Physical and psychological symptoms after a 135-day-long ocean-going voyage
To investigate major symptoms during the ocean-going voyage we used questionnaires at the beginning (day 1) and end (day 135) of the voyage. Only data from male crew members (Han nationality, China) aged 20 to 35 were analyzed statistically as they were the predominant group and this avoided the influence of age, gender and nationality. The questionnaire consisted of three parts (Method): (1) Physical indicators including body mass index (BMI) and the incidence of backache, headache, stomach ache, pectoralgia, muscular soreness, dyspnea and palpitation; (2) Psychological indicators including insomnia, shallow sleep, over-eating, nausea, inactivity, poor appetite, and also feelings of loneliness, depression, self-accusation and mistrustfulness; (3) Defecation related indicators including stool type, incidence of bloody stools, incomplete defecation, bowel difficulties, bowel movement (but without defecation), and defecation frequency (weekly).
In terms of physical symptoms (Fig. 1a), the highest incidence amongst crew members was for backache (46.8%) and headache (44.2%) which increased by 40.3% and 39.0%, respectively, compared to the baseline values (6.5%, 5.2%). The incidence of muscular soreness (35.1%) and stomachache (33.8%) were the next most common symptoms, with an increase of 27.3% and 29.9% respectively compared to the baseline (7.8% and 3.9%). After the voyage, 20.8% of the crew members were suffering palpitations, 19.5% had dyspnea and 19.5% had pectoralgia, the incidence rates of which had increased by 18.2%, 19.5% and 13.0%, respectively. However, the body mass index (BMI) of all crew members did not change significantly during the voyage.
As for the psychological situation (Fig. 1b), the major psychological problems reported by crew members were shallow sleep (77.9%) and insomnia (66.2%) while few had sleeping problems before the voyage. Moreover, the prevalence of over-eating (55.8%), inactivity (55.8%) and nausea (50.7%) in more than half the crew members, had increased by 46.8%, 44.2% and 48.1%, respectively compared to the baseline. In addition, crew members also suffered from poor appetite (49.4%), feeling lonely (44.2%), depression (36.4%), self-accusation (31.2%) and mistrustfulness (26.0%), each of which had increased by 33.8%, 40.3%, 31.2%, 23.4% and 24.7%, respectively. These symptoms suggest a high level of mental stress as a result of the semi-closed living and working environment during the ocean-going voyage.
Beyond physical and psychological symptoms, we found that defecation was abnormal for most crew members (Fig. 1c). More than half (53.3%) of the crew members had an abnormal frequency of defecation (weekly) compared to only 1.3% at baseline. In addition, 42.9% of crew had incomplete defecation (a 29.9% increase from baseline) and/or bowel movements that did not result in defecation (a 16.9% increase from baseline). Furthermore, 42.9% had bowel difficulties, which was an increase of 10.4% compared with baseline (32.5%). Notably, 13.0% of crew members had bloody stool. Together these results indicate a sub-optimal health status amongst crew members as a result of the ocean-going voyage and including multiple physical and psychological symptoms as well as defecation disorders.
Definition of Ocean-Going Syndrome
Although the extreme living and working environment endured by the crew was the same, it has resulted in different symptoms in different individuals. To explore the correlation among the symptoms, we conducted cluster analysis on the change of physical, psychological and defecation indicators of crew members during the ocean-going voyage (Fig. 1d, Method). We found that the change of those symptoms were clustered into four distinct groups: Group A: the crews suffering from abnormal defecation frequency (weekly), insomnia, shallow sleep, nausea and over-eating; Group B: in addition to the symptoms of Group A, some of the crews also reported to have stomach ache, headache, incomplete defecation, backache and self-accusation; Group C: in addition to the symptoms of Group A, a part of the crews also have poor appetite, abnormal stool type (e.g. watery stool), bloody stool, bowel difficulties, bowel movements (without defecation), muscular soreness, feeling of tiredness leading to inactivity, feeling depressed, palpitations, pectoralgia and mistrustfulness; Group D: the crew members that feels lonely and dyspnea instead of symptoms in other groups. Amongst the four groups, the symptoms of Group A had the highest incidence and were most strongly correlated with each other, followed by the symptoms of Group B and Group C; the incidence of symptoms in A, B and C showed a gradually decreasing (Fig. 1e). Thus, we considered that the co-appearance of symptoms in Group A (e.g. abnormal defecation frequency, insomnia, shallow sleep, nausea and overeating) as core characteristic of this newly-discovered sub-optimal health status for long voyage crews, which we name here as ‘Ocean-Going Syndrome (OGS)’. In addition to the core symptoms that almost every OGS crew suffers from, OGS crews also have a chance of suffering from symptoms in Group B and Group C.
Association between the gut microbiome and the Ocean-Going Syndrome
To explore any relationship between the OGS and the gut microbiome, we collected stool samples and sequenced the microbiome within them at two time points i.e. at day 1 (before voyage) and day 135 (after voyage) using WMS (Illumina HiSeq 2500). After quality control, each sample contained a mean of 22,255,766 clean reads which were profiled using mOTUs2 and HUMAnN2 to identify the taxonomical and functional structure of the gut microbiome (Method).
Then, we used Permanova test to determine the effect size of the OGS on the gut microbiome of crew members. Although Host ID had the greatest influence on the gut microbiome (Bray-Curtis distance, BCD: F = 2.690, p = 0.001; root Jensen Shannon distance, rJSD: F = 2.520, p = 0.001), the influence of the OGS as a factor on the gut microbiome was significant (Table 1, BCD: F = 1.946, p = 0.020; rJSD: F = 1.810, p = 0.024) and its significance was greater than the influence of time point (BCD: F = 1.762, p = 0.043; rJSD: F = 1.481, p = 0.063). Notably, based on Permanova, no effect sizes for any symptoms were positive (Table 1), suggesting only a weak influence of single symptom on the gut microbiome during the voyage. The above results indicated that the crews’ gut microbiome associated with a group of symptoms i.e. the OGS instead of any single symptom.
Table 1
Permanova test on the gut microbiome using different physical and psychological health indicators (symptoms) as factors.
Factors | Bray-Curtis | rJSD |
Adonis.F | Adonis.P | Adonis.F | Adonis.P |
Host ID | 2.689 | 0.001 | 2.523 | 0.001 |
Ocean-going syndrome | 1.946 | 0.020 | 1.810 | 0.024 |
Time points | 1.762 | 0.043 | 1.481 | 0.063 |
Self-accusation | 1.588 | 0.072 | 1.375 | 0.100 |
Feeling depressed | 1.585 | 0.054 | 1.424 | 0.083 |
Dyspnea | 1.519 | 0.086 | 1.418 | 0.085 |
Bloody stool | 1.464 | 0.081 | 1.354 | 0.096 |
Bowel difficulties | 1.405 | 0.136 | 1.394 | 0.104 |
Backache | 1.324 | 0.147 | 1.186 | 0.189 |
Defecation frequency | 1.290 | 0.159 | 1.137 | 0.245 |
Insomnia | 1.181 | 0.222 | 1.082 | 0.314 |
Palpitations | 1.169 | 0.257 | 1.142 | 0.229 |
Inactivity | 1.167 | 0.259 | 1.199 | 0.191 |
Shallow sleep | 1.157 | 0.237 | 1.042 | 0.347 |
BMI | 1.130 | 0.282 | 1.072 | 0.322 |
Nausea | 1.115 | 0.298 | 0.967 | 0.487 |
Overeating | 0.978 | 0.431 | 1.006 | 0.379 |
Stool type | 0.934 | 0.497 | 0.863 | 0.649 |
Mistrustfulness | 0.913 | 0.527 | 0.950 | 0.488 |
Pectoralgia | 0.913 | 0.533 | 0.904 | 0.572 |
Headache | 0.903 | 0.552 | 1.008 | 0.383 |
Poor appetite | 0.858 | 0.603 | 0.974 | 0.442 |
Incomplete defecation | 0.855 | 0.594 | 0.867 | 0.616 |
Bowel movement | 0.845 | 0.631 | 0.961 | 0.456 |
Feeling lonely | 0.841 | 0.637 | 0.841 | 0.679 |
Stomach ache | 0.811 | 0.662 | 0.917 | 0.540 |
Muscular soreness | 0.565 | 0.955 | 0.701 | 0.912 |
Ocean-Going Syndrome as a key factor for individual perturbations during the voyage
Given individual (Host ID) has the biggest effect size on the gut microbiome, we sought to investigate the individual perturbations during the voyage. Although there was no significant difference in alpha diversity (Shannon index, p = 0.73) nor obvious clustering in PCoA (Fig. 2a) in the gut microbiome between day 1 and day 135, individual perturbation between the two time points, indicated by BCD and rJSD, showed a significant change in the gut microbiome (Fig. 2b); this indicates a profound change in the gut microbiome within individual during the voyage.
Then we compared individual perturbations in the gut microbiome betweem crew members suffering the OGS (individuals that clustered in Group A, Fig. 1d) with non-OGS (crew members that not clustered in Group A, Fig. 1d) and found that the former were significantly different to the latter (BCD: p = 0.024; rJSD: p = 0.021, Fig. 2c). These results demonstrate a strong association between the OGS and individual change of the gut microbiome during the voyage. However, such correlation was not found between the gut microbiome and any single symptom. Specifically, crew members were grouped by symptom (e.g. shallow sleep vs. symptomless) and the individual perturbations were grouped accordingly and compared (Fig. S1, Method). There was no strong correlation between any physical or psychological symptom and individual perturbation in the gut microbiome during the voyage, suggesting the crews with OGS had greater individual perturbations in the gut microbiome during the voyage, and no single symptom can introduce such significant perturbations.
Microbial signature for Ocean-Going Syndrome during the voyage
To identify the microbial biomarkers contributing to the development of the OGS, we evaluated how microbial species and their functions differed in abundance and expression, respectively, between the OGS group (the crew members in Group A) and the non-OGS group from beginning to the end of the voyage (Fig. 3a, Method). Overall, 15 species were significantly enriched (p < 0.05) in the OGS group compare to the non-OGS group during voyage: Bifidobacterium pseudocatenulatum (p = 0.010), Clostridium sp. 3680 (p = 0.029), Collinsella aerofaciens (p = 0.008), Coprococcus comes (p = 0.010), Dorea longicatena (p = 0.001), Enterobacteriaceae sp. 96 (p = 0.043), Eubacterium ramulus (p = 0.030), Eubacterium sp CAG 180 (p = 0.020), Faecalibacterium sp. 12325 (p = 0.016), Firmicutes bacterium CAG 227 (p = 0.043), Firmicutes sp. 3641 (p = 0.044), Ruminococcaceae sp. 12259 (p = 0.004), Ruminococcus sp. CAG 254 (p = 0.031), Ruminococcus torques (p = 0.004) and Sutterella wadsworthensis (p = 0.013). In contrast, the abundance of four species decreased in the OGS group compare to the non-OGS group during the voyage e.g. Faecalibacterium sp. 12303 (p = 0.044), Clostridium sp. CAG 138 (p = 0.021), Clostridium sp. CAG 217 (p = 0.028) and Bacteroides coprocola (p = 0.019).
As for differential microbial functions, the abundance of 31 gene families from 14 pathways was found decreasing in the OGS group compare to the non-OGS group during the voyage. These pathways involved (Fig. 3a): Protein families: genetic information processing (09182); Protein families: metabolism (09181); Protein families: signaling and cellular processes (09183); Membrane transport (09131); Signal transduction (09132); Translation (09122); Infectious disease: bacterial (09171); Amino acid metabolism (09105); Carbohydrate metabolism (09101); Global and overview maps (09105); Lipid metabolism (09103); Poorly characterized (09194); Unclassified: signaling and cellular processes (09193), and Immune system (09151). These suggested that a microbial signature can be captured in the OGS crews during the voyage, which can be quantified as change of 15 taxonomical and 31 functional biomarkers.
To better understand the microbial dynamic difference between the OGS and non-OGS groups, co-occurrence network analysis was employed for the stool samples at both before and after the voyage (Fig. S2, Table S2, Method). Taking relative abundance > 0.001, prevalence > 10%, and p = 0.01 as the threshold, we compared the nodes, edges and density of the four co-occurrence networks (Fig. 3b). We found that: (1) before the voyage at day 1, the OGS and non-OGS shared a large number (170/190 and 170/199 respectively) of nodes while the edges based on those nodes are barely overlapped (5/237 and 5/216 respectively); (2) after the voyage at day 135, similarly, the OGS and non-OGS shared a large number (179/198 and 179/200 respectively) of nodes and the edges still barely overlapped (5/227 and 5/253 respectively); (3) from day 1 to day 135, within the OGS and non-OGS group, we observed a large of shared nodes but limited shared edges as well. On the other hand, we found a decreasing of network density (from 0.013 to 0.012) in the OGS group and an increasing of network density (from 0.011 to 0.013) in the non-OGS group from day 1 to day 135. Collectively, the above results suggest that the OGS and non-OGS have unique microbial dynamic, and the correlation of microbial species in the two groups are different even at the beginning of the voyage.
Prediction of Ocean-Going Syndrome before the voyage
To explore whether the OGS can be predicted, we investigated the microbial compositions of the OGS before the voyage. Although Permanova test showed that there was no influence of the OGS on overall taxonomic structure (BCD: F = 1.23, p = 0.215; rJSD: F = 1.169, p = 0.241) or functional structure (BCD: F = 0.676, p = 0.648; rJSD: F = 0.687, p = 0.691) before the voyage, six species (Bacteroides massiliensis (p = 0.028), Parabacteroides distasonis (p = 0.019), Eubacterium rectale (p = 0.035), Bacteroides rodentium (p = 0.023), Clostridiales sp. 12229 (p = 0.035), Bacteroides stercoris (p = 0.029)) and two gene families (Protein families: genetic information processing (09182, p = 0.035); Membrane transport (09130, p = 0.040)) were identified as biomarkers between the OGS and non-OGS at baseline. The differential abundance of the above taxon and functions can potentially be used to predict the onset of the OGS at baseline.
Moreover, to explored whether the OGS could be predicted, Random Forest was used to build a prediction model that distinguished between the OGS group and the non-OGS group based on microbial taxonomic and functional profiling of samples taken before the voyage began. Performance improvement was minimal once the top 27 most discriminatory species and functions were included (Table S3, Fig. 4a). Ultimately samples from the OGS could be distinguished from non-OGS samples with 87.0% accuracy (ten-fold cross validation AUC = 0.91, Fig. 4b). On the other hand, RF models using baseline data from microbial species, microbial functions, or questionnaire separately could only predict the onset of the OGS with 80.5% (AUC = 0.85), 83.1% (AUC = 0.85) and 68.8% (AUC = 0.67) accuracy (Fig. 4b). Thus, biomarkers from baseline gut microbiome data alone can be used to predict the likelihood of the OGS developing, without the need for determining physical or psychological status.