott et al., 2018). Thus, their FM and LM were expected to be significantly different from unaffected fish. Conversely, if fish in the unaffected group did have subtle curvature, it would be unlikely to have influenced their swimming and feeding. As such, their FM and LM would be more similar to truly unaffected fish than those in the affected group, so their presence in the unaffected group would not have confounded the observed results. Nevertheless, in future studies it would be preferable to use X-ray when selecting experimental fish.

TM (g) FM (%) LM (%) BMC/ash (%)

Mean difference (CCA – DXA)

Significance t, p

1694.37 ± 89.00 28.67 ± 2.19 70.17 ± 2.11 1.17 ± 0.09 1571.93 ± 59.70 28.72 ± 2.95 70.95 ± 2.98 0.33 ± 0.05 3.20, 0.085 1.04, 0.41 −1.72, 0.23 9.24, 0.01* 202.03 ± 63.17 0.92 ± 0.89 −1.69 ± 0.98 0.79 ± 0.09

Mean difference (CCA – DXA) Mean CCA

1693.70 ± 58.38 32.80 ± 0.38 65.90 ± 0.35 1.30 ± 0.12 1491.67 ± 52.77 31.87 ± 0.95 67.62 ± 0.94 0.49 ± 0.01 TM (g) FM (%) LM (%) BMC/ash (%)

Mean CCA Mean DXA Mean DXA

Significance t, p

Affected Unaffected

Table 2 Comparison of DXA and CCA measurements of TM and percent FM, LM and BMC/ash ± SE of ROI7 tissue samples from three affected and three unaffected size-matched fish (n = 6, 1482–1685 g sample weight). *p < .05 indicates significant difference.

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Despite the small sample size used in the current study, scanner repeatability and accuracy were acceptable and comparable to previous animal-based studies (Nagy and Clair, 2000; Hunter and Nagy, 2002; Wood, 2004; Stone and Turner, 2012; Johnson et al., 2017). There were also no differences in the DXA-CCA comparison for TM or percent LM, FM or BMC/ash between affected and unaffected fish. It is therefore unlikely that scanner error or operator variability contributed to the lack of observed between-group differences. Although DXA-derived BMC values were consistently lower than those obtained from CCA, this is a frequently reported discrepancy (Jayo et al., 1991; Koo et al., 1995; Black et al., 2001; Speakman et al., 2001; Hunter and Nagy, 2002; Swennen et al., 2004; Wood, 2004; Johnson et al., 2017). Whilst DXA measures the mineral content of exclusively bone, CCA-derived ash values are the cumulative mineral content of all cells in the sample (Johnson et al., 2017). Consequently, CCA is generally regarded as overestimating BMC compared to DXA, and thus is the most likely contributor to the observed disparity. The significant difference in pooled TM between CCA and DXA was likely the result of variation between the DXA scanner and the measuring equipment used by the laboratory where CCA was conducted, as CCA values for TM were consistently higher than those from DXA. However, as we accordingly standardised all body composition values by TM prior to analysis, this discrepancy had no effect on the results. Repeatability for BMC in the current study was low compared to that of TM, FM and LM and that reported in most other animal (nonfish)-based studies (Stone and Turner, 2012), as indicated by elevated mean CV values. The increased CV values for BMC were a consequence of high intra-individual variability in BMC, which is likely reflective of the very small contribution of BMC to TM in salmon. Salmon are a very fatty fish, and farmed salmon in particular are selectively bred to have a large volumes of musculature. Thus, the contribution of BMC to TM is typically < 1%. Any variation in measurements would therefore result in high mean CV values for BMC, as was observed in the current study. A similar result was reported by Johnson et al. (2017) for measurements of FM in channel catfish (Ictalurus punctatus), whereupon their low FM relative to TM (0–1%) produced high mean CV values (~30%). However, as the sample size used for accuracy validation within the study of Johnson et al. (2017) was large (n = 74), regression analyses were able to be conducted. By using the resulting prediction equations, mean CV for FM was able to be reduced to 5–5.5% (Johnson et al., 2017). Thus, with a larger sample size for comparisons between DXA and CCA, repeatability for DXA-derived BMC measurements in king salmon could be improved. This is the first time DXA has been applied to finfish pathology to evaluate the relationship between a spinal anomaly and body composition. As spinal curvature was evidently not associated with altered lean muscle, fat or bone mass in the current study, the relationship between soft tissue changes and spinal curvature in king salmon remains to be elucidated. However, given the apparent limitations associated with exclusively examining end-stage disease, future longitudinal studies will likely be valuable. Nevertheless, since there were no differences in composition detected between affected and unaffected fish, processed products (i.e. fillets) from fish with spinal curvature may still be sold as at a premium. Additionally, this work has demonstrated the viability of DXA for non-invasive assessment of salmon body composition, which may have significant value in future industry and research endeavours.

for their assistance during sampling events, and Tim from D & L Packaging for his guidance in packaging the fish appropriately for scanning. We are also grateful to The University of Auckland Body Composition Research Laboratory for use of their DXA scanner, the Massey University Nutrition Laboratory for conducting the chemical carcass analyses, and Troy Goodall (Troy Goodall Photography) for collating and optimising the images used in Fig. 1. The insights of Dr. Matthew Perrott (Massey University) regarding interpretation of the research findings were also greatly appreciated. Declarations of interest None. References Albanese, C.V., Diessel, E., Genant, H.K., 2003. Clinical applications of body composition measurements using DXA. J. Clin. Densitom. 6 (2), 75–85. AOAC, 2016. Official Methods of Analysis of AOAC International, 20th ed. AOAC International, Rockville, MD. Aubin, J., Gatesoupe, F.J., Labbé, L., Lebrun, L., 2005. 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Acknowledgements This research was funded by The University of Auckland, New Zealand Food and Health Programme [grant number 3708972], New Zealand. B. A. Lovett is also supported by a Callaghan Innovation, New Zealand R & D Student Fellowship [grant number NZKSC1401]. We thank the New Zealand King Salmon Company Ltd. for providing fish for this work, Dr. Seumas Walker, Stuart Barnes and Graeme Aldridge 54

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