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. 2021 Feb 16;16(2):e0247113. doi: 10.1371/journal.pone.0247113

Effect of sex/gender on obesity traits in Canadian first year university students: The GENEiUS study

Tanmay Sharma 1, Rita E Morassut 1, Christine Langlois 1, David Meyre 1,2,*
Editor: Mauro Lombardo3
PMCID: PMC7886219  PMID: 33592058

Abstract

Background

While weight gain during first year of university has been well documented in North America, literature on sex-specific effects is scarce and inconsistent. The objective of this investigation was to explore sex-specific changes in obesity traits during first year of university at McMaster University (Ontario, Canada).

Methods

245 first-year students (80.4% females) were followed longitudinally with data collected early in the academic year and towards the end of the year. Obesity parameters including weight, waist and hip circumferences, BMI, and waist to hip ratio were investigated. The Mann-Whitney U test and the Wilcoxon signed-rank test were used for pairwise comparison of traits in the absence of adjustments. Additionally, the repeated-measures ANOVA test was used with covariate adjustments to investigate the interaction between sex and time.

Results

Overall sample trends indicated a significant increase in mean weight by 1.55 kg (95% CI: 1.24–1.86) over the school year (p<0.001). This was accompanied by significant gains in BMI, and waist and hip circumferences (p<0.001) in the overall sample. At baseline, males presented with higher body weight, BMI, waist and hip circumferences, and WHR, as compared to their females counterparts (p<0.01). Additionally, sex-stratified analysis indicated significant gains in weight, BMI, and waist and hip circumferences in both males and females (p<0.01). However, a comparison of the magnitude of change over time between the two sex groups revealed no significant difference for any of the investigated traits (p>0.05).

Conclusion

While our study confirms significant weight gain in both male and female first year university students in Ontario, Canada, it does not show sex specific differences within this context. Our investigation highlights the importance of accounting for sex and gender in health research and supports the need of further studies in this area.

Introduction

According to recent World Health Organization (WHO) estimates, the global prevalence of obesity and overweight has nearly tripled over the last four decades with over one-third of the global population now having an estimated body mass index (BMI) above 25 kg/m2. Canada is one of several high-income countries that has a high prevalence of obesity and overweight [1]. Data from the 2018 Canadian Community Health Survey (CCHS) indicate that approximately 63.1% of Canadians are either overweight or obese, with a higher proportion of males (69.4%) being affected than females (56.7%) on average. Interestingly, when examining the CCHS estimates of obesity/overweight prevalence in Canadians between 2010 and 2018, a considerable increase from approximately 23.24% to 31.2% is noted amongst individuals aged 18–19. This is highly concerning as previous studies have implicated elevated BMI during adolescence and young adulthood as an important risk factor for chronic obesity and other secondary complications later in life including higher morbidity and early mortality [2–5]. Despite the availability of different therapeutic interventions, ranging from diet adjustments to bariatric surgery, treatment of obesity remains a biomedical and public health challenge due to its multifactorial pathogenesis and, as such, the disorder tends to persist as a chronic condition in most affected individuals [6,7]. In that context, further research in this area for a better understanding of the disorder is critical to optimize the prediction, prevention, and treatment of obesity [8].

The period between the age of 17 and 25 years, sometimes referred to as “young adulthood”, encompasses important transition events for many young adults, one of which includes starting post-secondary education [9]. Interestingly, while education status is negatively correlated with BMI in the general population from high-income countries, students pursuing post-secondary education have been shown to be at greater risk for weight gain than those not pursuing university education in the United States [10–13]. The “Freshman 15” is a popular belief that undergraduate university students gain 15 pounds (6.8 kg) during their first year of university studies [10,11]. While previous studies have supported the theory of weight gain during first year of university, the amount gained has been reported to be more modest at approximately 3 to 5 lbs (1.4 to 2.3 kg) [14–16]. This increase can be partially attributed to changes in environmental exposures and lifestyle habits, such as unhealthy diet, decreased physical activity, and increased sedentary behavior, which are usually observed during transition to university [17–20]. However, not everyone exposed to this ‘obesogenic’ environment becomes obese [21]. Several biological factors such as in utero programming, gut microbiome, epigenetics and genetics, can modulate an individual’s susceptibility to weight gain and can help explain a portion of the observed inter-individual anthropometric variance [21,22]. Sex/gender (hereafter referred to as sex) represents an important risk factor at the interface of biology and environment, comprising of a set of biological and sociological constructs [23,24].

Previous research examining obesity traits in post-secondary students indicates that males present with a higher BMI than their female counterparts on average [25,26]. However, the literature on the effect of sex on weight gain during the academic year is mixed [25,27,28]. Canadian studies within this context have been relatively limited and have also reported contradictory results [29–32]. While some reports have indicated sex-specific anthropometric change in first year Canadian university students, others have found no significant differences [29–32]. Most recently, Beaudry et al. reported a significant sex effect amongst first year students at a university in Ontario, showing that male students gain about twice as much weight as their female counterparts [29]. This has important implications as this report implicates sex as an important risk factor to be taken into consideration for prevention efforts [29]. Since we recruited first year students at a different university campus in Ontario, we carried out a follow-up investigation and attempted to replicate this observation in a cohort of 245 undergraduate students at McMaster University.

Methods

Study design and participants

Genetic and EnviroNmental Effects on weight in University Students (GENEiUS) is a prospective observational study which investigates the environmental and biological determinants of obesity trait changes in Canadian undergraduate students [8]. Undergraduate students from McMaster University (Hamilton, Ontario) are followed every six months over four years beginning in September of their first year of study. First year undergraduate students were primarily recruited via in-class advertising on main university campus and through social media promotion. First year students enrolled at McMaster University between the ages of 17 and 25 are eligible to participate in the study. Individuals who are pregnant, have given birth, or have a medical condition which can impact BMI for a long period of time (e.g. bariatric surgery, immobilization from injury) have been excluded from the study. Additional details regarding the GENEiUS study have been described previously [8]. Written informed consent was obtained directly from the participants. All methods and procedures for this study were in accordance with the Declaration of Helsinki principles and were reviewed and approved by the Hamilton Integrated Research Ethics Board (REB#0524).

Data collection

Four cohorts of participants (2015–2016, 2016–2017, 2017–2018, 2018–2019) were followed longitudinally with data collected at two study visits: the beginning of their first-year (September/October) and towards the end of their first-year (March/April). A total of 361 participants were enrolled in the study. Two-hundred forty-five of them completed the baseline and follow-up visits and represent the analyzed sample in this report (N = 245; 80.4% females: 19.6% males). Data analyzed in this study included anthropometrics (weight, BMI, waist and hip circumference, waist to hip ratio), and demographics (sex, ethnicity, living arrangement, program of study). Anthropometric traits, including weight, height, waist circumference (WC), hip circumference (HC), were measured at baseline (September/October) and again at 6 months post-baseline (March/April). Weight was measured to the nearest 0.1 kg using a digital scale (Seca, Hamburg, Germany) and height was measured to the nearest 0.1 cm using a portable stadiometer (Seca 225, Hamburg, Germany). WC was measured at the midpoint of the last palpable rib and the superior portion of the iliac crest to the nearest 0.1 cm and HC was measured at the widest part of the buttocks to the nearest 0.1 cm using a stretch-resistant tape measure, in accordance with WHO guidelines. All anthropometric measurements were performed by trained research assistants. Additional obesity trait outcomes, including BMI and waist to hip ratio (WHR), were calculated. BMI (kg/m2) was calculated by dividing weight by squared height and WHR was calculated by dividing WC by HC. Information about demographic characteristics was collected at the first appointment using an online, self-reported questionnaire.

Statistical analysis

All statistical analyses were performed using IBM SPSS Version 25 statistical package. Descriptive analysis was carried out to assess the preliminary distribution of traits within the study sample. Data for continuous variables have been reported using means and SD while categorical data have been reported by counts and percentages. Anthropometric data at each time point were screened for potential outliers. Any identified outlying data points were individually cross-checked to determine if they were true outliers, representing participants who truly fell outside the general distribution of our data, or if the outliers were a result of inaccuracies in measurement or data transcription. Data inaccuracies were corrected while all other outliers were left in the dataset. All data were assessed graphically and statistically for normality of distribution prior to analysis. The Mann-Whitney U test was used for a pairwise comparison of outcomes at baseline between males and females, in absence of adjustments for covariates. The Wilcoxon signed-rank test was used for a pairwise comparison of change in obesity traits from the beginning to the end of first year university, in absence of adjustments for covariates. The effect of sex on anthropometric change was assessed using a repeated measures analysis of variance (RMANOVA) [33]. An inverse normal rank transformation was applied to the anthropometric data for each time point, as previously reported [34,35]. Transformation resulted in the normality of the distribution for the anthropometric data. Different covariates including cohort of recruitment (i.e. 2015–2016, 2016–2017, 2017–2018, 2018–2019), faculty of study (i.e. science vs. non-science), and living arrangement (i.e. living in residence on campus, living at home, living in student housing off campus) were tested separately in each RMANOVA model. In this case, we followed the covariate adjustment strategy used by Beaudry et al. for the available traits [29]. As such, the covariates were only retained if their interaction with time was significant or marginally significant (p<0.1), otherwise reduced models were presented. Partial eta-squared values (η2) from the RMANOVA were also presented as a measure of effect size [36]. Based on the fact that i) the present study is hypothesis-driven; ii) the research questions have been previously tested in literature; iii) tested obesity outcomes are not independent; applying a Bonferroni correction reduces the chance of making type I errors, but increases the chance of making type II errors [37,38]. Therefore, the level of statistical significance was set at p <0.05 for all tests.

Results

Participant characteristics

A total of 361 participants were enrolled into the study between 2015 and 2018 of which 245 (68%) completed one year of follow up (i.e. completed the first baseline visit around September/October and a second follow-up visit in March/April) between 2016 and 2019. The 245 participants that completed one year of follow up represent the analyzed sample in this report. The mean length of time between the baseline and follow-up visits was 21.6 weeks (SD = 2.18). Participants displayed an average age of 17.87 years (SD = 0.48) and female participants accounted for 80.4% of the analyzed sample (n = 197). Thirty one percent of the participants were East Asian (n = 76), 24.9% were white Caucasian (n = 61), 18.8% were South Asian (n = 46), 12.7% were mixed (n = 31), 6.9% were Middle Eastern (n = 17), and 5.7% (n = 14) collectively belonged to other ethnicity groups including African, Latin American, and Pacific Islander. In terms of living arrangement, 69.4% percent of the sample reported living in university residence on campus (n = 170), 19.6% reported living at home with family (n = 48), and 10.6% reported living in a student house off campus (n = 26). Among those who reported their program of study, 86.2% reported being enrolled in a science based academic program (e.g. Health Science, Life Science, Kinesiology, Engineering) while 13.8% reported being in enrolled a non-science academic program (e.g. Humanities, Business, Arts).

Anthropometric patterns in first year of university: Overall trends

Early on in the academic year (i.e. at baseline), the average body weight, BMI, WC, HC, and WHR for the overall sample was 60.42 kg (SD = 11.98), 21.52 kg/m2 (SD = 3.34), 75.08 cm (SD = 8.69), 97.18 cm (SD = 7.73), and 0.772 (SD = 0.050) respectively. Approximately 78.4% (n = 192) of the participants had a normal BMI, 12.2% were underweight (n = 30), 6.5% were overweight (n = 16), and 2.9% (n = 7) were obese. By the end of the academic year, an average increase was noted across all anthropometric traits when compared to earlier on in the year. Table 1 summarizes the aggregated anthropometric data at each time point. There was a significant increase in average body weight, by 1.55 kg (3.4 pounds; p<0.001), and in mean BMI, from 21.52 kg/m2 to 22.16 kg/m2 between the two time points (+0.64 kg/m2, p<0.001). Notably, however, the mean BMI at both time points remained below 25 kg/m2 indicating that the sample, on average, remained within the ‘normal weight’ category throughout the year. WC and HC also increased significantly, by 1.14 cm (p<0.001) and 0.93 cm (p<0.001) respectively. While a modest rise in WHR was noted between the two time points, it did not reach the threshold for statistical significance (P = 0.083). There was no significant difference in anthropometric change (i.e. change in weight, BMI, WC, HC, and WHR) between those who were followed for less than, or more than, the average follow-up time (21.6 weeks). In terms of their BMI categories, by the end of the academic year 77.1% (n = 189) of the participants were in the normal weight range, 8.6% were underweight (n = 21), 11.4% were overweight (n = 28), and 2.9% (n = 7) were obese.

Table 1. Overall trends in first year of university.

Beginning Mean (SD) End Mean (SD) Change MD (95% CI) P-value*
Body Weight (kg) 60.42 (11.98) 61.97 (12.39) 1.55 (1.24–1.86) <0.001
BMI (kg/m2) 21.52 (3.34) 22.16 (3.45) 0.65 (0.53–0.76) <0.001
Waist Circumference (cm) 75.08 (8.69) 76.27 (8.99) 1.14 (0.63–1.66) <0.001
Hip Circumference (cm) 97.18 (7.73) 98.11 (7.44) 0.93 (0.55–1.31) <0.001
WHR 0.772 (0.050) 0.776 (0.054) 0.004 (-0.001–0.009) 0.083
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Data are expressed as mean (SD) and mean difference (95% CI); Abbreviations: BMI, body mass index; WHR, Waist to hip ratio; MD, Mean difference. *Pairwise comparison via Wilcoxon sign rank test (non-adjusted analysis of change in outcomes from the beginning to the end of the school year). P-values below 0.05 represented in bold font.

Sex-specific trends: Anthropometric presentation at baseline

Table 2 presents the sex-specific trends across all anthropometric traits at the beginning of the year (i.e. baseline). Overall, males presented with larger body weight (p<0.001), higher BMI (p = 0.008), larger WC (p<0.001), larger HC (p<0.001), and a higher WHR (p<0.001) at baseline, as compared to their females counterparts.

Table 2. Baseline differences between Male (n = 48) and Female (n = 197) students at the beginning of the 1st year.

Anthropometric Trait Beginning of the Year Mean (SD) P-value*
Body Weight (kg) Males 71.37 (12.68) <0.001
Females 57.76 (10.18)
BMI (kg/m2) Males 22.62 (3.79) 0.008
Females 21.25 (3.18)
Waist Circumference (cm) Males 81.38 (9.28) <0.001
Females 73.55 (7.83)
Hip Circumference (cm) Males 100.56 (7.57) <0.001
Females 96.36 (7.56)
WHR Males 0.808 (0.040) <0.001
Females 0.763 (0.048)
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All data presented as mean (SD); Abbreviations: BMI, body mass index; WHR, Waist to hip ratio. *Non-parametric comparison via Mann Whitney U test (non-adjusted comparison of males vs. females at baseline). P-values below 0.05 represented in bold font.

Sex-specific trends: Anthropometric changes in first year of university

In terms of change from the beginning to the end of the year, both sexes saw an increase across all measured anthropometric characteristics. Separate subgroup analyses of anthropometric change from baseline, for both the males and females, revealed significant gains in both genders groups for body weight (males: p<0.001, females: p<0.001), BMI (males: p<0.001, females: p<0.001), WC (males: p = 0.006, females: p<0.001), and HC (males: p = 0.006, females: p<0.001) from the beginning to the end of the year (S1 and S2 Tables). In comparison, no significant change in WHR was noted in both subgroups (males: p = 0.173, females: p = 0.193). Comparing the magnitude of change between the two sex groups showed that males gained slightly more body weight than females (1.90 kg vs.1.46 kg respectively). Overall, this trend was consistent across the other measured obesity traits as well, wherein males displayed moderately larger gains in BMI (0.74 kg/m2 vs. 0.62 kg/m2), WC (1.76cm vs. 0.99 cm), HC (1.08cm vs. 0.89cm), and WHR (0.0085 vs. 0.0030) towards the end of first year in university, compared to females. However, none of the observed differences in the magnitude of change between males and females were found to be statistically significant (interaction: p>0.05 across all traits). Table 3 summarizes the sex-specific anthropometric trends from the beginning and end of first year university.

Table 3. Sex-specific trends from the beginning to the end of first year in male (n = 48) and female (n = 197) undergraduate students.

Beginning Mean (SD) End Mean (SD) Change MD (95% CI) Time* p and η2 Sex* p and η2 Interaction* p and η2
Body Weight1 (kg) Males 71.37 (12.68) 73.27 (13.12) 1.90 (1.13–2.68) <0.001; 0.079 <0.001; 0.233 0.270; 0.005
Females 57.76 (10.18) 59.22 (10.53) 1.46 (1.12–1.80)
BMI2 (kg/m2) Males 22.62 (3.79) 23.36 (3.90) 0.74 (0.48–1.00) <0.001; 0.064 0.002; 0.041 0.640; 0.001
Females 21.25 (3.18) 21.87 (3.28) 0.62 (0.49–0.76)
Waist Circumference1 (cm) Males 81.38 (9.28) 83.14 (9.83) 1.76 (0.66–2.85) 0.005; 0.033 <0.001; 0.147 0.638; 0.001
Females 73.55 (7.83) 74.59 (7.93) 0.99 (0.41–1.58)
Hip Circumference (cm) Males 100.56 (7.57) 101.64 (7.19) 1.08 (0.29–1.88) <0.001; 0.054 <0.001; 0.067 0.506; 0.002
Females 96.36 (7.56) 97.25 (7.26) 0.89 (0.46–1.32)
WHR1 Males 0.808 (0.040) 0.816 (0.052) 0.0085 (-0.0027–0.0197) 0.373; 0.003 <0.001; 0.140 0.645; 0.001
Females 0.763 (0.048) 0.767 (0.049) 0.0030 (-0.0024–0.0084)
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Data are expressed as mean (SD) and mean difference (95% CI), WC data not collected for one female participant. * Significance from RMANOVA (Group: sex; Time: beginning to end); Rank based inverse normal transformation applied to all obesity traits; P-value threshold of 0.05 used for statistical significance; effect size determined by Partial Eta-Squared (η2)

1. Body weight, WC, and WHR with living arrangement as a covariate, data on living arrangement was not collected for one participant

2. BMI with living arrangement and cohort of recruitment as covariates

Discussion

In this investigation, we examined the effect of sex on obesity traits in first year of university. The investigation yielded several important results. Firstly, we found that males on average presented with larger body weight, BMI, WC, HC, and WHR at baseline as compared to females. Secondly, an overall net increase was observed in the sample, across all measured outcomes, towards the end of the academic year when compared to early on in the year. Notably, in this case, while significant gains in body weight, BMI, WC, and HC were noted, the change in WHR was not found to be significant. Thirdly, a consistent trend was observed in the two separate sex subgroups, wherein both males and females experienced significant growth in body weight, BMI, WC, and HC, but not WHR, during first year of university. Lastly, we found that while males displayed slightly larger gains than females over time, across all measured anthropometric parameters, the differences in the magnitude of change were modest with no significant sex effect being found within this context.

Weight gain in undergraduate students during first year of university has been extensively documented in previous studies from around the world [15,16]. Through our study, we confirmed this trend at McMaster University in Ontario, Canada. While the popular North American notion of ‘Freshman 15’ suggests that students gain approximately 15lbs (6.8kg) in first year of university, our results indicate a more modest overall increase of about 3.4lbs (1.55kg) on average. Nevertheless, this represents a significant change when compared to the general Canadian population. A report from Statistics Canada, involving data collected through the Canadian National Health Survey, previously indicated an average weight gain of 0.5 to 1 kg over a two-year period among Canadian adults [39]. Hence, in comparison, an average weight gain of 1.55 kg over a 5-month period among first-year university students represents a noteworthy change.

Our result is in line with the pooled weight gain estimates of 1.36 kg and 1.75kg, determined via previous meta-analyses by Vadeboncoeur et al., and Vella-Zarb and Elgar respectively [15,16]. Particularly, in their subgroup investigation of Canadian studies, Vadeboncoeur et al. further reported a pooled weight gain estimate of 1.71kg for Canadian first year university students, which is also consistent with our finding [15]. However, in their study, Vadeboncoeur et al. detected significant heterogeneity (I2  =  86.5%) [15]. This is particularly interesting because the reported estimates of overall weight gain in Canadian reports, that include both males and females, have varied from 0.79kg to 1.5kg [15]. With respect to BMI, the increasing trend that we found is also consistent with what has been previously reported by Mifsud et al. and Beaudry et al. [29,30]. It is noteworthy that most studies within this context have primarily investigated only two anthropometric traits (i.e. body weight and BMI), with only a few examining additional parameters such as WC, HC, and WHR. With respect to WC, while our data indicates a significant increase over the academic year, consistent with the findings of Mifsud et al., this result differs from that of Beaudry et al. which indicates no significant overall change in WC over time [29,30]. A similar inconsistency is noted between our result for WHR, which indicates no significant change over time, and that of Beaudry et al., which indicates a significant rise over the academic year [29]. Overall, the observed heterogeneity in these findings can be partly attributed to the differences in either demographic factors (e.g. differences in ethnic distribution, baseline BMI distribution, sex ratios, living arrangement, academic program), and environmental factors (e.g. differences in campus environments and resources available on campus) across the different universities in Canada, or variation in methodological factors across studies (e.g. differences in sampling strategies, measurement strategies).

When examining differences between males and females, our results reveal sexual dimorphism of obesity traits at baseline, with males displaying significantly higher body weight, BMI, WC, HC, and WHR than females. While most Canadian studies within this context have reported consistent sex specific trends at baseline, most prominently with respect to body weight and BMI, formal statistical testing of these baseline differences has been limited [29,30,40,41]. The sexual dimorphism of obesity traits has been extensively studied and can be attributed to fundamental biological differences between men and women across various age windows, such as differences in skeletal size, bone mass density, hormonal activity, and adipose tissue deposition [42–44].

With respect to change in obesity traits, our results indicate that both males and females experience significant gains across all measured obesity traits in first year of university. This indicates that both males and females are susceptible to gains in body weight and adiposity in first year of university. This trend is consistent with what has been previously reported for both male and female Canadian students within this context [15]. However, most notably, when comparing the average magnitude of change between males and females, we found no significant difference for any of the measured outcomes. Our finding aligns with that of a pooled subgroup analysis of 14 studies, by Vadeboncoeur et al., which also indicates no difference in the amount of weight gained between males and female students [15]. Nevertheless, previous Canadian studies within this context have reported mixed results. While the findings reported by Pliner and Saunders, and Vella-Zarb and Elgar indicate no sex-based differences with respect to change in BMI and body weight respectively, studies by Mifsud et al., and Beaudry et al. have evidenced significant sex-specific trends for change in body weight, BMI, WC, and WHR, but not HC [29–32]. Interestingly, all the aforementioned studies have been conducted in Ontario, Canada [29–32]. There are several possible reasons for the observed heterogeneity in findings, ranging from differences in population characteristics and campus environments at each university, to differences in study methodology. For instance, the ethnic distribution in our sample differs considerably from the aforementioned studies. Particularly, the samples in the studies by Mifsud et al., Vella-Zarb RA et al., and Beaudry et al are predominantly white Caucasian (>50%), with the latter two including only a modest proportion of Asian and African-Canadian students [29,30,32]. In comparison, our study sample presents a relatively diverse distribution, with the majority of the students being from the East Asian ethnic group, followed by considerable proportion of students from white Caucasian, South Asian, and Middle Eastern ethnic groups. Such factors may have an impact on the sex-specific trajectories of weight gain. Additionally, the baseline distribution of BMI weight status at a university can also impact the trajectory of BMI change. Hence, within this context, our differing results highlight the importance of conducting multiple studies not only across Canada but also within each province because multiple factors may differentiate university campuses from each other. Ultimately, a systematic review and meta-analysis of more studies, with exploration of between-study heterogeneity, will provide conclusive answers on the sexual dimorphism in change of obesity traits in first year, and its associated predictors in the Canadian undergraduate student population.

Our follow up investigation has several strengths, including a longitudinal study design, use of the same anthropometric parameters as the most recent study by Beaudry et al, investigation of participants from the same geographic region (i.e. Ontario), and use of the same statistical methodology. Furthermore, given that most Canadian studies so far have primarily examined either body weight or BMI outcomes within this context, our study provides valuable data on additional obesity parameters including WC, HC, and WHR, which is lacking in current literature. Limitations of our study include a modest sample size (N = 245) which is insufficiently powered to detect small effects. Additionally, we did not investigate physical activity as a covariate in our models, as done by Beaudry et al. in their study, due to a change in our method of measurement after the two first waves of recruitment. Similarly, we could not investigate ethnicity as a potential covariate in our present analysis due to the limited sample size of certain ethnic subgroups in our overall study sample. Apart from that, we did not examine body composition parameters in our study and hence could not specifically characterize the observed anthropometric change. It is important to note here that the weight gain observed in our sample may not be entirely attributed to an increase in fat, but also to additional contributing factors such as continued development and/or increased physical activity, and increase in muscle mass. Unfortunately, in this case, we could not evaluate parameters such as lean mass or fat mass. Nonetheless, our investigation of adiposity indicators, such as waist and hip circumference, revealed significant increases in those areas among both male and female participants. Hence, based on our data, we postulate that one of the components contributing to the observed weight gain in our sample may possibly be a potential increase in fat. However, we acknowledge that there may be additional contributing factors as discussed above, and recognize that the data is limited in terms of the information it provides to characterize the observed change. In our study, we also witnessed a higher attrition rate than Beaudry et al. which may have potentially biased our study results. We are aware that using normal transformation is a question of debate in the statistical field [45]. Lastly, we acknowledge that our sample had a significant imbalance in the ratio of male to female participants (approximately 20:80). This imbalance in the sex ratio, along with insufficient power for detection of small effects, may have prevented us from detecting subtle sex differences in anthropometric change. However, it is important to note that most previous studies within this context have included a disproportionately larger percentage of female participants [15]. Furthermore, previous Canadian reports have shown varied results and our study results are at least consistent with some of those previous reports. Nevertheless, we recognize this is an important limitation that restricts our ability to make inferences with the results of this investigation. Lastly, as highlighted previously, in this investigation, we followed the analysis protocol outlined by Beaudry at al. to optimize our ability to compare our results. However, we are aware that alternative statistical methods can be utilized to analyze this data.

In conclusion, our data confirm significant weight gain in both male and female first year university students in Ontario, Canada. However, our data do not indicate sex specific differences within this context. Ultimately, our study contributes to current evidence on this unresolved topic and highlights the need of further studies in this area. It further highlights the importance of accounting for sex and gender in health research to make the findings more applicable to the population. Given the association of obesity with higher morbidity and mortality, understanding the predictors of weight gain in young adulthood may be critical in optimizing the prediction, prevention and treatment of obesity. Future studies may also consider investigating the mechanisms of weight gain in the undergraduate student population by sex/gender, through quantitative and qualitative approaches.

Supporting information

S1 Data

(XLSX)

Click here for additional data file. (36.6KB, xlsx)
S1 Table. Distribution of demographic characteristics in the overall sample (n = 245) and in the male (n = 48) and female (n = 197) subgroups.

(DOCX)

Click here for additional data file. (13.3KB, docx)
S2 Table. Sex-specific trends in obesity traits from the beginning to the end of first year by male (n = 48) and female (n = 197) subgroups.

(DOCX)

Click here for additional data file. (14.2KB, docx)

Acknowledgments

We are indebted to all participants of this study. We would also like to extend our thanks to Anika Shah, Roshan Ahmad, Baanu Manoharan, Adrian Santhakumar, Kelly Zhu, Guneet Sandhu, Dea Sulaj, Tina Khordehi, Ansha Suleman, Heba Shahaed, Andrew Ng, Tania Mani, Sriyathavan Srichandramohan, Deven Deonarain, Celine Keomany, Omaike Sikder, Isis Lunsky, Gurudutt Kamath, and Christy Yu for their contribution.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

DM holds a Canada Research Chair in Genetics of Obesity. TS is supported by the Canadian Institutes of Health Research Canada Graduate Scholarship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Abarca-Gómez L, Abdeen ZA, Hamid ZA, Abu-Rmeileh NM, Acosta-Cazares B, et al. (2017) Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128· 9 million children, adolescents, and adults. The Lancet 390: 2627–2642. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Guo SS, Chumlea WC (1999) Tracking of body mass index in children in relation to overweight in adulthood. The American journal of clinical nutrition 70: 145S–148S. [DOI] [PubMed] [Google Scholar]
  • 3.Hirko KA, Kantor ED, Cohen SS, Blot WJ, Stampfer MJ, et al. (2015) Body mass index in young adulthood, obesity trajectory, and premature mortality. American journal of epidemiology 182: 441–450. 10.1093/aje/kwv084 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Juonala M, Magnussen CG, Berenson GS, Venn A, Burns TL, et al. (2011) Childhood adiposity, adult adiposity, and cardiovascular risk factors. N Engl J Med 365: 1876–1885. 10.1056/NEJMoa1010112 [DOI] [PubMed] [Google Scholar]
  • 5.Zheng Y, Manson JE, Yuan C, Liang MH, Grodstein F, et al. (2017) Associations of weight gain from early to middle adulthood with major health outcomes later in life. Jama 318: 255–269. 10.1001/jama.2017.7092 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Janssen I (2013) The public health burden of obesity in Canada. Canadian journal of diabetes 37: 90–96. 10.1016/j.jcjd.2013.02.059 [DOI] [PubMed] [Google Scholar]
  • 7.Peirson L, Douketis J, Ciliska D, Fitzpatrick-Lewis D, Ali MU, et al. (2014) Treatment for overweight and obesity in adult populations: a systematic review and meta-analysis. CMAJ open 2: E306 10.9778/cmajo.20140012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Morassut RE, Langlois C, Alyass A, Ishola AF, Yazdi FT, et al. (2017) Rationale and design of GENEiUS: a prospective observational study on the genetic and environmental determinants of body mass index evolution in Canadian undergraduate students. BMJ open 7: e019365 10.1136/bmjopen-2017-019365 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Nelson MC, Story M, Larson NI, Neumark‐Sztainer D, Lytle LA (2008) Emerging adulthood and college‐aged youth: an overlooked age for weight‐related behavior change. Obesity 16: 2205–2211. 10.1038/oby.2008.365 [DOI] [PubMed] [Google Scholar]
  • 10.Baum CL (2017) The effects of college on weight: examining the “freshman 15” myth and other effects of college over the life cycle. Demography 54: 311–336. 10.1007/s13524-016-0530-6 [DOI] [PubMed] [Google Scholar]
  • 11.Levitsky DA, Halbmaier CA, Mrdjenovic G (2004) The freshman weight gain: a model for the study of the epidemic of obesity. International journal of obesity 28: 1435–1442. 10.1038/sj.ijo.0802776 [DOI] [PubMed] [Google Scholar]
  • 12.McLaren L (2007) Socioeconomic status and obesity. Epidemiologic reviews 29: 29–48. 10.1093/epirev/mxm001 [DOI] [PubMed] [Google Scholar]
  • 13.Mokdad AH, Serdula MK, Dietz WH, Bowman BA, Marks JS, et al. (1999) The spread of the obesity epidemic in the United States, 1991–1998. Jama 282: 1519–1522. 10.1001/jama.282.16.1519 [DOI] [PubMed] [Google Scholar]
  • 14.Crombie AP, Ilich JZ, Dutton GR, Panton LB, Abood DA (2009) The freshman weight gain phenomenon revisited. Nutrition reviews 67: 83–94. 10.1111/j.1753-4887.2008.00143.x [DOI] [PubMed] [Google Scholar]
  • 15.Vadeboncoeur C, Townsend N, Foster C (2015) A meta-analysis of weight gain in first year university students: is freshman 15 a myth? BMC obesity 2: 22 10.1186/s40608-015-0051-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Vella-Zarb RA, Elgar FJ (2009) The ‘freshman 5’: a meta-analysis of weight gain in the freshman year of college. Journal of American College Health 58: 161–166. 10.1080/07448480903221392 [DOI] [PubMed] [Google Scholar]
  • 17.Deforche B, Van Dyck D, Deliens T, De Bourdeaudhuij I (2015) Changes in weight, physical activity, sedentary behaviour and dietary intake during the transition to higher education: a prospective study. International Journal of Behavioral Nutrition and Physical Activity 12: 16 10.1186/s12966-015-0173-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Pullman AW, Masters RC, Zalot LC, Carde LE, Saraiva MM, et al. (2009) Effect of the transition from high school to university on anthropometric and lifestyle variables in males. Applied Physiology, Nutrition, and Metabolism 34: 162–171. 10.1139/H09-007 [DOI] [PubMed] [Google Scholar]
  • 19.Wansink B, Cao Y, Saini P, Shimizu M, Just DR (2013) College cafeteria snack food purchases become less healthy with each passing week of the semester. Public health nutrition 16: 1291–1295. 10.1017/S136898001200328X [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Wengreen HJ, Moncur C (2009) Change in diet, physical activity, and body weight among young-adults during the transition from high school to college. Nutrition journal 8: 32 10.1186/1475-2891-8-32 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Pigeyre M, Yazdi FT, Kaur Y, Meyre D (2016) Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity. Clinical science 130: 943–986. 10.1042/CS20160136 [DOI] [PubMed] [Google Scholar]
  • 22.Castaner O, Goday A, Park Y-M, Lee S-H, Magkos F, et al. (2018) The gut microbiome profile in obesity: a systematic review. International journal of endocrinology 2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Coen S, Banister E (2012) What a difference sex and gender make: a gender, sex and health research casebook. Ottowa, Canada: Canadian Institutes of Health Research. [Google Scholar]
  • 24.Link JC, Reue K (2017) Genetic basis for sex differences in obesity and lipid metabolism. Annual review of nutrition 37: 225–245. 10.1146/annurev-nutr-071816-064827 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Cluskey M, Grobe D (2009) College weight gain and behavior transitions: male and female differences. Journal of the American Dietetic Association 109: 325–329. 10.1016/j.jada.2008.10.045 [DOI] [PubMed] [Google Scholar]
  • 26.Miller KJ, Gleaves DH, Hirsch TG, Green BA, Snow AC, et al. (2000) Comparisons of body image dimensions by race/ethnicity and gender in a university population. International Journal of Eating Disorders 27: 310–316. [DOI] [PubMed] [Google Scholar]
  • 27.Hoffman DJ, Policastro P, Quick V, Lee S-K (2006) Changes in body weight and fat mass of men and women in the first year of college: A study of the" freshman 15". Journal of American College Health 55: 41–46. 10.3200/JACH.55.1.41-46 [DOI] [PubMed] [Google Scholar]
  • 28.Holm-Denoma JM, Joiner TE Jr, Vohs KD, Heatherton TF (2008) The" freshman fifteen"(the" freshman five" actually): Predictors and possible explanations. Health Psychology 27: S3 10.1037/0278-6133.27.1.S3 [DOI] [PubMed] [Google Scholar]
  • 29.Beaudry KM, Ludwa IA, Thomas AM, Ward WE, Falk B, et al. (2019) First-year university is associated with greater body weight, body composition and adverse dietary changes in males than females. PloS one 14 10.1371/journal.pone.0218554 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Mifsud G, Duval K, Doucet E (2009) Low body fat and high cardiorespiratory fitness at the onset of the freshmen year may not protect against weight gain. British Journal of Nutrition 101: 1406–1412. [DOI] [PubMed] [Google Scholar]
  • 31.Pliner P, Saunders T (2008) Vulnerability to freshman weight gain as a function of dietary restraint and residence. Physiology & Behavior 93: 76–82. 10.1016/j.physbeh.2007.07.017 [DOI] [PubMed] [Google Scholar]
  • 32.Vella-Zarb RA, Elgar FJ (2010) Predicting the ‘freshman 15’: Environmental and psychological predictors of weight gain in first-year university students. Health Education Journal 69: 321–332. [Google Scholar]
  • 33.Gero D, File B, Justiz J, Steinert RE, Frick L, et al. (2019) Drinking microstructure in humans: A proof of concept study of a novel drinkometer in healthy adults. Appetite 133: 47–60. 10.1016/j.appet.2018.08.012 [DOI] [PubMed] [Google Scholar]
  • 34.Sharma T, Langlois C, Morassut RE, Meyre D (2020) Effect of living arrangement on anthropometric traits in first-year university students from Canada: The GENEiUS study. PLoS One 15: e0241744 10.1371/journal.pone.0241744 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Sharma T, Manoharan B, Langlois C, Morassut RE, Meyre D (2020) The effect of race/ethnicity on obesity traits in first year university students from Canada: The GENEiUS study. PLoS One 15: e0242714 10.1371/journal.pone.0242714 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Bakeman R (2005) Recommended effect size statistics for repeated measures designs. Behavior research methods 37: 379–384. 10.3758/bf03192707 [DOI] [PubMed] [Google Scholar]
  • 37.Feise RJ (2002) Do multiple outcome measures require p-value adjustment? BMC Med Res Methodol 2: 8 10.1186/1471-2288-2-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Leon AC, Heo M, Teres JJ, Morikawa T (2007) Statistical power of multiplicity adjustment strategies for correlated binary endpoints. Stat Med 26: 1712–1723. 10.1002/sim.2795 [DOI] [PubMed] [Google Scholar]
  • 39.Orpana HM, Tremblay MS, Finès P (2006) Trends in Weight Change Among Canadian Adults: Evidence from 1996/1997 to 2004/2005 National Population Health Survey: Citeseer. [PubMed]
  • 40.Frehlich LC, Eller LK, Parnell JA, Fung TS, Reimer RA (2017) Dietary intake and associated body weight in Canadian undergraduate students enrolled in nutrition education. Ecology of food and nutrition 56: 205–217. 10.1080/03670244.2017.1284066 [DOI] [PubMed] [Google Scholar]
  • 41.Provencher V, Polivy J, Wintre MG, Pratt MW, Pancer SM, et al. (2009) Who gains or who loses weight? Psychosocial factors among first-year university students. Physiology & Behavior 96: 135–141. 10.1016/j.physbeh.2008.09.011 [DOI] [PubMed] [Google Scholar]
  • 42.Bloor ID, Symonds ME (2014) Sexual dimorphism in white and brown adipose tissue with obesity and inflammation. Hormones and behavior 66: 95–103. 10.1016/j.yhbeh.2014.02.007 [DOI] [PubMed] [Google Scholar]
  • 43.Nieves JW, Formica C, Ruffing J, Zion M, Garrett P, et al. (2005) Males have larger skeletal size and bone mass than females, despite comparable body size. Journal of Bone and Mineral Research 20: 529–535. 10.1359/JBMR.041005 [DOI] [PubMed] [Google Scholar]
  • 44.Palmer BF, Clegg DJ (2015) The sexual dimorphism of obesity. Molecular and cellular endocrinology 402: 113–119. 10.1016/j.mce.2014.11.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Beasley TM, Erickson S, Allison DB (2009) Rank-based inverse normal transformations are increasingly used, but are they merited? Behav Genet 39: 580–595. 10.1007/s10519-009-9281-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Mauro Lombardo

25 Sep 2020

PONE-D-20-20612

Effect of sex/gender on obesity traits in Canadian first year university students: the GENEiUS study

PLOS ONE

Dear Dr. Meyre,

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: I Don't Know

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: No

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is a prospective observational cohort study that investigated the sex-specific changes in obesity features during first year of university. Two hundred and forty five first year undergraduate students were followed over 1 year with data collected early in the academic year and towards the end of the year. Sex-stratified analysis showed significant increase in weight, BMI, and waist and hip circumferences in both males and females. However, a comparison of the magnitude of changes over time between the two sex groups did not reach a threshold for statistical significance for any of the investigated traits.

Manuscript is well-written, with logical sequence and easy to understand. Furthermore, it is timely and relevant to current obesity problem.

I have no major issues with the methods and results or the conclusion the authors draw from them. Some minor comments are as follows:

1- Method: Study is well designed and method is appropriate. However, there is too much explanation about statistical analysis. This section can be written more concise. Instead, previous paper like Beaudry et al, (2019) can be cited.

2- Results: Results are clear and well organized. However, some data in the text (lines 195-203) are duplicated in table 1. Authors can choose to present them in either text or the table.

Reviewer #2: Thanks for the opportunity to review the article entitled "Effect of sex/gender on obesity traits in Canadian first year university students: the GENEiUS study". Although I found the topic interesting, the investigation is limited within the scope of the hypothesis formulated and tested.

Comments:

Demographics: I do not agree with the claim that the study recruited a "multi-ethnic" sample. The categories used to group participants by race and ethnicity might not capture their actual cultural and ethnic backgrounds.Also, your study would benefit from a table that includes the key demographic characteristics in your sample.

Moderator / grouping variable: Sex was a key study indicator in your study. Yet, females (n=197) outnumbered males (n=48) 4 times over. Your acknowledgement is valid, but it still makes it difficult to make inferences grouping the data in such a way (pg. 17, ln 346-349).

Small-to-no significant effect sizes: I am wondering if your investigation could in a new direction after learning from failure to reject your null hypothesis. I am optimistic that such a finding is a meaningful finding, which perhaps leads to sub-group analyses or studies with more depth. For example, how about collecting information on the processes that might be shaping weight gain by sex/gender? What are some differences and similarities that might explain each group's weight gain? A qualitative study might be a key next step.

Reviewer #3: The manuscript of Sharma et al. describes the weight gain that occurs during the first year of college in students at McMaster University in Ontario (Canada). The average gain was around 1.5 kg, with no significant differences between the sexes.

It is a well-written and interesting paper.

Only one aspect that I consider important to include in the manuscript. In the paper, the weight gain that occurs during the period studied seems to be a consequence of the university stay (change of environment and habits, sedentary lifestyle ...). However, it is not clear whether this weight change also occurs in the general population of similar age. A global significant weight increase has been described during adulthood. Therefore, this Reviewer thinks that it would be important to compare the data obtained in this study with those that can be found in general population. It would be interesting to have some group of non-university people available to compare, but since this is not the case, it is at least necessary to make the comparison with data available in Ontario from general population.

Since it is possible that part of the differences found between studies could be caused by the ethnic distribution of the students, I do not know if it would be possible to subdivide, at least the group of women, more numerous, thus comparing if there are differences associated with ethnic distribution.

Reviewer #4: Sharma et al. compose a very well written manuscript that longitudinally examines the 'freshman 15' in a group of undergraduate students in Ontario Canada, with an emphasis on sex-specific differences in weight gain. After review of the manuscript, it is clear that the authors considered their data prior to interpretation and do a great job highlighting both the strengths and weaknesses with the discussion of this manuscript. As mentioned by the authors a major weakness is the major discrepancy in males vs. females. While this is a limiting factor that had the potential to limit their ability to detect significant differences, the authors demonstrate that others have found similar results suggesting there may be generalizability to their findings. Overall I thought the manuscript was technically sound and well-written. I only have a few minor concerns.

1. Throughout the manuscript, the authors dive into the idea of obesity and the potential for the 'freshman 15' to contribute to obesity development. While this is likely true, the authors only briefly in the discussion touch on the possibility for continued development. This would be particularly true for males as many can undergo late-stage development during their early college years. Therefore, its unclear if the increase in weight gain for males is truly a reflection of an obesogenic environment or development.

2. In addition, without the knowledge of physical activity among these individuals, we again can not be sure if the weight gain is purely fat mass or a combination of fat mass and muscle mass (particularly if PA was increased). Again this is not likely, but should be more thoughtfully developed in the discussion.

3. Lastly, this group largely falls within the realm of normal weight status with only a small percentage falling in the category of overweight and an even smaller percent falling in the category of obese. This is important to note as it may not reflect other Universities, for instance those in the south in the USA where obesity rates can be over 40%. Again, are these students obesogenic?

Reviewer #5: In this work, Sharma and colleagues perform a longitudinal study investigating the morphometric evolution of first year University students in the McMaster University (Ontario, Canada). During this first year, several previous reports had shown a significant increase in BW and BMI. Some of them had reported significant sex-specific trends in these parameters, although the existing literature was heterogeneous and not conclusive. This new study, although including a low number of individuals (245), confirms a significant increase in BW, BMI, WC and HC in the students, with no significant difference between sexes, although males showed a tendency to greater increases in BW, BMI, WC and HC. This work, although limited, adds to previous reports using a longitudinal and relatively more complete assessment of morphometric parameters, and could be of use for future meta-analysis.

I have several comments:

1.- Statistical methods: it is not clear why authors choose to use the non-parametric tests for baseline and pairwise comparison of outcomes (Tables 1 and 2); while using the RMANOVA test for other year- and sex-specific outcomes. I suggest that outcomes are analyzed for their parametric or non-parametric distribution, justifying and homogenizing the choice of the statistical tests used.

2.- Authors claim several times throughout the text as a relevant finding that males presented with larger BW, WC or HC than females, while these parameters are usually larger in males than in females. In my opinion, only the higher BMI and WHR in males is a relevant finding, showing a stronger tendency to obesity in males.

3.- Since the mean topic of the work is the difference of sex in weight gain during first-year university, the ratio male/female in the cohort is a very important piece of information. However, it is not shown until the Results section. Here, authors report that 80.4% of the sample were female, but it is not clear to what cohort is this % referring to. Since it is presented in the same paragraph where the initial recruited population is described, (n=361), I understand that it 80.4% of this population are females. Therefore, only 76 males were included in the study. However, in Table 2 authors indicate a 19.6% (n=48) males, so maybe the 80.4% females indicated earlier in the Results section referred to the population that completed the follow-up. This piece of information (% of females/males at every stage of the study) is very important for this precise work but is not clearly shown in the text until the final part of the Discussion section. I think that it should be emphasized and made clear from the beginning, including the abstract.

Also, and related to this, the ethnicity and living arrangements were only shown for the total population, but not indicated for each sex, which could also shed some light to the differences.

4.- Tables 1 and 2: indicate the precise statistical methods used for these tables. I assume, from the Methods section, that the Wilcoxon signed-rank test was performed for Table 1, and the Mann-Whitney U test was used for Table 2, since Table 1 shows the paired data and Table 2 shows the baseline. But I think it should be stated in the legends for clarity.

5.- In Table 3 there are a few unclear notes (1, 2), that are hard to find in the table text. In particular, it is not clear to me why only BW, WC and WHR were calculated using living arrangement as a covariate, and BMI also used cohort as covariate. Also, it is not clear to me the nature of the “cohort” covariate, a clear explanation would be welcome.

Also it would be interesting to see if ethnicity was a relevant covariate. I assume that, since it was not included, it was not; but I would mention it in the text. Author indicate ethnicity as a possible reason for the discrepancy between this study and previous ones; but the data presented in this study does not indicate that ethnicity is a relevant covariate.

6.- Authors mention in the Discussion section an increase in BMI with no increase in WHR; a deeper discussion of the nutritional and metabolic meaning of this finding would be welcome.

7.- Full data are not available in a public repository, and there is no Data availability statement in the text. Please, provide a link to download the full, anonymized data in a public repository.

In all, I think that this study is well performed and adds robust data to the existing studies that could be of use to the specialized public. I consider this work worthy of publication in PLOS-ONE, once the previous comments are addressed.

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Reviewer #1: Yes: Baran Hashemi

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

Reviewer #5: No

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PLoS One. 2021 Feb 16;16(2):e0247113. doi: 10.1371/journal.pone.0247113.r002

Author response to Decision Letter 0

9 Nov 2020

We would like to thank the editor and the reviewers for their exceptional input and suggestions on the article. We have addressed their comments to the best of our ability and we think that the revised version of the manuscript has significantly improved.

Reviewer #1

1- Method: Study is well designed and method is appropriate. However, there is too much explanation about statistical analysis. This section can be written more concise. Instead, previous paper like Beaudry et al, (2019) can be cited.

We thank the reviewer for this comment. We acknowledge the reviewer’s perspective regarding the statistical methods being slightly explanation-heavy for some readers, and hence have made an effort to shorten the section slightly. However, most of the details included in that section have been retained as we feel they are necessary for a few different reasons. Firstly, the submission guidelines of PLOS One particularly require detailed reporting of statistical methods as to allow for replication of analysis by anyone who may be interested. As such, many of the details included in this section are based on the reporting criteria specified by the journal. Secondly, in this case, while we followed the methodology of Beaudry et al (2019) given the follow-up nature of our investigation, we further included a couple of additional elements of analysis in our paper (e.g. analyzing differences in obesity traits between males and females at baseline) that were not present in the previous paper by Beaudry and colleagues. Hence, our methods include a couple of additional aspects that are not present in the previous paper and thus require additional explanation. Lastly, while the reviewer is correct in mentioning that we can cite the paper by Beaudry et al. for all the overlapping methodologies between the two papers, we feel that this could affect the readability of our paper. Generally, when methods are simply referenced without inclusion of any details in the paper, it requires an extra effort on the part of the readers to go through a separate referenced and can hence create extra steps for readers. Thus, while we did reference the paper by Beaudry et al. (2019) for readers who may be interested in reviewing that source, we also included some of the fundamental methodological details in our paper in order to provide clarity and sufficient information for the average reader to understand the paper without having to consult additional sources.

2- Results: Results are clear and well organized. However, some data in the text (lines 195-203) are duplicated in table 1. Authors can choose to present them in either text or the table.

We thank the reviewer for the comment. We re-reviewed that section and feel that it is important to include some of the important details in the text even if those details are available in the table. In this case, we chose to only reiterate the information pertaining to the ‘change in the obesity traits.’ Given that the ‘change in traits’ is central focus of our paper and a critical point of discussion later in the paper, we think that mentioning those results in the text, in addition to the table, is important to make sure that readers can take note of that information and keep that important information in mind as they read the paper.

Reviewer #2

1. Demographics: I do not agree with the claim that the study recruited a "multi-ethnic" sample. The categories used to group participants by race and ethnicity might not capture their actual cultural and ethnic backgrounds. Also, your study would benefit from a table that includes the key demographic characteristics in your sample.

We thank the reviewer for the comment. We agree with the reviewer that the composition of our sample may not be reflective of a truly ‘multi-ethnic’ sample that incorporates all the diverse cultural and ethnic backgrounds. We have now removed the term multi-ethnic as a description of our sample. Additionally, we have included a supplementary table with the key demographic characteristics of the sample as per the reviewer’s suggestion.

2. Moderator / grouping variable: Sex was a key study indicator in your study. Yet, females (n=197) outnumbered males (n=48) 4 times over. Your acknowledgement is valid, but it still makes it difficult to make inferences grouping the data in such a way (pg. 17, ln 346-349).

Thank you for the comment. We agree with the reviewer that this is a limitation of the investigation and as mentioned by the reviewer, we have acknowledged this in the discussion section of our paper. As we discussed in the paper, most studies in this area present a similar imbalance in ratio of males and females, including the previous study by Beaudry et al (2019) that we followed. We have now added an additional sentence in the discussion section to further indicate that this imbalance in ratio of males to females is an important limitation that should be considered when interpreting the results of our study.

“Lastly, we acknowledge that our sample had a significant imbalance in the ratio of male to female participants (approximately 20:80). This imbalance in the sex ratio, along with insufficient power for detection of small effects, may have prevented us from detecting subtle sex differences in anthropometric change. However, it is important to note that most previous studies within this context have included a disproportionately larger percentage of female participants [15]. Furthermore, previous Canadian reports have shown varied results and our study results are at least consistent with some of those previous reports. Nevertheless, we recognize this is an important limitation that restricts our ability to make inferences with the results of this investigation.”

3. Small-to-no significant effect sizes: I am wondering if your investigation could in a new direction after learning from failure to reject your null hypothesis. I am optimistic that such a finding is a meaningful finding, which perhaps leads to sub-group analyses or studies with more depth. For example, how about collecting information on the processes that might be shaping weight gain by sex/gender? What are some differences and similarities that might explain each group's weight gain? A qualitative study might be a key next step.

Thank you for the comment. In this case, since we did not find a difference in anthropometric change between males and females, our investigation did not merit further investigation into the different mechanisms of weight gain by sex/gender. Nevertheless, while this is beyond the scope of our paper, we do agree that this is an interesting field of research. We have now added this point as a recommendation for future studies in the discussion section of our paper.

“Future studies may also consider investigating the mechanisms of weight gain in the undergraduate student population by sex/gender, through quantitative and qualitative approaches.”

Reviewer #3

1. In the paper, the weight gain that occurs during the period studied seems to be a consequence of the university stay (change of environment and habits, sedentary lifestyle ...). However, it is not clear whether this weight change also occurs in the general population of similar age. A global significant weight increase has been described during adulthood. Therefore, this Reviewer thinks that it would be important to compare the data obtained in this study with those that can be found in general population. It would be interesting to have some group of non-university people available to compare, but since this is not the case, it is at least necessary to make the comparison with data available in Ontario from general population.

The reviewer brings up an excellent point. While we were not able to specifically find data pertaining to non-university Canadians in the same age group, we have added a broader comparison to the general Canadian population based on data available from Statistics Canada.

“Nevertheless, this represents a significant change when compared to the general Canadian population. A report from Statistics Canada, involving data collected through the Canadian National Health Survey, previously indicated an average weight gain of 0.5 to 1 kg over a two-year period among Canadian adults [36]. Hence, in comparison, an average weight gain of 1.55 kg over a 5-month period among first-year university students represents a noteworthy change.”

2. Since it is possible that part of the differences found between studies could be caused by the ethnic distribution of the students, I do not know if it would be possible to subdivide, at least the group of women, more numerous, thus comparing if there are differences associated with ethnic distribution.

Thank you for the great comment. We acknowledge that examining the differences associated with ethnic distribution would be an interesting area of analysis. However, given our limited sample size in this case, we feel that an analysis of ethnic subgroups within the sex/gender subgroups would be severely underpowered. We think this analysis would also not be adequately powered with male group as our sample includes participants from multiple ethnic groups with the distribution of participants across each of the ethnic subgroups being unequal. Hence, the number of people within each ethnicity x sex subgroup is minimal and hence such analysis would be severely underpowered. Nonetheless, we agree with the reviewer that this is an interested area of investigation, and while it may not be possible to include the suggested analysis in this report due to power and sample size limitations, we have another paper that is currently in revision at PLOS One that independently examines the effect of race/ethnicity on anthropometric traits in the same cohort.

Reviewer #4

1. Throughout the manuscript, the authors dive into the idea of obesity and the potential for the 'freshman 15' to contribute to obesity development. While this is likely true, the authors only briefly in the discussion touch on the possibility for continued development. This would be particularly true for males as many can undergo late-stage development during their early college years. Therefore, its unclear if the increase in weight gain for males is truly a reflection of an obesogenic environment or development.

We thank the reviewer for this important comment. We have now added a paragraph in the discussion section of the paper that addresses these points.

“It is important to note here that the weight gain observed in our sample may not be entirely attributed to an increase in fat, but also to additional contributing factors such as continued development and increase in muscle mass. Unfortunately, in this case, we could not evaluate parameters such as lean mass or fat mass. Nonetheless, our investigation of adiposity indicators, such as waist and hip circumference, revealed significant increases in those areas among both male and female participants. Hence, based on our data, we postulate that one of the components contributing to the observed weight gain in our sample may possibly be a potential increase in fat. However, we acknowledge that there may be additional contributing factors as discussed above, and recognize that the data is limited in terms of the information it provides to characterize the observed change.”

2. In addition, without the knowledge of physical activity among these individuals, we again can not be sure if the weight gain is purely fat mass or a combination of fat mass and muscle mass (particularly if PA was increased). Again this is not likely, but should be more thoughtfully developed in the discussion.

The reviewer brings up a fair point. We have now extended the discussion about the limitations pertaining to the characterization of weight change in our study, including the lack of information about physical activity.

“Additionally, we did not investigate physical activity as a covariate in our models, as done by Beaudry et al. in their study, due to a change in our method of measurement after the two first waves of recruitment. Furthermore, we did not examine body composition parameters in our study and hence could not specifically characterize the observed anthropometric change. It is important to note here that the weight gain observed in our sample may not be entirely attributed to an increase in fat, but also to additional contributing factors such as continued development and / or increased physical activity, and increase in muscle mass. Unfortunately, in this case, we could not evaluate parameters such as lean mass or fat mass. Nonetheless, our investigation of adiposity indicators, such as waist and hip circumference, revealed significant increases in those areas among both male and female participants. Hence, based on our data, we postulate that one of the components contributing to the observed weight gain in our sample may possibly be potential increase in fat. However, we acknowledge that there may be additional contributing factors as discussed above, and recognize that the data is limited in terms of the information it provides to characterize the observed change.”

3. Lastly, this group largely falls within the realm of normal weight status with only a small percentage falling in the category of overweight and an even smaller percent falling in the category of obese. This is important to note as it may not reflect other Universities, for instance those in the south in the USA where obesity rates can be over 40%. Again, are these students obesogenic?

We agree with the reviewer that there are differences in university environments. We talked about campus heterogeneity as an important factor through the discussion section of our paper. We have further added a note about the differing distribution of BMI weight status across universities.

“Additionally, the baseline distribution of BMI weight status at a university can also impact the trajectory of BMI change. Hence, within this context, our differing results highlight the importance of conducting multiple studies not only across Canada but also within each province because multiple factors may differentiate university campuses from each other.”

Apart from that, we have also added a sentence in the results section to highlight the distribution of BMI weight statuses observed in our sample by the end of the academic year.

“In terms of their BMI categories, by the end of the academic year 77.1% (n = 189) of the participants were in the normal weight range, 8.6% were underweight (n = 21), 11.4% were overweight (n = 28), and 2.9% (n = 7) were obese.”

Reviewer #5

1.- Statistical methods: it is not clear why authors choose to use the non-parametric tests for baseline and pairwise comparison of outcomes (Tables 1 and 2); while using the RMANOVA test for other year- and sex-specific outcomes. I suggest that outcomes are analyzed for their parametric or non-parametric distribution, justifying and homogenizing the choice of the statistical tests used.

Thank you for the comment. In this case, given that our investigation was a follow-up to a previous report by Beaudry et al (2019), we tried to largely follow the same analytical methodology as the paper by Beaudry et al. (i.e. RMANVOA). Nevertheless, we further included a couple of additional elements of analysis in our paper (e.g. analyzing differences in obesity traits between males and females at baseline) that were not present in the previous paper by Beaudry and colleagues. Particularly, we used non-parametric tests for evaluation of traits without adjustment for covariates and used the RMANOVA test with transformation for analysis of traits with adjustment for covariates. We recognize that there are different analytical approaches that be used to analyze the data. In fact, we used a different approach involving regression analysis for our other papers on the effect of living arrangement and ethnicity on anthropometric that are currently published (Sharma et al., PLOS One 2020) or under revision. In this case, we feel that the respective tests used here are appropriate, and do not see the need to homogenize the approach.

2.- Authors claim several times throughout the text as a relevant finding that males presented with larger BW, WC or HC than females, while these parameters are usually larger in males than in females. In my opinion, only the higher BMI and WHR in males is a relevant finding, showing a stronger tendency to obesity in males.

Thank you for the comment. While we agree with the reviewer that BMI and WHR are critical parameters and could be the focus of a paper, we feel that it is still important to discuss the other parameters in the text as well considering that the data for these traits have been presented in the paper. Given that we conducted this investigation as a follow-up to the previous paper by Beaudry et al, we assessed and discussed the same traits as the ones discussed by Beaudry and colleagues. Additionally, in recent times, there has generally been an increasing amount of the literature on the prognostic value of indicators such as WC and hence including these results may be of interest to the readers.

3.- Since the mean topic of the work is the difference of sex in weight gain during first-year university, the ratio male/female in the cohort is a very important piece of information. However, it is not shown until the Results section. Here, authors report that 80.4% of the sample were female, but it is not clear to what cohort is this % referring to. Since it is presented in the same paragraph where the initial recruited population is described, (n=361), I understand that it 80.4% of this population are females. Therefore, only 76 males were included in the study. However, in Table 2 authors indicate a 19.6% (n=48) males, so maybe the 80.4% females indicated earlier in the Results section referred to the population that completed the follow-up. This piece of information (% of females/males at every stage of the study) is very important for this precise work but is not clearly shown in the text until the final part of the Discussion section. I think that it should be emphasized and made clear from the beginning, including the abstract. Also, and related to this, the ethnicity and living arrangements were only shown for the total population, but not indicated for each sex, which could also shed some light to the differences.

This is an excellent point. We have now updated the text to better reflect the cohort information and have included a supplementary table the described the ethnicity and living arrangement distribution by sex/gender.

4.- Tables 1 and 2: indicate the precise statistical methods used for these tables. I assume, from the Methods section, that the Wilcoxon signed-rank test was performed for Table 1, and the Mann-Whitney U test was used for Table 2, since Table 1 shows the paired data and Table 2 shows the baseline. But I think it should be stated in the legends for clarity.

The reviewer brings up an important point. We have now updated the legends of both the tables as per the reviewer’s recommendation.

5.- In Table 3 there are a few unclear notes (1, 2), that are hard to find in the table text. In particular, it is not clear to me why only BW, WC and WHR were calculated using living arrangement as a covariate, and BMI also used cohort as covariate. Also, it is not clear to me the nature of the “cohort” covariate, a clear explanation would be welcome. Also it would be interesting to see if ethnicity was a relevant covariate. I assume that, since it was not included, it was not; but I would mention it in the text. Author indicate ethnicity as a possible reason for the discrepancy between this study and previous ones; but the data presented in this study does not indicate that ethnicity is a relevant covariate.

Thank you for the comment. In this case, we used the same statistical methodology and covariate adjustment strategy as the previous paper by Beaudry et al (2019). As such, in accordance with the protocol of Beadry et al, the covariates were only retained in the model if their interaction with the main effect (i.e. time) was significant or marginally significant. We have included this detail in the statistical methods section and have included a reference to the paper by Beaudry et al. (2019) for readers who are interested in reading their protocol in further detail. Cohort refers to the cohort of recruitment in this case. We have updated the legend of Table 3 to better reflect this. While we agree that ethnicity is an interesting variable to test, we did not explore it as a covariate in this case particularly because it was not also included as a covariate by Beaudry et al. in their paper. Hence, in order to keep the methodology consistent, we only explored the variables that were evaluated in the previous paper. However, we so have another paper that is currently in revision at PLOS One that examines the effect of ethnicity on anthropometric traits in GENEiUS.

6.- Authors mention in the Discussion section an increase in BMI with no increase in WHR; a deeper discussion of the nutritional and metabolic meaning of this finding would be welcome.

We thank the reviewer for this inspired comment. We have now added a paragraph in the discussion section of the paper that addresses these points.

“It is important to note here that the weight gain observed in our sample may not be entirely attributed to an increase in fat, but also to additional contributing factors such as continued development and increase in muscle mass. Unfortunately, in this case, we could not evaluate parameters such as lean mass or fat mass. Nonetheless, our investigation of adiposity indicators, such as waist and hip circumference, revealed significant increases in those areas among both male and female participants. Hence, based on our data, we postulate that one of the components contributing to the observed weight gain in our sample may possibly be a potential increase in fat. However, we acknowledge that there may be additional contributing factors as discussed above, and recognize that the data is limited in terms of the information it provides to characterize the observed change.”

7.- Full data are not available in a public repository, and there is no Data availability statement in the text. Please, provide a link to download the full, anonymized data in a public repository.

Thank you for bringing up this important point. The dataset has now been included.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (26.9KB, docx)

Decision Letter 1

Mauro Lombardo

26 Nov 2020

PONE-D-20-20612R1

Effect of sex/gender on obesity traits in Canadian first year university students: the GENEiUS study

PLOS ONE

Dear Dr. Meyre,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

Reviewer #5: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: I Don't Know

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

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Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: (No Response)

Reviewer #4: Sharma et al. have re-submitted their revised manuscript. It appears that the comments have been addressed adequately and that the authors have updated their methods, results, data and discussion to reflect these changes. While all points were not addressed in the manuscript (i.e. methods revisions), the authors provide adequate rationale for this decision. In this case, the authors left the methods more detailed for appropriate reproducibility as opposed to referring the reader/reviewer to other manuscripts for methodology. This follows with PLoS One's guidelines.

Reviewer #5: 1.- Statistical methods: it is not clear why authors choose to use the non-parametric tests for baseline and pairwise comparison of outcomes (Tables 1 and 2); while using the RMANOVA test for other year- and sex-specific outcomes. I suggest that outcomes are analyzed for their parametric or non-parametric distribution, justifying and homogenizing the choice of the statistical tests used.

A: Thank you for the comment. In this case, given that our investigation was a follow-up to a previous report by Beaudry et al (2019), we tried to largely follow the same analytical methodology as the paper by Beaudry et al. (i.e. RMANVOA). Nevertheless, we further included a couple of additional elements of analysis in our paper (e.g. analyzing differences in obesity traits between males and females at baseline) that were not present in the previous paper by Beaudry and colleagues. Particularly, we used non- parametric tests for evaluation of traits without adjustment for covariates and used the RMANOVA test with transformation for analysis of traits with adjustment for covariates. We recognize that there are different analytical approaches that be used to analyze the data. In fact, we used a different approach involving regression analysis for our other papers on the effect of living arrangement and ethnicity on anthropometric that are currently published (Sharma et al., PLOS One 2020) or under revision. In this case, we feel that the respective tests used here are appropriate, and do not see the need to homogenize the approach.

R: it sounds odd that the only argument to justify the use of a statistical method is “that others also used it before”, rather than proving that the statistical method is the most appropriate for the kind of data analyzed.

2.- Authors claim several times throughout the text as a relevant finding that males presented with larger BW, WC or HC than females, while these parameters are usually larger in males than in females. In my opinion, only the higher BMI and WHR in males is a relevant finding, showing a stronger tendency to obesity in males.

A: Thank you for the comment. While we agree with the reviewer that BMI and WHR are critical parameters and could be the focus of a paper, we feel that it is still important to discuss the other parameters in the text as well considering that the data for these traits have been presented in the paper. Given that we conducted this investigation as a follow-up to the previous paper by Beaudry et al, we assessed and discussed the same traits as the ones discussed by Beaudry and colleagues. Additionally, in recent times, there has generally been an increasing amount of the literature on the prognostic value of indicators such as WC and hence including these results may be of interest to the readers.

R: I think my point was not well understood. I agree that analyzing changes in BW, WC and HC of individuals with time is a very valuable analysis. What I do not agree is on the treatment as a relevant finding of the difference between male and female volunteers in BW, WC and HC: males always display larger BW, WC and HC in average than females, that is not biologically relevant.

3.- Since the mean topic of the work is the difference of sex in weight gain during first-year university, the ratio male/female in the cohort is a very important piece of information. However, it is not shown until the Results section. Here, authors report that 80.4% of the sample were female, but it is not clear to what cohort is this % referring to. Since it is presented in the same paragraph where the initial recruited population is described, (n=361), I understand that it 80.4% of this population are females. Therefore, only 76 males were included in the study. However, in Table 2 authors indicate a 19.6% (n=48) males, so maybe the 80.4% females indicated earlier in the Results section referred to the population that completed the follow-up. This piece of information (% of females/males at every stage of the study) is very important for this precise work but is not clearly shown in the text until the final part of the Discussion section. I think that it should be emphasized and made clear from the beginning, including the abstract.

Also, and related to this, the ethnicity and living arrangements were only shown for the total population, but not indicated for each sex, which could also shed some light to the differences.

A: This is an excellent point. We have now updated the text to better reflect the cohort information and have included a supplementary table the described the ethnicity and living arrangement distribution by sex/gender.

R: thank you for your efforts. I still do not see any mention to the males/females ratio in the abstract, where I think that it is a very relevant piece of information. Please, indicate where exactly in the text these changes were included, to be able to verify them easily.

4.- Tables 1 and 2: indicate the precise statistical methods used for these tables. I assume, from the Methods section, that the Wilcoxon signed-rank test was performed for Table 1, and the Mann-Whitney U test was used for Table 2, since Table 1 shows the paired data and Table 2 shows the baseline. But I think it should be stated in the legends for clarity.

A: The reviewer brings up an important point. We have now updated the legends of both the tables as per the reviewer’s recommendation.

R: thank you for your efforts, it is more complete now.

5.- In Table 3 there are a few unclear notes (1, 2), that are hard to find in the table text. In particular, it is not clear to me why only BW, WC and WHR were calculated using living arrangement as a covariate, and BMI also used cohort as covariate. Also, it is not clear to me the nature of the “cohort” covariate, a clear explanation would be welcome.

Also it would be interesting to see if ethnicity was a relevant covariate. I assume that, since it was not included, it was not; but I would mention it in the text. Author indicate ethnicity as a possible reason for the discrepancy between this study and previous ones; but the data presented in this study does not indicate that ethnicity is a relevant covariate.

A: Thank you for the comment. In this case, we used the same statistical methodology and covariate adjustment strategy as the previous paper by Beaudry et al (2019). As such, in accordance with the protocol of Beadry et al, the covariates were only retained in the model if their interaction with the main effect (i.e. time) was significant or marginally significant. We have included this detail in the statistical methods section and have included a reference to the paper by Beaudry et al. (2019) for readers who are interested in reading their protocol in further detail. Cohort refers to the cohort of recruitment in this case. We have updated the legend of Table 3 to better reflect this. While we agree that ethnicity is an interesting variable to test, we did not explore it as a covariate in this case particularly because it was not also included as a covariate by Beaudry et al. in their paper. Hence, in order to keep the methodology consistent, we only explored the variables that were evaluated in the previous paper. However, we so have another paper that is currently in revision at PLOS One that examines the effect of ethnicity on anthropometric traits in GENEiUS.

R: Thank you for your efforts. I understand that only the covariates that interacted significantly (or marginally singifficantly) with the main effect (time) were included. It would be useful to know the exact degree of significance for the analyzed variables, since the term “marginally” significant is not very clear. Also, authors indicate that they did not include ethnicity “because it was not included in Beaudry et al”; again, this is not a valid answer, because this is a different study. Was ethnicity significantly interacting with time? If it was, it should be included; if notit was not, that is a valid argument, not Beaudry’s.

6.- Authors mention in the Discussion section an increase in BMI with no increase in WHR; a deeper discussion of the nutritional and metabolic meaning of this finding would be welcome.

A: We thank the reviewer for this inspired comment. We have now added a paragraph in the discussion section of the paper that addresses these points.

“It is important to note here that the weight gain observed in our sample may not be entirely attributed to an increase in fat, but also to additional contributing factors such as continued development and increase in muscle mass. Unfortunately, in this case, we could not evaluate parameters such as lean mass or fat mass. Nonetheless, our investigation of adiposity indicators, such as waist and hip circumference, revealed significant increases in those areas among both male and female participants. Hence, based on our data, we postulate that one of the components contributing to the observed weight gain in our sample may possibly be a potential increase in fat. However, we acknowledge that there may be additional contributing factors as discussed above, and recognize that the data is limited in terms of the information it provides to characterize the observed change.”

R: Thank you for the new discussion.

7.- Full data are not available in a public repository, and there is no Data availability statement in the text. Please, provide a link to download the full, anonymized data in a public repository.

A: Thank you for bringing up this important point. The dataset has now been included.

R: Thank you for this dataset, it is very useful.

**********

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Reviewer #3: No

Reviewer #4: No

Reviewer #5: No

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PLoS One. 2021 Feb 16;16(2):e0247113. doi: 10.1371/journal.pone.0247113.r004

Author response to Decision Letter 1

28 Dec 2020

We thank the editor and reviewers for their comments and feedback. We have addressed their comments below to the best of our ability.

Reviewer #4: Sharma et al. have re-submitted their revised manuscript. It appears that the comments have been addressed adequately and that the authors have updated their methods, results, data and discussion to reflect these changes. While all points were not addressed in the manuscript (i.e. methods revisions), the authors provide adequate rationale for this decision. In this case, the authors left the methods more detailed for appropriate reproducibility as opposed to referring the reader/reviewer to other manuscripts for methodology. This follows with PLoS One's guidelines.

Thank you so much for the constructive comment.

Reviewer #5: 1.- Statistical methods: it is not clear why authors choose to use the non-parametric tests for baseline and pairwise comparison of outcomes (Tables 1 and 2); while using the RMANOVA test for other year- and sex-specific outcomes. I suggest that outcomes are analyzed for their parametric or non-parametric distribution, justifying and homogenizing the choice of the statistical tests used.

A: Thank you for the comment. In this case, given that our investigation was a follow-up to a previous report by Beaudry et al (2019), we tried to largely follow the same analytical methodology as the paper by Beaudry et al. (i.e. RMANVOA). Nevertheless, we further included a couple of additional elements of analysis in our paper (e.g. analyzing differences in obesity traits between males and females at baseline) that were not present in the previous paper by Beaudry and colleagues. Particularly, we used non- parametric tests for evaluation of traits without adjustment for covariates and used the RMANOVA test with transformation for analysis of traits with adjustment for covariates. We recognize that there are different analytical approaches that be used to analyze the data. In fact, we used a different approach involving regression analysis for our other papers on the effect of living arrangement and ethnicity on anthropometric that are currently published (Sharma et al., PLOS One 2020) or under revision. In this case, we feel that the respective tests used here are appropriate, and do not see the need to homogenize the approach.

R: it sounds odd that the only argument to justify the use of a statistical method is “that others also used it before”, rather than proving that the statistical method is the most appropriate for the kind of data analyzed.

Thank you for the comment. We agree “that others also used it before” would not be a sufficient argument to apply the same approach. However, it seems the reviewer overlooked the other arguments we provided to justify our approach. Firstly, we think that the statistical methods proposed by Beaudry et al. are adequate to answer the questions asked. Secondly, the manuscript by Beaudry et al. has been reviewed and validated by experts, as part of the peer-review process, so we are not the only ones to think that the statistical approach used by Beaudry et al. is adequate. Thirdly, given the follow-up nature of our investigation, using a drastically different statistical approach may have added heterogeneity and impaired our ability to compare our results to those of Beaudry et al. Fourthly, we have provided additional elements of analysis in our paper that were not present in Beaudry’s paper and hence those methods differ – particularly with respect to using non-parametric tests for a raw comparison of traits in the absence of covariates, and the using RMANOVA, as done by Beaudry et al. for the comparison of traits with adjustment for covariates. We believe these are adequate reasons to justify our approach. However, we understand well the reviewer’s concern and we have added a limitation in the discussion to reflect their important point.

“In this investigation, we followed the analysis protocol outlined by Beaudry at al. to optimize our ability to compare our results. However, we are aware that alternative statistical methods can also be utilized to analyze this data.”

2.- Authors claim several times throughout the text as a relevant finding that males presented with larger BW, WC or HC than females, while these parameters are usually larger in males than in females. In my opinion, only the higher BMI and WHR in males is a relevant finding, showing a stronger tendency to obesity in males.

A: Thank you for the comment. While we agree with the reviewer that BMI and WHR are critical parameters and could be the focus of a paper, we feel that it is still important to discuss the other parameters in the text as well considering that the data for these traits have been presented in the paper. Given that we conducted this investigation as a follow-up to the previous paper by Beaudry et al, we assessed and discussed the same traits as the ones discussed by Beaudry and colleagues. Additionally, in recent times, there has generally been an increasing amount of the literature on the prognostic value of indicators such as WC and hence including these results may be of interest to the readers.

R: I think my point was not well understood. I agree that analyzing changes in BW, WC and HC of individuals with time is a very valuable analysis. What I do not agree is on the treatment as a relevant finding of the difference between male and female volunteers in BW, WC and HC: males always display larger BW, WC and HC in average than females, that is not biologically relevant.

Thank you for the clarification. While we agree with the reviewer that the observation that males always display larger BW, WC and HC on average is a well-established observation in literature, we still feel it is important to include this finding in our report for a couple of reasons. Firstly, we do think that males being larger than females is a biologically important observation. Secondly, in any case, we believe that before discussing the ‘change’ in traits across groups, it is imperative that the baseline distribution of the traits are assessed and reported, as knowing the baseline distribution provides context to better interpret values of change. In this case, while this may seem redundant given the extensive amount of literature on sex-based anthropometric patterns, we believe it is still important that we confirm this finding in our sample and provide that baseline context for the readers before providing a discussion on the magnitude of change observed in these traits. Apart from that, it should be noted that we only discussed this observation very briefly in our result and discussion sections with only a few lines dedicated this observation in the entire manuscript. We are aware that this is a common observation and hence it is definitely not something we have discussed frequently or emphasized as a core point of discussion in our manuscript. In fact, in our brief discussion of this observation, we also particularly acknowledge that this is a well-established observation in different age groups in literature (lines 312-315). Our discussion on the change in traits definitely makes up the majority of our paper.

3.- Since the mean topic of the work is the difference of sex in weight gain during first-year university, the ratio male/female in the cohort is a very important piece of information. However, it is not shown until the Results section. Here, authors report that 80.4% of the sample were female, but it is not clear to what cohort is this % referring to. Since it is presented in the same paragraph where the initial recruited population is described, (n=361), I understand that it 80.4% of this population are females. Therefore, only 76 males were included in the study. However, in Table 2 authors indicate a 19.6% (n=48) males, so maybe the 80.4% females indicated earlier in the Results section referred to the population that completed the follow-up. This piece of information (% of females/males at every stage of the study) is very important for this precise work but is not clearly shown in the text until the final part of the Discussion section. I think that it should be emphasized and made clear from the beginning, including the abstract.

Also, and related to this, the ethnicity and living arrangements were only shown for the total population, but not indicated for each sex, which could also shed some light to the differences.

A: This is an excellent point. We have now updated the text to better reflect the cohort information and have included a supplementary table the described the ethnicity and living arrangement distribution by sex/gender.

R: thank you for your efforts. I still do not see any mention to the males/females ratio in the abstract, where I think that it is a very relevant piece of information. Please, indicate where exactly in the text these changes were included, to be able to verify them easily.

The size of the analyzed sample and the gender ratio has now been specified in the abstract (line 29), methods section (lines 120-122), and the results section (line 172, 175). Thank you.

4.- Tables 1 and 2: indicate the precise statistical methods used for these tables. I assume, from the Methods section, that the Wilcoxon signed-rank test was performed for Table 1, and the Mann-Whitney U test was used for Table 2, since Table 1 shows the paired data and Table 2 shows the baseline. But I think it should be stated in the legends for clarity.

A: The reviewer brings up an important point. We have now updated the legends of both the tables as per the reviewer’s recommendation.

R: thank you for your efforts, it is more complete now.

Thank you so much for the feedback.

5.- In Table 3 there are a few unclear notes (1, 2), that are hard to find in the table text. In particular, it is not clear to me why only BW, WC and WHR were calculated using living arrangement as a covariate, and BMI also used cohort as covariate. Also, it is not clear to me the nature of the “cohort” covariate, a clear explanation would be welcome.

Also it would be interesting to see if ethnicity was a relevant covariate. I assume that, since it was not included, it was not; but I would mention it in the text. Author indicate ethnicity as a possible reason for the discrepancy between this study and previous ones; but the data presented in this study does not indicate that ethnicity is a relevant covariate.

A: Thank you for the comment. In this case, we used the same statistical methodology and covariate adjustment strategy as the previous paper by Beaudry et al (2019). As such, in accordance with the protocol of Beadry et al, the covariates were only retained in the model if their interaction with the main effect (i.e. time) was significant or marginally significant. We have included this detail in the statistical methods section and have included a reference to the paper by Beaudry et al. (2019) for readers who are interested in reading their protocol in further detail. Cohort refers to the cohort of recruitment in this case. We have updated the legend of Table 3 to better reflect this. While we agree that ethnicity is an interesting variable to test, we did not explore it as a covariate in this case particularly because it was not also included as a covariate by Beaudry et al. in their paper. Hence, in order to keep the methodology consistent, we only explored the variables that were evaluated in the previous paper. However, we so have another paper that is currently in revision at PLOS One that examines the effect of ethnicity on anthropometric traits in GENEiUS.

R: Thank you for your efforts. I understand that only the covariates that interacted significantly (or marginally singifficantly) with the main effect (time) were included. It would be useful to know the exact degree of significance for the analyzed variables, since the term “marginally” significant is not very clear. Also, authors indicate that they did not include ethnicity “because it was not included in Beaudry et al”; again, this is not a valid answer, because this is a different study. Was ethnicity significantly interacting with time? If it was, it should be included; if notit was not, that is a valid argument, not Beaudry’s.

Thank you for the comment. We have now specified our definition of marginal p-value (i.e. p<0.1) in the methods section of the paper for further clarity. With regards to the analysis of ethnicity as a covariate, as discussed previously, we did not initially intend on exploring ethnicity as a covariate as we followed the analytical protocol, covariate adjustment strategy, outlined by Beaudry et al. (2019) in order to minimize the heterogeneity between the study methodologies for better comparability of results. While this is a “different study, we believe that this is still an important consideration as changing the analytical strategy can introduce methodological heterogeneity and consequently reduce comparability, which is not favorable for a follow up investigation like ours. Nevertheless, we acknowledge the reviewer’s point and recognize the merit in assessing ethnicity as a potential covariate. However, in this case, we are unable to assess ethnicity as a covariate in the current investigation as the sample sizes of some of the represented ethnic groups in our study are too small and hence insufficient for adequate statistical adjustment. While our study sample includes certain large homogenous ethnic groups (i.e. East Asian, white-Caucasian, and South Asian), as we have indicated in Supplementary Table 1, the other ethnic groups in our sample are not sufficiently represented in terms of sample size. For example, the Middle Eastern ethnic group contains only 17 participants (6 males, 11 females). Similarly, in our sample there are 14 participants that collectively belong to other ethnic groups (e.g. African, Latin American etc.) with insufficient representation of each ethnic group in our study sample. As such, given that the number of participants in each of the individual ethnic categories are limited, we believe that the conclusions drawn based on such low subgroup sizes would be skewed due to inadequate power. We have now added this as a limitation in the discussion section of the paper to highlight this comment.

“We could not investigate ethnicity as a potential covariate in our present analysis due to the limited sample size of certain ethnic subgroups in our overall study sample.”

6.- Authors mention in the Discussion section an increase in BMI with no increase in WHR; a deeper discussion of the nutritional and metabolic meaning of this finding would be welcome.

A: We thank the reviewer for this inspired comment. We have now added a paragraph in the discussion section of the paper that addresses these points.

“It is important to note here that the weight gain observed in our sample may not be entirely attributed to an increase in fat, but also to additional contributing factors such as continued development and increase in muscle mass. Unfortunately, in this case, we could not evaluate parameters such as lean mass or fat mass. Nonetheless, our investigation of adiposity indicators, such as waist and hip circumference, revealed significant increases in those areas among both male and female participants. Hence, based on our data, we postulate that one of the components contributing to the observed weight gain in our sample may possibly be a potential increase in fat. However, we acknowledge that there may be additional contributing factors as discussed above, and recognize that the data is limited in terms of the information it provides to characterize the observed change.”

R: Thank you for the new discussion.

Thank you so much for the feedback.

7.- Full data are not available in a public repository, and there is no Data availability statement in the text. Please, provide a link to download the full, anonymized data in a public repository.

A: Thank you for bringing up this important point. The dataset has now been included.

R: Thank you for this dataset, it is very useful.

Thank you so much for the feedback.

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file. (23.2KB, docx)

Decision Letter 2

Mauro Lombardo

12 Jan 2021

PONE-D-20-20612R2

Effect of sex/gender on obesity traits in Canadian first year university students: the GENEiUS study

PLOS ONE

Dear Dr. Meyre,

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #5: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #5: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #5: I Don't Know

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #5: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #5: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #5: In this work, Sharma and colleagues perform a longitudinal study investigating the morphometric evolution of first year University students in the McMaster University (Ontario, Canada). During this first year, several previous reports had shown a significant increase in BW and BMI. Some of them had reported significant sex-specific trends in these parameters, although the existing literature was heterogeneous and not conclusive. This new study, although including a low number of individuals (245), confirms a significant increase in BW, BMI, WC and HC in the students, with no significant difference between sexes, although males showed a tendency to greater increases in BW, BMI, WC and HC. This work, although limited, adds to previous reports using a longitudinal and relatively more complete assessment of morphometric parameters, and could be of use for future meta-analysis.

I have several comments:

1.- Statistical methods: it is not clear why authors choose to use the non-parametric tests for baseline and pairwise comparison of outcomes (Tables 1 and 2); while using the RMANOVA test for other year- and sex-specific outcomes. I suggest that outcomes are analyzed for their parametric or non-parametric distribution, justifying and homogenizing the choice of the statistical tests used.

A: Thank you for the comment. In this case, given that our investigation was a follow-up to a previous report by Beaudry et al (2019), we tried to largely follow the same analytical methodology as the paper by Beaudry et al. (i.e. RMANVOA). Nevertheless, we further included a couple of additional elements of analysis in our paper (e.g. analyzing differences in obesity traits between males and females at baseline) that were not present in the previous paper by Beaudry and colleagues. Particularly, we used non- parametric tests for evaluation of traits without adjustment for covariates and used the RMANOVA test with transformation for analysis of traits with adjustment for covariates. We recognize that there are different analytical approaches that be used to analyze the data. In fact, we used a different approach involving regression analysis for our other papers on the effect of living arrangement and ethnicity on anthropometric that are currently published (Sharma et al., PLOS One 2020) or under revision. In this case, we feel that the respective tests used here are appropriate, and do not see the need to homogenize the approach.

R: it sounds odd that the only argument to justify the use of a statistical method is “that others also used it before”, rather than proving that the statistical method is the most appropriate for the kind of data analyzed.

A2: Thank you for the comment. We agree “that others also used it before” would not be a sufficient argument to apply the same approach. However, it seems the reviewer overlooked the other arguments we provided to justify our approach. Firstly, we think that the statistical methods proposed by Beaudry et al. are adequate to answer the questions asked. Secondly, the manuscript by Beaudry et al. has been reviewed and validated by experts, as part of the peer-review process, so we are not the only ones to think that the statistical approach used by Beaudry et al. is adequate. Thirdly, given the follow-up nature of our investigation, using a drastically different statistical approach may have added heterogeneity and impaired our ability to compare our results to those of Beaudry et al. Fourthly, we have provided additional elements of analysis in our paper that were not present in Beaudry’s paper and hence those methods differ –particularly with respect to using non-parametric tests for a raw comparison of traits in the absence of covariates, and the using RMANOVA, as done by Beaudry et al. for the comparison of traits with adjustment for covariates. We believe these are adequate reasons to justify our approach. However, we understand well the reviewer’s concern and we have added a limitation in the discussion to reflect their important point. “In this investigation, we followed the analysis protocol outlined by Beaudry at al. to optimize our ability to compare our results. However, we are aware that alternative statistical methods can also be utilized to analyze this data.

R2: I leave this matter for the Editor to decide. I still think that an evaluation of the parametric and non-parametric type of data would support the correct use of the chosen statistical methods, and would complete the information presented in the paper. Although I also acknowledge the reasons given by the authors for not changing the statistical methods from Beaudry et al., still if the data are not suited for a precise statistical method, using it is incorrect and could help fixing a previous mistake.

2.- Authors claim several times throughout the text as a relevant finding that males presented with larger BW, WC or HC than females, while these parameters are usually larger in males than in females. In my opinion, only the higher BMI and WHR in males is a relevant finding, showing a stronger tendency to obesity in males.

A: Thank you for the comment. While we agree with the reviewer that BMI and WHR are critical parameters and could be the focus of a paper, we feel that it is still important to discuss the other parameters in the text as well considering that the data for these traits have been presented in the paper. Given that we conducted this investigation as a follow-up to the previous paper by Beaudry et al, we assessed and discussed the same traits as the ones discussed by Beaudry and colleagues. Additionally, in recent times, there has generally been an increasing amount of the literature on the prognostic value of indicators such as WC and hence including these results may be of interest to the readers.

R: I think my point was not well understood. I agree that analyzing changes in BW, WC and HC of individuals with time is a very valuable analysis. What I do not agree is on the treatment as a relevant finding of the difference between male and female volunteers in BW, WC and HC: males always display larger BW, WC and HC in average than females, that is not biologically relevant.

A2: Thank you for the clarification. While we agree with the reviewer that the observation that males always display larger BW, WC and HC on average is a well-established observation in literature, we still feel it is important to include this finding in our report for a couple of reasons. Firstly, we do think that males being larger than females is a biologically important observation. Secondly, in any case, we believe that before discussing the ‘change’ in traits across groups, it is imperative that the baseline distribution of the traits are assessed and reported, as knowing the baseline distribution provides context to better interpret values of change. In this case, while this may seem redundant given the extensive amount of literature on sex-based anthropometric patterns, we believe it is still important that we confirm this finding in our sample and provide that baseline context for the readers before providing a discussion on the magnitude of change observed in these traits. Apart from that, it should be noted that we only discussed this observation very briefly in our result and discussion sections with only a few lines dedicated this observation in the entire manuscript. We are aware that this is a common observation and hence it is definitely not something we have discussed frequently or emphasized as a core point of discussion in our manuscript. In fact, in our brief discussion of this observation, we also particularly acknowledge that this is a well-established observation in different age groups in literature (lines 312-315). Our discussion on the change in traits definitely makes up the majority of our paper.

R2: I agree with the author’s response, thank you.

3.- Since the mean topic of the work is the difference of sex in weight gain during first-year university, the ratio male/female in the cohort is a very important piece of information. However, it is not shown until the Results section. Here, authors report that 80.4% of the sample were female, but it is not clear to what cohort is this % referring to. Since it is presented in the same paragraph where the initial recruited population is described, (n=361), I understand that it 80.4% of this population are females. Therefore, only 76 males were included in the study. However, in Table 2 authors indicate a 19.6% (n=48) males, so maybe the 80.4% females indicated earlier in the Results section referred to the population that completed the follow-up. This piece of information (% of females/males at every stage of the study) is very important for this precise work but is not clearly shown in the text until the final part of the Discussion section. I think that it should be emphasized and made clear from the beginning, including the abstract.

Also, and related to this, the ethnicity and living arrangements were only shown for the total population, but not indicated for each sex, which could also shed some light to the differences.

A: This is an excellent point. We have now updated the text to better reflect the cohort information and have included a supplementary table the described the ethnicity and living arrangement distribution by sex/gender.

R: thank you for your efforts. I still do not see any mention to the males/females ratio in the abstract, where I think that it is a very relevant piece of information. Please, indicate where exactly in the text these changes were included, to be able to verify them easily.

A2: The size of the analyzed sample and the gender ratio has now been specified in the abstract (line 29), methods section (lines 120-122), and the results section (line 172,175). Thank you.

R2: Thank you for this clarification.

4.- Tables 1 and 2: indicate the precise statistical methods used for these tables. I assume, from the Methods section, that the Wilcoxon signed-rank test was performed for Table 1, and the Mann-Whitney U test was used for Table 2, since Table 1 shows the paired data and Table 2 shows the baseline. But I think it should be stated in the legends for clarity.

A: The reviewer brings up an important point. We have now updated the legends of both the tables as per the reviewer’s recommendation.

R: thank you for your efforts, it is more complete now.

5.- In Table 3 there are a few unclear notes (1, 2), that are hard to find in the table text. In particular, it is not clear to me why only BW, WC and WHR were calculated using living arrangement as a covariate, and BMI also used cohort as covariate. Also, it is not clear to me the nature of the “cohort” covariate, a clear explanation would be welcome.

Also it would be interesting to see if ethnicity was a relevant covariate. I assume that, since it was not included, it was not; but I would mention it in the text. Author indicate ethnicity as a possible reason for the discrepancy between this study and previous ones; but the data presented in this study does not indicate that ethnicity is a relevant covariate.

A: Thank you for the comment. In this case, we used the same statistical methodology and covariate adjustment strategy as the previous paper by Beaudry et al (2019). As such, in accordance with the protocol of Beadry et al, the covariates were only retained in the model if their interaction with the main effect (i.e. time) was significant or marginally significant. We have included this detail in the statistical methods section and have included a reference to the paper by Beaudry et al. (2019) for readers who are interested in reading their protocol in further detail. Cohort refers to the cohort of recruitment in this case. We have updated the legend of Table 3 to better reflect this. While we agree that ethnicity is an interesting variable to test, we did not explore it as a covariate in this case particularly because it was not also included as a covariate by Beaudry et al. in their paper. Hence, in order to keep the methodology consistent, we only explored the variables that were evaluated in the previous paper. However, we so have another paper that is currently in revision at PLOS One that examines the effect of ethnicity on anthropometric traits in GENEiUS.

R: Thank you for your efforts. I understand that only the covariates that interacted significantly (or marginally singifficantly) with the main effect (time) were included. It would be useful to know the exact degree of significance for the analyzed variables, since the term “marginally” significant is not very clear. Also, authors indicate that they did not include ethnicity “because it was not included in Beaudry et al”; again, this is not a valid answer, because this is a different study. Was ethnicity significantly interacting with time? If it was, it should be included; if notit was not, that is a valid argument, not Beaudry’s.

A2: Thank you for the comment. We have now specified our definition of marginal p-value (i.e. p<0.1) in the methods section of the paper for further clarity. With regards to the analysis of ethnicity as a covariate, as discussed previously, we did not initially intend on exploring ethnicity as a covariate as we followed the analytical protocol, covariate adjustment strategy, outlined by Beaudry et al. (2019) in order to minimize the heterogeneity between the study methodologies for better comparability of results. While this is a “different study, we believe that this is still an important consideration as changing the analytical strategy can introduce methodological heterogeneity and consequently reduce comparability, which is not favorable for a follow up investigation like ours. Nevertheless, we acknowledge the reviewer’s point and recognize the merit in assessing ethnicity as a potential covariate. However, in this case, we are unable to assess ethnicity as a covariate in the current investigation as the sample sizes of some of the represented ethnic groups in our study are too small and hence insufficient for adequate statistical adjustment. While our study sample includes certain large homogenous ethnic groups (i.e. East Asian, white-Caucasian, and South Asian), as we have indicated in Supplementary Table 1, the other ethnic groups in our sample are not sufficiently represented in terms of sample size. For example, the Middle Eastern ethnic group contains only 17 participants (6 males, 11 females). Similarly, in our sample there are 14 participants that collectively belong to other ethnic groups (e.g. African, Latin American etc.) with insufficient representation of each ethnic group in our study sample. As such, given that the number of participants in each of the individual ethnic categories are limited, we believe that the conclusions drawn based on such low subgroup sizes would be skewed due to inadequate power. We have now added this as a limitation in the discussion section of the paper to highlight this comment. “We could not investigate ethnicity as a potential covariate in our present analysis due to the limited sample size of certain ethnic subgroups in our overall study sample.”

R2: Thank you for this new information, I think it is clearer now.

6.- Authors mention in the Discussion section an increase in BMI with no increase in WHR; a deeper discussion of the nutritional and metabolic meaning of this finding would be welcome.

A: We thank the reviewer for this inspired comment. We have now added a paragraph in the discussion section of the paper that addresses these points.

“It is important to note here that the weight gain observed in our sample may not be entirely attributed to an increase in fat, but also to additional contributing factors such as continued development and increase in muscle mass. Unfortunately, in this case, we could not evaluate parameters such as lean mass or fat mass. Nonetheless, our investigation of adiposity indicators, such as waist and hip circumference, revealed significant increases in those areas among both male and female participants. Hence, based on our data, we postulate that one of the components contributing to the observed weight gain in our sample may possibly be a potential increase in fat. However, we acknowledge that there may be additional contributing factors as discussed above, and recognize that the data is limited in terms of the information it provides to characterize the observed change.”

R: Thank you for the new discussion.

7.- Full data are not available in a public repository, and there is no Data availability statement in the text. Please, provide a link to download the full, anonymized data in a public repository.

A: Thank you for bringing up this important point. The dataset has now been included.

R: Thank you for this dataset, it is very useful.

**********

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Reviewer #5: No

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PLoS One. 2021 Feb 16;16(2):e0247113. doi: 10.1371/journal.pone.0247113.r006

Author response to Decision Letter 2

18 Jan 2021

We would like to thank the editor and reviewer 5 for their feedback on the second revision of our article. We have addressed their comments below to the best of our ability, and we think that the manuscript is now mature for publication.

Reviewer #5: In this work, Sharma and colleagues perform a longitudinal study investigating the morphometric evolution of first year University students in the McMaster University (Ontario, Canada). During this first year, several previous reports had shown a significant increase in BW and BMI. Some of them had reported significant sex-specific trends in these parameters, although the existing literature was heterogeneous and not conclusive. This new study, although including a low number of individuals (245), confirms a significant increase in BW, BMI, WC and HC in the students, with no significant difference between sexes, although males showed a tendency to greater increases in BW, BMI, WC and HC. This work, although limited, adds to previous reports using a longitudinal and relatively more complete assessment of morphometric parameters, and could be of use for future meta-analysis.

We would like to thank the reviewer for the constructive comment.

I have several comments:

1.- Statistical methods: it is not clear why authors choose to use the non-parametric tests for baseline and pairwise comparison of outcomes (Tables 1 and 2); while using the RMANOVA test for other year- and sex-specific outcomes. I suggest that outcomes are analyzed for their parametric or non-parametric distribution, justifying and homogenizing the choice of the statistical tests used.

A: Thank you for the comment. In this case, given that our investigation was a follow-up to a previous report by Beaudry et al (2019), we tried to largely follow the same analytical methodology as the paper by Beaudry et al. (i.e. RMANVOA). Nevertheless, we further included a couple of additional elements of analysis in our paper (e.g. analyzing differences in obesity traits between males and females at baseline) that were not present in the previous paper by Beaudry and colleagues. Particularly, we used non- parametric tests for evaluation of traits without adjustment for covariates and used the RMANOVA test with transformation for analysis of traits with adjustment for covariates. We recognize that there are different analytical approaches that be used to analyze the data. In fact, we used a different approach involving regression analysis for our other papers on the effect of living arrangement and ethnicity on anthropometric that are currently published (Sharma et al., PLOS One 2020) or under revision. In this case, we feel that the respective tests used here are appropriate, and do not see the need to homogenize the approach.

R: it sounds odd that the only argument to justify the use of a statistical method is “that others also used it before”, rather than proving that the statistical method is the most appropriate for the kind of data analyzed.

A2: Thank you for the comment. We agree “that others also used it before” would not be a sufficient argument to apply the same approach. However, it seems the reviewer overlooked the other arguments we provided to justify our approach. Firstly, we think that the statistical methods proposed by Beaudry et al. are adequate to answer the questions asked. Secondly, the manuscript by Beaudry et al. has been reviewed and validated by experts, as part of the peer-review process, so we are not the only ones to think that the statistical approach used by Beaudry et al. is adequate. Thirdly, given the follow-up nature of our investigation, using a drastically different statistical approach may have added heterogeneity and impaired our ability to compare our results to those of Beaudry et al. Fourthly, we have provided additional elements of analysis in our paper that were not present in Beaudry’s paper and hence those methods differ –particularly with respect to using non-parametric tests for a raw comparison of traits in the absence of covariates, and the using RMANOVA, as done by Beaudry et al. for the comparison of traits with adjustment for covariates. We believe these are adequate reasons to justify our approach. However, we understand well the reviewer’s concern and we have added a limitation in the discussion to reflect their important point. “In this investigation, we followed the analysis protocol outlined by Beaudry at al. to optimize our ability to compare our results. However, we are aware that alternative statistical methods can also be utilized to analyze this data.

R2: I leave this matter for the Editor to decide. I still think that an evaluation of the parametric and non-parametric type of data would support the correct use of the chosen statistical methods, and would complete the information presented in the paper. Although I also acknowledge the reasons given by the authors for not changing the statistical methods from Beaudry et al., still if the data are not suited for a precise statistical method, using it is incorrect and could help fixing a previous mistake.

Thank you for the feedback. Once again, we respectfully disagree with the reviewer that performing non-parametric analyses on untransformed longitudinal data adjusted for covariates is needed in our study, and that for different reasons:

1) The statistical methods proposed by Beaudry et al. are adequate to answer the questions asked. Anthropometric data are skewed and depart systematically from normality. Performing RMANOVA on non-normal longitudinal data following its inverse normal rank transformation is the best analytical strategy to deal with the lack of normality of data.

2) The previous publication by Beaudry et al. has been published in PLOS One and has been validated by an academic editor and at least two reviewers. This is a strong indication that the statistical approach used by Beaudry et al. and more recently by us is adequate. If the reviewer really thinks that the statistical design in the Beaudry et al. publication is inadequate, their first action may be to post a comment on the PLOS One website and ask for a formal correction of the article. We checked the Beaudry et al. publication on the website and since its publication 1.5 years ago, no comment has been posted regarding the Baudry’s publication. Knowing that the article has been viewed by 5,800 people, the absence of negative comment strongly indicates that the reader’s community is comfortable with the statistical methods used in the paper.

3) Given the follow-up nature of our investigation, using a drastically different statistical approach may have added heterogeneity and impaired our ability to compare our results to those of Beaudry et al.

4) We have provided additional elements of analysis in our paper that were not present in Beaudry’s paper and hence those methods differ –particularly with respect to using non-parametric tests for a raw comparison of traits in the absence of covariates. As we use both parametric an non-parametric statistical analyses in our study, we do not understand why the reviewer is not satisfied.

5) The method used by Beaudry et al. (RMANOVA with transformed data) is by far the most frequently used method in literature when it comes to analyzing longitudinal series of non-normal data with adjustment for covariates. As the reviewer did not provide any guidance on the type of statistical tests he wanted us to use, we had to make an extensive literature search to find examples of non-parametric tests that allow the analysis of longitudinal series of untransformed non-normal data with adjustment for covariates. Unfortunately, we did not find any alternative non-parametric method that we apply to our study. This indicates that the reviewer’s request is very unconventional.

We feel that the reviewer’s repeated request is kind of odd and is very challenging to address in practice. This is the third time we provide a strong justification for our analytical design, and we do not understand why the reviewer is not more receptive to our arguments. We leave this matter for the Editor to decide, but we hope he is satisfied with our answers. We have added a limitation in the discussion to reflect the reviewer’s comment. “In this investigation, we followed the analysis protocol outlined by Beaudry at al. to optimize our ability to compare our results. However, we are aware that alternative statistical methods may also be utilized to analyze this data.

2.- Authors claim several times throughout the text as a relevant finding that males presented with larger BW, WC or HC than females, while these parameters are usually larger in males than in females. In my opinion, only the higher BMI and WHR in males is a relevant finding, showing a stronger tendency to obesity in males.

A: Thank you for the comment. While we agree with the reviewer that BMI and WHR are critical parameters and could be the focus of a paper, we feel that it is still important to discuss the other parameters in the text as well considering that the data for these traits have been presented in the paper. Given that we conducted this investigation as a follow-up to the previous paper by Beaudry et al, we assessed and discussed the same traits as the ones discussed by Beaudry and colleagues. Additionally, in recent times, there has generally been an increasing amount of the literature on the prognostic value of indicators such as WC and hence including these results may be of interest to the readers.

R: I think my point was not well understood. I agree that analyzing changes in BW, WC and HC of individuals with time is a very valuable analysis. What I do not agree is on the treatment as a relevant finding of the difference between male and female volunteers in BW, WC and HC: males always display larger BW, WC and HC in average than females, that is not biologically relevant.

A2: Thank you for the clarification. While we agree with the reviewer that the observation that males always display larger BW, WC and HC on average is a well-established observation in literature, we still feel it is important to include this finding in our report for a couple of reasons. Firstly, we do think that males being larger than females is a biologically important observation. Secondly, in any case, we believe that before discussing the ‘change’ in traits across groups, it is imperative that the baseline distribution of the traits are assessed and reported, as knowing the baseline distribution provides context to better interpret values of change. In this case, while this may seem redundant given the extensive amount of literature on sex-based anthropometric patterns, we believe it is still important that we confirm this finding in our sample and provide that baseline context for the readers before providing a discussion on the magnitude of change observed in these traits. Apart from that, it should be noted that we only discussed this observation very briefly in our result and discussion sections with only a few lines dedicated this observation in the entire manuscript. We are aware that this is a common observation and hence it is definitely not something we have discussed frequently or emphasized as a core point of discussion in our manuscript. In fact, in our brief discussion of this observation, we also particularly acknowledge that this is a well-established observation in different age groups in literature (lines 312-315). Our discussion on the change in traits definitely makes up the majority of our paper.

R2: I agree with the author’s response, thank you.

We would like to thank the reviewer for the constructive comment.

3.- Since the mean topic of the work is the difference of sex in weight gain during first-year university, the ratio male/female in the cohort is a very important piece of information. However, it is not shown until the Results section. Here, authors report that 80.4% of the sample were female, but it is not clear to what cohort is this % referring to. Since it is presented in the same paragraph where the initial recruited population is described, (n=361), I understand that it 80.4% of this population are females. Therefore, only 76 males were included in the study. However, in Table 2 authors indicate a 19.6% (n=48) males, so maybe the 80.4% females indicated earlier in the Results section referred to the population that completed the follow-up. This piece of information (% of females/males at every stage of the study) is very important for this precise work but is not clearly shown in the text until the final part of the Discussion section. I think that it should be emphasized and made clear from the beginning, including the abstract.

Also, and related to this, the ethnicity and living arrangements were only shown for the total population, but not indicated for each sex, which could also shed some light to the differences.

A: This is an excellent point. We have now updated the text to better reflect the cohort information and have included a supplementary table the described the ethnicity and living arrangement distribution by sex/gender.

R: thank you for your efforts. I still do not see any mention to the males/females ratio in the abstract, where I think that it is a very relevant piece of information. Please, indicate where exactly in the text these changes were included, to be able to verify them easily.

A2: The size of the analyzed sample and the gender ratio has now been specified in the abstract (line 29), methods section (lines 120-122), and the results section (line 172,175). Thank you.

R2: Thank you for this clarification.

We would like to thank the reviewer for the constructive comment.

4.- Tables 1 and 2: indicate the precise statistical methods used for these tables. I assume, from the Methods section, that the Wilcoxon signed-rank test was performed for Table 1, and the Mann-Whitney U test was used for Table 2, since Table 1 shows the paired data and Table 2 shows the baseline. But I think it should be stated in the legends for clarity.

A: The reviewer brings up an important point. We have now updated the legends of both the tables as per the reviewer’s recommendation.

R: thank you for your efforts, it is more complete now.

5.- In Table 3 there are a few unclear notes (1, 2), that are hard to find in the table text. In particular, it is not clear to me why only BW, WC and WHR were calculated using living arrangement as a covariate, and BMI also used cohort as covariate. Also, it is not clear to me the nature of the “cohort” covariate, a clear explanation would be welcome.

Also it would be interesting to see if ethnicity was a relevant covariate. I assume that, since it was not included, it was not; but I would mention it in the text. Author indicate ethnicity as a possible reason for the discrepancy between this study and previous ones; but the data presented in this study does not indicate that ethnicity is a relevant covariate.

A: Thank you for the comment. In this case, we used the same statistical methodology and covariate adjustment strategy as the previous paper by Beaudry et al (2019). As such, in accordance with the protocol of Beadry et al, the covariates were only retained in the model if their interaction with the main effect (i.e. time) was significant or marginally significant. We have included this detail in the statistical methods section and have included a reference to the paper by Beaudry et al. (2019) for readers who are interested in reading their protocol in further detail. Cohort refers to the cohort of recruitment in this case. We have updated the legend of Table 3 to better reflect this. While we agree that ethnicity is an interesting variable to test, we did not explore it as a covariate in this case particularly because it was not also included as a covariate by Beaudry et al. in their paper. Hence, in order to keep the methodology consistent, we only explored the variables that were evaluated in the previous paper. However, we so have another paper that is currently in revision at PLOS One that examines the effect of ethnicity on anthropometric traits in GENEiUS.

R: Thank you for your efforts. I understand that only the covariates that interacted significantly (or marginally singifficantly) with the main effect (time) were included. It would be useful to know the exact degree of significance for the analyzed variables, since the term “marginally” significant is not very clear. Also, authors indicate that they did not include ethnicity “because it was not included in Beaudry et al”; again, this is not a valid answer, because this is a different study. Was ethnicity significantly interacting with time? If it was, it should be included; if notit was not, that is a valid argument, not Beaudry’s.

A2: Thank you for the comment. We have now specified our definition of marginal p-value (i.e. p<0.1) in the methods section of the paper for further clarity. With regards to the analysis of ethnicity as a covariate, as discussed previously, we did not initially intend on exploring ethnicity as a covariate as we followed the analytical protocol, covariate adjustment strategy, outlined by Beaudry et al. (2019) in order to minimize the heterogeneity between the study methodologies for better comparability of results. While this is a “different study, we believe that this is still an important consideration as changing the analytical strategy can introduce methodological heterogeneity and consequently reduce comparability, which is not favorable for a follow up investigation like ours. Nevertheless, we acknowledge the reviewer’s point and recognize the merit in assessing ethnicity as a potential covariate. However, in this case, we are unable to assess ethnicity as a covariate in the current investigation as the sample sizes of some of the represented ethnic groups in our study are too small and hence insufficient for adequate statistical adjustment. While our study sample includes certain large homogenous ethnic groups (i.e. East Asian, white-Caucasian, and South Asian), as we have indicated in Supplementary Table 1, the other ethnic groups in our sample are not sufficiently represented in terms of sample size. For example, the Middle Eastern ethnic group contains only 17 participants (6 males, 11 females). Similarly, in our sample there are 14 participants that collectively belong to other ethnic groups (e.g. African, Latin American etc.) with insufficient representation of each ethnic group in our study sample. As such, given that the number of participants in each of the individual ethnic categories are limited, we believe that the conclusions drawn based on such low subgroup sizes would be skewed due to inadequate power. We have now added this as a limitation in the discussion section of the paper to highlight this comment. “We could not investigate ethnicity as a potential covariate in our present analysis due to the limited sample size of certain ethnic subgroups in our overall study sample.”

R2: Thank you for this new information, I think it is clearer now.

We would like to thank the reviewer for the constructive comment.

6.- Authors mention in the Discussion section an increase in BMI with no increase in WHR; a deeper discussion of the nutritional and metabolic meaning of this finding would be welcome.

A: We thank the reviewer for this inspired comment. We have now added a paragraph in the discussion section of the paper that addresses these points.

“It is important to note here that the weight gain observed in our sample may not be entirely attributed to an increase in fat, but also to additional contributing factors such as continued development and increase in muscle mass. Unfortunately, in this case, we could not evaluate parameters such as lean mass or fat mass. Nonetheless, our investigation of adiposity indicators, such as waist and hip circumference, revealed significant increases in those areas among both male and female participants. Hence, based on our data, we postulate that one of the components contributing to the observed weight gain in our sample may possibly be a potential increase in fat. However, we acknowledge that there may be additional contributing factors as discussed above, and recognize that the data is limited in terms of the information it provides to characterize the observed change.”

R: Thank you for the new discussion.

7.- Full data are not available in a public repository, and there is no Data availability statement in the text. Please, provide a link to download the full, anonymized data in a public repository.

A: Thank you for bringing up this important point. The dataset has now been included.

R: Thank you for this dataset, it is very useful.

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file. (21.8KB, docx)

Decision Letter 3

Mauro Lombardo

27 Jan 2021

PONE-D-20-20612R3

Effect of sex/gender on obesity traits in Canadian first year university students: the GENEiUS study

PLOS ONE

Dear Dr. Meyre,

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PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #5: (No Response)

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Reviewer #5: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #5: I Don't Know

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Reviewer #5: Yes

**********

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Reviewer #5: Yes

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6. Review Comments to the Author

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Reviewer #5: There is only one question remaining, all the other comments were properly answered by authors. Congratulations to the authors for their work.

This is my original question:

1.- Statistical methods: it is not clear why authors choose to use the non-parametric tests for baseline and pairwise comparison of outcomes (Tables 1 and 2); while using the RMANOVA test for other year- and sex-specific outcomes. I suggest that outcomes are analyzed for their parametric or non-parametric distribution, justifying and homogenizing the choice of the statistical tests used.

I am afraid that there has been a lack of communication with this issue in subsequent answers. The data obtained in the repeated measures of the study are not normal, and this is why authors use non-parametric tests (Mann-Whitney or Wilcoxon signed-rank tests) to analyze these data. However, when analyzing differences in anthropometric outcomes over the year and the effect of sex on anthropometric change, authors perform an inverse normal rank transformation that converts their non-normal data into normal data. This transformed data is proposed to be now suitable for RMANOVA analysis.

Probably this strategy has been used in other reports previously, and I assume that, since transformed data are now normal, the test is correct. I was not aware of the use of this statistical strategy, and this is why I asked. I strongly suggest that authors cite previous similar reports to support this statistical strategy (they mention the existence of several reports, but refer to none in particular).

I understand that using non-parametric tests for the analysis of non-normal repeated measures would have been the best option. There are several of these tests available:

https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.4780101210

https://www.jstatsoft.org/article/view/v050i12

https://www.jstatsoft.org/article/view/v064i09

Actually, normal transformation is a question of debate in the statistical field (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2921808/).

However, I also understand that, if this transformation is a common practice, and it is the strategy followed by the paper that is being used as a model by the authors (Beaudry et al.,), it makes sense to keep the transformation + RMANOVA for this precise analysis, since, strictly speaking, transformed data are normal.

I have not more to say in this point. I would have acknowledged this simple clarification (transformed data are normal, once transformed) when I first asked. I think we all have learnt with this revision!.

**********

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PLoS One. 2021 Feb 16;16(2):e0247113. doi: 10.1371/journal.pone.0247113.r008

Author response to Decision Letter 3

29 Jan 2021

Reviewer #5: There is only one question remaining, all the other comments were properly answered by authors. Congratulations to the authors for their work.

We would like to thank the editor and the reviewer for their feedback on the third revision of our article. We have addressed their comments below to the best of our ability.

This is my original question:

1.- Statistical methods: it is not clear why authors choose to use the non-parametric tests for baseline and pairwise comparison of outcomes (Tables 1 and 2); while using the RMANOVA test for other year- and sex-specific outcomes. I suggest that outcomes are analyzed for their parametric or non-parametric distribution, justifying and homogenizing the choice of the statistical tests used.

I am afraid that there has been a lack of communication with this issue in subsequent answers. The data obtained in the repeated measures of the study are not normal, and this is why authors use non-parametric tests (Mann-Whitney or Wilcoxon signed-rank tests) to analyze these data. However, when analyzing differences in anthropometric outcomes over the year and the effect of sex on anthropometric change, authors perform an inverse normal rank transformation that converts their non-normal data into normal data. This transformed data is proposed to be now suitable for RMANOVA analysis.

Probably this strategy has been used in other reports previously, and I assume that, since transformed data are now normal, the test is correct. I was not aware of the use of this statistical strategy, and this is why I asked. I strongly suggest that authors cite previous similar reports to support this statistical strategy (they mention the existence of several reports, but refer to none in particular).

We thank the reviewer for the clarification. We now mention in the Methods section that the inverse normal rank transformation resulted in the normality of the transformed data distribution. This means that our RMANOVA analysis of these transformed data is valid, as acknowledged by the reviewer. We also cite previous reports where the same transformation has been used.

I understand that using non-parametric tests for the analysis of non-normal repeated measures would have been the best option. There are several of these tests available:

https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.4780101210

https://www.jstatsoft.org/article/view/v050i12

https://www.jstatsoft.org/article/view/v064i09

Actually, normal transformation is a question of debate in the statistical field (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2921808/).

However, I also understand that, if this transformation is a common practice, and it is the strategy followed by the paper that is being used as a model by the authors (Beaudry et al.,), it makes sense to keep the transformation + RMANOVA for this precise analysis, since, strictly speaking, transformed data are normal.

The reviewer makes a relevant point, thank you. We have now added as a limitation in the Discussion that normal transformation is a question of debate in the statistical field.

I have not more to say in this point. I would have acknowledged this simple clarification (transformed data are normal, once transformed) when I first asked. I think we all have learnt with this revision!

Once again, we thank the reviewer for the very helpful clarification.

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file. (14KB, docx)

Decision Letter 4

Mauro Lombardo

2 Feb 2021

Effect of sex/gender on obesity traits in Canadian first year university students: the GENEiUS study

PONE-D-20-20612R4

Dear Dr. Meyre,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Mauro Lombardo

Academic Editor

PLOS ONE

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1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #5: All comments have been addressed

**********

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #5: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #5: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #5: Yes

**********

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Reviewer #5: Yes

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Reviewer #5: (No Response)

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Reviewer #5: No

Acceptance letter

Mauro Lombardo

5 Feb 2021

PONE-D-20-20612R4

Effect of sex/gender on obesity traits in Canadian first year university students: the GENEiUS study

Dear Dr. Meyre:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Mauro Lombardo

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PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Data

    (XLSX)

    Click here for additional data file. (36.6KB, xlsx)
    S1 Table. Distribution of demographic characteristics in the overall sample (n = 245) and in the male (n = 48) and female (n = 197) subgroups.

    (DOCX)

    Click here for additional data file. (13.3KB, docx)
    S2 Table. Sex-specific trends in obesity traits from the beginning to the end of first year by male (n = 48) and female (n = 197) subgroups.

    (DOCX)

    Click here for additional data file. (14.2KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

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    Submitted filename: Response to reviewers.docx

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    Submitted filename: Response to reviewers.docx

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    Data Availability Statement

    All relevant data are within the manuscript and its Supporting Information files.

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