Metabolic response against traffic congestion-induced stress

Mirasari Putri; Hilmi Sulaiman Rathomi; Fajar Awalia Yulianto; Raden Aliya T. M. Djajanagara; Dony Septriana Rosady; Sadiah Achmad; M. Ahmad Djojosugito

doi:10.12688/f1000research.73795.1

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Research Article

Metabolic response against traffic congestion-induced stress

[version 1; peer review: 1 approved with reservations, 1 not approved]

Mirasari Putri ¹, Hilmi Sulaiman Rathomi², Fajar Awalia Yulianto², [...] Raden Aliya T. M. Djajanagara³, Dony Septriana Rosady⁴, Sadiah Achmad¹, M. Ahmad Djojosugito⁵

Mirasari Putri ¹, Hilmi Sulaiman Rathomi², [...] Fajar Awalia Yulianto², Raden Aliya T. M. Djajanagara³, Dony Septriana Rosady⁴, Sadiah Achmad¹, M. Ahmad Djojosugito⁵

PUBLISHED 23 Nov 2021

Author details Author details

¹ Department of Biochemistry, Nutrition, and Biomolecular, Faculty of Medicine, Universitas Islam Bandung, Bandung, West Java, 40116, Indonesia
² Department of Public Health, Faculty of Medicine, Universitas Islam Bandung, Bandung, West Java, 40116, Indonesia
³ Faculty of Medicine, Universitas Islam Bandung, Bandung, West JAva, 40116, Indonesia
⁴ Department of Medical education, humanities and bioethics, Faculty of Medicine, Universitas Islam Bandung, Bandung, West Java, 40116, Indonesia
⁵ Department of Surgery, Faculty of Medicine, Universitas Islam Bandung, Bandung, West Java, 40116, Indonesia

Mirasari Putri
Roles: Conceptualization, Data Curation, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Hilmi Sulaiman Rathomi
Roles: Formal Analysis, Methodology, Software, Writing – Original Draft Preparation

Fajar Awalia Yulianto
Roles: Formal Analysis, Methodology, Writing – Original Draft Preparation

Raden Aliya T. M. Djajanagara
Roles: Project Administration, Visualization, Writing – Original Draft Preparation

Dony Septriana Rosady
Roles: Data Curation, Investigation, Project Administration

Sadiah Achmad
Roles: Writing – Review & Editing

M. Ahmad Djojosugito
Roles: Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Research Synergy Foundation gateway.

Abstract

Background: Traffic congestion is a common problem in large cities, which can induce stress in participants in vehicles as well as pedestrians. In such conditions, several stress hormones, such as catecholamines and cortisol are secreted by the adrenal glands resulting in metabolic responses. Prolonged metabolic changes can lead to metabolic diseases, such as metabolic syndrome and cardiovascular disease. This study aimed to examine the metabolic responses induced by traffic congestion in young adults.
Methods: We enrolled 71 undergraduate students aged 20-30 years from the Faculty of Medicine Universitas Islam Bandung, who regularly go to campus on foot, by public transportation, motorcycles, and cars. Medical history was screened by questionnaires before the testing day. Blood samples were drawn soon after participants arrived on campus. We used bioimpedance analysis to measure fasting blood glucose, lipid profiles, and nutrient body status.
Results: Most participants rode motorcycles (46.48%), and the most common travel time was short (<15 min, 54.9%.). Total cholesterol and low-density lipoprotein (LDL) levels were significantly lower in participants using vehicles - either private or public transportation - compared with pedestrians (p<0.05 and p<0.02, respectively). Participants with a short travel duration had significantly higher total cholesterol and LDL levels than those with longer ones (p<0.02 and p<0.05, respectively).
Conclusions: Based on the above results, metabolic responses to traffic congestion such as changes to total cholesterol and LDL levels depended the type of transportation and travel duration.

Keywords

Metabolic responses, Stress, Traffic congestion, Travel duration, Transportation type

Corresponding author: Mirasari Putri

Competing interests: No competing interests were disclosed.

Grant information: This work was supported by a grant from the Faculty of Medicine Universitas Islam Bandung (UNISBA) 2017-2018 to Sadiah Achmad and the team.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2021 Putri M et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Putri M, Rathomi HS, Yulianto FA et al. Metabolic response against traffic congestion-induced stress [version 1; peer review: 1 approved with reservations, 1 not approved]. F1000Research 2021, 10:1179 (https://doi.org/10.12688/f1000research.73795.1) First published: 23 Nov 2021, 10:1179 (https://doi.org/10.12688/f1000research.73795.1) Latest published: 23 Nov 2021, 10:1179 (https://doi.org/10.12688/f1000research.73795.1)

Introduction

A problematic issue in big cities is traffic congestion, which can induce stress in traffic users.¹ Bandung, Indonesia, is ranked 14th on the list of 278 Asian cities with traffic congestion. Until 2013, the number of private four-wheeled vehicles in Bandung was 318,598, and that of two-wheeled ones 1,030,279 (Badan Pusat Statistik Kota Bandung, 2015).²

Stressors can induce both positive and negative responses, determined by a balance between tolerance and sensitivity. Stressors can be helpful; for example, they can motivate the body to produce the energy to get through challenging situations such as exams or job tasks. However, stressors in extreme amounts and over long periods can cause chronic stress affecting the immune system, cardiovascular, neuroendocrine, and central nervous systems.³^,⁴

Stress induces several metabolic responses in the body; for example, the hypothalamus transmits signals directly through the sympathetic nervous system to the adrenal glands, resulting in the production of catecholamines in the adrenal medulla, including epinephrine, which increases the heart rate and respiration.⁵ In addition, norepinephrine, stimulates liver cells to release glucose for cellular respiration.⁵ In contrast, hormones secreted by the adrenal cortex signal a chronic response to stress. In such a case, under stress stimulation, the hypothalamus causes the anterior pituitary to secrete the adrenocorticotrophic hormone (ACTH), which induces cells in the adrenal cortex to produce and secrete corticosteroids, such as glucocorticoids, to break down fat and stimulate glucose synthesis. Cortisol is an example of glucocorticoid involved in this stress response, and regulates or supports various cardiovascular, metabolic, immunological, and homeostatic processes.³

Tsigos (2019) suggested that chronic stress is associated with hypercortisolemia and sympathetic system activation, resulting in metabolic changes. For example, visceral fat accumulation contributes to visceral obesity, type 2 diabetes mellitus (DM-2), and associated cardiometabolic complications.⁶^,⁷ Currently, the prevalence of DM-2 in adults is approximately 6.4% of the world population and will continue to increase to 7.7% of the population by 2030.⁸ The prevalence of DM in urban areas in Indonesia is approximately 5.7%, and the incidence of this disease at young age tends to increase.⁹ This increase deserves particular attention, considering their productive role in society.⁹ However, studies on the effect of chronic stress due to traffic congestion on the metabolism at young age are still limited.

In this study, we investigated whether stress whilst driving would affect metabolic responses by examining several metabolic parameters such as fasting blood glucose, lipid profiles, and nutrient body status in young adult traffic users, which were active students at UNISBA Faculty of Medicine.

Methods

Ethical considerations

The ethics committee of the university approved this study with the number: 003/KEPK-FK/XI/2017. Interviews were in the UNISBA Faculty of Medicine area, after written consent was obtained from the subjects following an explanation about the study, without any coercion.

Study design and setting

This was a cross-sectional study to determine the correlation between driving stress caused by different types of transportation and travel duration, and lipid profile and glucose. The study was conducted from September 2017 to February 2018 on data from second-year and third-year students at UNISBA Faculty of Medicine, Bandung-Indonesia. Participants were approached by visiting classroom and offering participation in this study. Then, interviews for recruitment were conducted in the UNISBA Faculty of Medicine area after participants provided informed consent. The study materials including participant information sheet and questionnaire can be found as Extended data.¹⁹^,²⁰ We collected blood serum samples in the 7th-floor laboratory of the UNISBA Faculty of Medicine.

We followed the STROBE cross-sectional reporting guidelines.¹⁰ Restriction methods (by applying inclusion and exclusion criteria) were applied to variables that biologically possible to compromise the outcome variable, such as metabolic diseases, cholesterol-affecting medications, pancreatic disorders, and smoking-alcohol drinking habits. Valid variable measurements explained later on this section.

Participants

The minimum sample size in this study was 55 people, and calculated using the following correlation test formula:

n = {[\frac{Z α + Z β}{0.5 ln [\frac{(1 + r)}{(1 - r)}]}]}^{2} + 3

With Zα: 1.96 (with α: 0.05), Zβ: 0.842 (with β: 0.2), r: 0.37 (obtained from previous research), N: 55.

To anticipate sample dropouts, the number of samples in this study was 10% of the minimum sample size (60.5) rounded up to 62 people.

We used consecutive sampling to obtain the 71 participants among students in the UNISBA Faculty of Medicine. After conducted interview for recruitment, the selected subjects fulfilled the following inclusion criteria: being active students (active in academic activity during the study period) and having performed academic activities for >1 year (this was to prevent sampling bias due to student adaptation to the campus environment). The exclusion criteria were: taking drugs which can interfere with the results, such as steroids, diazoxide, diuretics, and phenytoin, being active smokers or consuming alcohol, a history of DM-2 or cardiovascular disease, dyslipidemia, obesity, or pancreas abnormalities, family history of DM-2, cardiovascular disease, and dyslipidemia.

Measurement of travel duration

In this study, the travel duration was the time participants needed to go from their residence to campus as measured by themselves using a stopwatch application in their own mobile phone. Subjects were requested to use their usual transportation method to campus. Travel duration data were collected twice on different days within a week before blood sampling collection. As evidence, we asked the participants to share the location twice, namely, when they left the residence and reached the campus lobby. They shared their location and the measurement of their travel duration immediately with the researchers via text messages.

Blood sampling

The participants fasted for 8-10 hours (fasting started 10 PM, night before the blood collection), then blood collection was conducted at 6-8 AM). First, we obtained venous blood 3-5 cc (venipuncture) from the median cubital vein, venous cephalic, or basilica vein. Then, the blood serum was centrifuged (ZENTRIFUGE EBA 200 HETTICH, Tuttlingen, Germany) at 1500 × g for 15 min at 4°C to separate the serum, which was stored at 80°C until further use.

Measurement of body composition

We measured body composition by bioimpedance analysis (BIA) using Omron HBF-375 (Omron Healthcare, Kyoto, Japan). The procedure and normal reference values followed the manufacturer's protocol. Body fat percentage measurement = (body fat mass [kg] /body weight [kg]) × 100, skeletal muscle ratio = (skeletal muscle mass [kg] /body weight [kg]) × 100. As hydration status is essential for accurate body composition assessment using BIA.¹¹ The participants were asked to avoid alcohol the night before the test and not exercise on the day before the test.

Measurement of blood parameters

Blood glucose levels were measured using a glutes sensor (Sanwa Kagaku, Aichi, Japan). In addition, we measured serum triglyceride levels (Triglyceride E-test, Wako Chemical, Osaka), total cholesterol, low-density lipoprotein (LDL), and high-density lipoprotein (HDL) (NEFA C-test Wako Chemical, Osaka) following manufacturer's protocols.

Statistical analysis

We conducted univariate, bivariate, and multivariate data analysis by STATA/MP 16. First, we used univariate analysis to describe the frequency, distribution, and percentage of each variable in the study and specific measures of average, median, standard deviation, and the maximum and minimum values. Next, we performed bivariate analysis using t-test and ANOVA test followed by Dunnett's post hoc multiple comparison tests to determine the relationship between variables, and Pearson's correlation test to determine the power of the correlation if the data obtained had a normal distribution,¹²^,¹³ with p < 0.05 considered as statistically significant. Finally, a logistic regression test was used for the multivariate analysis of the relationship between the variables.

Results

Participant characteristics

Table 1 presents the characteristics of the participants. The total number of participants in the study was 71.¹⁸ Most participants were female, with a mean age of 21 years, and the average body mass index (BMI) was normal. In addition, the participants' average fasting glucose and lipid profiles were normal, but 63.4% of participants had pre-diabetic fasting glucose levels and 12.7% at a diabetic level (Table 2).

Table 1. Participant characteristics.

Category	Frequency (%)	Mean ± SD	Median (range)
Gender
Man	29 (40.9%)
Woman	42 (59.1%)
Age (years)			21 (20-23)
Level
Level 3	25 (35.2%)
Level 4	46 (64.8%)
Bodyweight (kg)		56.1 ± 9.1
Height (cm)		160.6 ± 8.9
BMI (kg/m²)		21.66 ± 2.41
Parameter serum
Fasting glucose ( mg/dL)		107.5 ± 14.8
Total cholesterol ( mg/dL)		168.2 ± 32.6
HDL ( mg/dL)			55.7 (27.6 – 91.9)
LDL ( mg/dL)		91.0 ± 36.1
Triglyceride ( mg/dL)			107.6 (38.8 – 230.9)

Table 2. Obtained parameters.

Parameters	N	%	Median (Range)
Travel duration
Short (<15 minutes)	39	54.9
Medium (15 – 30 minutes)	12	16.9
Long (>30 minutes)	20	28.2
Type of transportation
Pedestrian	15	21.13
Private motorcycle	33	46.48
Private car	15	21.13
Public transportation	8	11.27
Travel duration (Range in minutes)
Pedestrian	15		5 (4 – 9)
Private motorcycle	33		14 (3-58)
Private car	15		36 (8 – 100)
Public transportation	8		6 (4 – 66)
Total of travel duration (Range in minutes)	71		11 (3 – 100)
Fasting glucose
Normal	17	23.9
Pre-diabetic	45	63.4
Diabetes	9	12.7
Total cholesterol
Normal	62	87.3
Borderline	8	11.3
High	1	0.01
HDL
Normal	41	57.8
Low	5	7
High	25	35.2
LDL
Optimal	44	62.9
Almost optimal	18	25.7
Borderline	6	8.6
High	1	1.4
Very High	1	1.4
Triglyceride
Normal	63	88.7
Borderline	7	9.9
High	1	1.4
Total	71	100

Before univariate analysis, the Shapiro–Wilk normality test was conducted. All data had a normal distribution, except for travel duration, HDL, and triglyceride levels. In the variable duration of travel, data transformation to obtain normal data distribution could not be performed, so we converted this variable into a categorical/ordinal variable with the following categories: 1. Short duration (<15 min); 2. Medium duration (15–30 min); 3. Long duration (> 30 min). We performed data transformation using the Log function to conduct a parametric analysis of HDL and triglyceride levels.

Table 2 shows that most respondents had a short travel duration (54.9%), and that motorcycle was the most used type of transportation in this study (46.48%). At the beginning of the research design, we divided public transportation into 2-wheeled and 4-wheeled public vehicles. However, in the study, the participants who used 2-wheeled public vehicles were very few (1 participant), so the categories were combined into general public transportation users. Next, despite the broad range of travel duration in participants using the same means of transportation, the median travel duration was 11 minutes.

Table 3 shows that most respondents had a normal body fat percentage, visceral fat, and skeletal muscle ratio. The mean body fat percentage was 24.4 ± 7.39%, while the mean values of visceral fat and skeletal muscle ratio were 3.5 (0.5-13) and 26.8. There were no significant differences between the types of transportation and duration of travel with the three parameters. Next, we measured the proportion of high and very high body fat percentages. The highest body fat percentage was observed in private car users (46.67%), although the difference was not significant (Table 4).

Table 3. Body fat, visceral fat, and skeletal muscle ratio.

Parameter	Body fat percentage					Visceral fat				Skeletal muscle ratio
Parameter	Low	Normal	High	Really high	Total	Low	Normal	High	Total	Low	Normal	High	Total
Type of transportation
Pedestrian (n)	1	8	5	1	15	1	13	1	15	1	13	1	15
(%)	6.67	53.33	33.33	6.67	100	6.67	86.67	6.67	100	6.67	86.67	6.67	100
Private motorcycle (n)	1	22	8	2	33	8	25	0	33	8	25	0	33
(%)	3.03	66.67	24.24	6.06	100	24.24	75.76	0	100	24.24	75.76	0	100
Private car (n)	1	7	6	1	15	1	14	0	15	1	14	0	15
(%)	6.67	46.67	40	6.67	100	6.67	93.33	0	100	6.67	93.33	0	100
Public transportation (n)	0	6	1	1	8	2	6	0	8	2	6	0	8
(%)	0	75	12.5	12.5	100	25	75	0	100	25	75	0	100
Total (n)	3	43	20	5	71	12	58	1	71	12	58	1	71
(%)	4.23	60.56	28.17	7.04	100	16.9	81.69	1.41	100	16.9	81.69	1.41	100
Travel duration
Short (n)	2	25	11	1	39	4	34	1	39	4	34	1	39
(%)	5.13	64.1	28.21	2.56	100	10.26	87.18	2.56	100	10.26	87.18	2.56	100
Medium (n)	1	6	3	2	12	4	8	0	12	4	8	0	12
(%)	8.33	50	25	16.67	100	33.33	66.67	0	100	33.33	66.67	0	100
Long (n)	0	12	6	2	20	4	16	0	20	4	16	0	20
(%)	0	60	30	10	100	20	80	0	100	20	80	0	100
Total (n)	3	43	20	5	71	12	58	1	71	12	58	1	71
(%)	4.23	60.56	28.17	7.04	100	16.9	81.69	1.41	100	16.9	81.69	1.41	100

Table 4. Differences in high and very high body fat percentages.

Parameters	%	P value
Type of transportation		>0.05
Pedestrian	40
Private motorcycle	30.3
Private car	46.67
Public transportation	25
Travel duration
Short	5
Medium	8
Long	25
Total	100

Next, we conducted a comparative bivariate analysis and Bonferroni's post hoc test. The results showed a significant relationship between the type of transportation and travel duration with total cholesterol (p = 0.0492 and 0.0214) and LDL levels (p = 0.0197 and 0.0494, respectively). However, there was no significant relationship between the two variables and other risk parameters, such as fasting glucose, HDL, and triglycerides (Table 5). Respondents with a short travel duration had higher total cholesterol and LDL levels than those with a long travel duration (177.1 vs. 152.4 mg/dL p = 0.016, 99.6 vs. 75.5 mg/dL p = 0.049, respectively). In addition, we found a significant difference between respondents who walked and those who used vehicles; pedestrians had higher levels of total cholesterol and LDL than other transportation users (p = 0.049 and p = 0.021, respectively)

Table 5. Relationship between travel duration and transportation type with fasting glucose, total cholesterol, HDL, LDL, and triglyceride levels.

Parameter	Fasting glucose (SD)		Total cholesterol		HDL		LDL (SD)		Triglyceride
Parameter	Mean ± SD	P value	Mean ± SD	P value	Range	P value	Mean ± SD	P value	Range	P value
All participants	107.5 ± 14.8		168.2 ± 32.6		53.7 (27.6-91.9)		91.0 ± 36.1		103.3 (38.8-230.9)
Type of transportation
Pedestrian	104.1 ± 17.3	0.7347	185.3 ± 29.6	0.049*	53.8 (27.6-73)	0.4522	109 ± 31.9	0.021*	100.5 (48.3-182.1)	0.438
Private motorcycle	109.3 ± 16.4		169.3 ± 32.7		48.7 (34.7-91.9)		94.2 ± 36.1		98.2 (38.8-230.9)
Private car	106.9 ± 11.2		156.0 ± 32.4		58.1 (45.4-85.2)		74.9 ± 35.9		103.3 (65.7-199.9)
Public transportation	107.3 ± 7.6		154.5 ± 27.2		56.9 (40.5-70)		72.7 ± 27.2		131 (93.7-161.6)
Travel duration
Short	109.2 ± 13.8	0.2917	177.1 ± 30.8	0.019*	53.3 (27.6-88)	0.9121	99.6 ± 24.5	0.049*	101.7 (38.8-230.9)	0.915
Medium	101.5 ± 18.4		165.9 ± 35.4		50.9 (45.6-91.9)		89.0 ± 39.2		97.9 (60.5-165.6)
Long	107.8 ± 14.1		152.4 ± 29.6		55.6 (36.2-85.2)		75.5 ± 33.5		103.8 (52.3-199.9)

* One way-ANOVA test.

Abbreviations: HDL, High density lipoprotein; LDL, Low density lipoprotein.

Table 6 shows the correlation between variables using the Spearman rank test. There was a statistically significant correlation between duration and type of transportation with total cholesterol and LDL levels. The correlation coefficients for both travel duration and transportation type were negative, which means that the longer the trip, the lower the total cholesterol and LDL levels. However, the strength of the correlation between these variables was low (0.20-0.39).

Table 6. Correlation between total cholesterol and LDL with travel duration and transportation type.

Parameter	Travel duration		Type of transportation
Parameter	P value	Correlation coefficient(r)	P-value	Correlation coefficient (r)
Fasting glucose	0.399	−0.1016	0.9562	0.0066
Total cholesterol*	0.0118*	−0.2975	0.0076*	−0.3142
HDL	0.8141	0.0284	0.3888	0.1038
LDL*	0.0235*	−0.2687	0.0013*	−0.3744
Triglyceride	0.7995	−0.307	0.1797	0.1611

* Statistically significant difference between groups, Spearman rank correlation test.

Abbreviations: HDL, High density lipoprotein; LDL, Low density lipoprotein.

The adequacy of the sample size for each metabolic parameter between travel duration and type of transportation was analyzed based on their correlation coefficient properties. The number of samples in the fasting glucose and HDL groups was inadequate for both travel duration and type of transportation classification (Table 7), as was the triglyceride sample for the type of transportation.

Table 7. Power analysis by correlation coefficient and sample size.

Parameter	Travel duration	Type of transportation
Parameter	Power	Power
Fasting glucose	0.13	0.05
Total cholesterol	0.72	0.75
HDL	0.05	0.13
LDL	0.72	0.89
Triglyceride	0.75	0.26

Discussion

According to Jovanović et al. (2008), the driver's serum glucose, total cholesterol, LDL cholesterol, and triacylglycerol concentrations increase according to the work stress index.¹⁴ However, research identifying the metabolic responses to driving stress, primarily due to traffic congestion, is limited. The current study showed a correlation between travel duration and type of transportation with total cholesterol and LDL levels. Pedestrians showed the highest total cholesterol and LDL compared with other groups. In travel duration, the shortest travel duration was associated with the highest total cholesterol and LDL cholesterol levels. In the group with the shortest travel duration, >50% were pedestrians. The reason for this finding might be that during physical activity, substrate reserves are released to accommodate energetic needs. The primary sources of energy substrates are glucose and fatty acids. When needed, fatty acids can be obtained from both nutrients and adipose tissue reserves (triglycerides) by lipolysis. Triglycerides, an LDL content, are distributed to body tissues, including the skeletal muscle.¹⁵ Although we found no difference in triglycerides, it may be because the triglycerides in serum were found in the LDL fraction (LDLs consist of cholesterol, cholesterol esters, triglycerides, and phospholipids).

We also found that >50% of participants were pre-diabetic with a fasting glucose level >10% in the classification of diabetes. In addition, many risk factors influence DM-2. One of these is a modifiable behavioral factor. Therefore, it is essential to find pre-diabetes at a young age, because at this age, the average insulin sensitivity is still good; therefore, behavioral factors can be modified as soon as possible, such as diet, physical activity, and stress, to reduce the risk of DM-2.¹⁶

Traffic congestion can cause stress. We suspected that external stress stimuli cause increasing metabolic stress at the cellular level and activate the hypothalamic-pituitary-adrenocortical (HPA) axis, which subsequently causes insulin resistance.⁶ Several studies have suggested that cortisol, as a product of the HPA axis, plays a role in the processes associated with DM-2.³^,¹⁷ Nevertheless, this study showed no significant differences in fasting glucose levels associated with vehicle type and travel duration.

The limitations of this study are that we only focused on the metabolic responses of lipids and glucose. Furthermore, traffic congestion is a physiological stressor with different effects among people; some people may feel stress when they face this condition, while others may not because they face traffic congestion every day for instance. In addition, subjects in this study were not randomly pooled from the general population hence the results of this study only applicable to this group only.

Conclusions

Metabolic responses such as total cholesterol and LDL levels were affected by traffic congestion depending on the type of transportation and travel duration. Our study gives another perspective that traffic condition could affect metabolic dynamic changes. Further study in older respondents and or longer traffic jam condition can be potential to see the possibility of traffic jam affect metabolic changes. Moreover, exploration of chronic stress pathway and other metabolic markers associated with stress, such as cortisol and catecholamine, is necessary to see the correlation between chronic stress and metabolic profile.

Data availability

Underlying data

Figshare: Data Measurement of The Metabolic Response Against Stress Induced Traffic Congestion. https://doi.org/10.6084/m9.figshare.16529886.¹⁸

Extended data

Figshare: Questionnaire Correlation of Driving Stress with Fasting Blood Sugar Level and Lipid Profile. https://doi.org/10.6084/m9.figshare.16959637.v1.¹⁹

Figshare: Information sheet and consent form of Correlation of Driving Stress with Fasting Blood Sugar Level and Lipid Profile. https://doi.org/10.6084/m9.figshare.16959649.v1.²⁰

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).

Acknowledgments

We thank the staff of laboratory of Bandung Medical Center for excellent technical assistance, and the students of the UNISBA Faculty of Medicine who were willing to participate in this study.

References

1. Bitkina OV, Kim J, Park J, et al.: Identifying Traffic Context Using Driving Stress: A Longitudinal Preliminary Case Study. Sensors (Basel, Switzerland). 2019; 19. PubMed Abstract | Publisher Full Text | Free Full Text
2. Bandung BPSK: Kota Bandung Dalam Angka. Bandung: 2015.
3. Do Yup Lee EK, Choi MH: Technical and clinical aspects of cortisol as a biochemical marker of chronic stress. BMB Reports. 2015; 48: 209–216. Publisher Full Text
4. Chrousos GP: Stress and disorders of the stress system. Nature Reviews Endocrinology. 2009; 5: 374–381. PubMed Abstract | Publisher Full Text
5. Nicolaides NC, Charmandari E, Chrousos GP: The hypothalamic-pituitary-adrenal axis in human health and disease. Introduction to translational cardiovascular research. Springer; 2015; p. 91–107. Publisher Full Text
6. Diz-Chaves Y, Gil-Lozano M, Toba L, et al.: Stressing diabetes? The hidden links between insulinotropic peptides and the HPA axis. Journal of Endocrinology. 2016; 230: R77–R94. Publisher Full Text
7. Tsigos C, Stress KI: Endocrine Physiology and Pathophysiology - Endotext - NCBI Bookshelf.2019.
8. Shaw JE, Sicree RA, Zimmet PZ: Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes research and clinical practice. 2010; 87: 4–14. PubMed Abstract | Publisher Full Text
9. Mihardja L, Soetrisno U, Soegondo S: Prevalence and clinical profile of diabetes mellitus in productive aged urban Indonesians. J. Diabe. Invest. 2014; 5: 507–512. PubMed Abstract | Publisher Full Text | Free Full Text
10. Von Elm E, Altman DG, Egger M, et al.: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Epidemiology (Cambridge, Mass). 2007; 18: 800–804. PubMed Abstract | Publisher Full Text
11. Heyward VH, Wagner DR: Applied body composition assessment. Human Kinetics. 2004.
12. Salkind NJ: Encyclopedia of research design. sage; 2010.
13. Kremelberg D; Pearson’sr, chi-square, t-test, and ANOVA: Practical statistics: A quick and easy guide to IBM^® SPSS^® statistics, STATA, and other statistical software. 2011; 119–204.
14. Jovanović J, Stefanović V, Stanković DN, et al.: Serum lipids and glucose disturbances at professional drivers exposed to occupational stressors. Central European Journal of Public Health. 2008; 16: 54–58. PubMed Abstract | Publisher Full Text
15. Rodwell VW, Bender DA, Botham KM, et al.: Harper's illustrated biochemistry. New York (NY): McGraw-Hill Education; 2018.
16. Mahan LK, Escott-Stump S, Krause MV, et al.: Krause's Food & Nutrition Therapy. Saunders/Elsevier; 2008.
17. Hackett RA, Kivimäki M, Kumari M, et al.: Diurnal cortisol patterns, future diabetes, and impaired glucose metabolism in the Whitehall II cohort study. The Journal of Clinical Endocrinology & Metabolism. 2016; 101: 619–625. PubMed Abstract | Publisher Full Text | Free Full Text
18. Putri M, Rathomi HS, Yulianto FA, et al.: Data Measurement of The Metabolic Response Against Stress Induced Traffic Congestion. figshare. Dataset. 2021. Publisher Full Text
19. Putri M, Rathomi HS, Yulianto FA, et al.: Questionnaire Correlation of Driving Stress with Fasting Blood Sugar Level and Lipid Profile. figshare. Dataset. 2021. Publisher Full Text
20. Putri M, Yulianto FA, Rathomi HS, et al.: Information Sheet and Consent Form of Correlation of Driving Stress with Fasting Blood Sugar Level and Lipid Profile. figshare. Dataset. 2021. Publisher Full Text

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Author details Author details

¹ Department of Biochemistry, Nutrition, and Biomolecular, Faculty of Medicine, Universitas Islam Bandung, Bandung, West Java, 40116, Indonesia
² Department of Public Health, Faculty of Medicine, Universitas Islam Bandung, Bandung, West Java, 40116, Indonesia
³ Faculty of Medicine, Universitas Islam Bandung, Bandung, West JAva, 40116, Indonesia
⁴ Department of Medical education, humanities and bioethics, Faculty of Medicine, Universitas Islam Bandung, Bandung, West Java, 40116, Indonesia
⁵ Department of Surgery, Faculty of Medicine, Universitas Islam Bandung, Bandung, West Java, 40116, Indonesia

Mirasari Putri
Roles: Conceptualization, Data Curation, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Hilmi Sulaiman Rathomi
Roles: Formal Analysis, Methodology, Software, Writing – Original Draft Preparation

Fajar Awalia Yulianto
Roles: Formal Analysis, Methodology, Writing – Original Draft Preparation

Raden Aliya T. M. Djajanagara
Roles: Project Administration, Visualization, Writing – Original Draft Preparation

Dony Septriana Rosady
Roles: Data Curation, Investigation, Project Administration

Sadiah Achmad
Roles: Writing – Review & Editing

M. Ahmad Djojosugito
Roles: Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

This work was supported by a grant from the Faculty of Medicine Universitas Islam Bandung (UNISBA) 2017-2018 to Sadiah Achmad and the team.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Published: 23 Nov 2021, 10:1179

https://doi.org/10.12688/f1000research.73795.1

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© 2021 Putri M et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Putri M, Rathomi HS, Yulianto FA et al. Metabolic response against traffic congestion-induced stress [version 1; peer review: 1 approved with reservations, 1 not approved]. F1000Research 2021, 10:1179 (https://doi.org/10.12688/f1000research.73795.1)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

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Reviewer Report 23 Apr 2024

Santhosh Kareepadath Rajan, CHRIST (Deemed to be University), Bangalore, Karnataka, India

Approved with Reservations

https://doi.org/10.5256/f1000research.77470.r261137

This manuscript presents an interesting investigation into the metabolic response and congestion-induced stress. Overall, the research question is relevant, and the chosen methodology seems appropriate. However, there are a few areas where further clarification and improvement could strengthen the ... Continue reading

This manuscript presents an interesting investigation into the metabolic response and congestion-induced stress. Overall, the research question is relevant, and the chosen methodology seems appropriate. However, there are a few areas where further clarification and improvement could strengthen the overall quality of the work.
By addressing the following points, the authors can ensure their findings have a more robust foundation and contribute significantly to the field.

Some of the studies that have been used as evidence to defend the findings (in the discussion) have not been updated in the background. For instance, study number 14 by Jovanović J, Stefanović V, Stanković DN, et al. has not been updated in the background. This reduces the link between the background of the study and the discussion.
The rationale has been well placed.

Method

In the method, study design and setting, last statement “valid variable measurements were explained” is right.
In statistical analysis: “If the data observed had a normal distribution, with p < .05,” misguides as the probability is for the test of normality. I hope it is for the correlation.

Results

Why were the authors unable to transform the “data of duration of travel” to normal?
“…we converted the variable into categorical with the following categories: 1. <15, 2. 15 to 30, and 3. > 30.” What was the logic in transforming the data into three categories? Did the author/s follow a standardized process to categorize?
One-way ANOVA is not a bivariate analysis. It is comparative. Why did the authors refrain from saying that they did one-way ANOVA?
ANOVA tells about the between-group differences. Why did the authors interpret it in terms of “relationship” instead of differences?
The p-value definitely tells about the significance. An asterisk can represent it. But F is the real coefficient that represents ANOVA. Why did the authors not show the coefficient (F) in the explanation or the table?
Authors have indicated that they have done the Spearman rank test. The coefficient for the Spearman rank test is not ‘r.’ It is ‘ρ’.
Authors have already tested the difference in HDL, LDL, and triglycerides concerning the type of transportation and travel duration using ANOVA. Looks like redundant testing the correlation between them again.
“However, the strength of the correlation between these variables was low.” In my opinion, if the correlation is significant, even if it is low, it indicates the relationship. A low correlation is not a “bad” correlation!
Is Table 7 and its interpretation given to indicate the limitation of the study? After reading Table 7 and its interpretation, I doubt whether the reports from the study are reliable.

Discussion

Is it because of the lack of sufficient samples that the cholesterol levels are lower in non-pedestrians?
Authors have discussed that >50% of participants were pre-diabetic with a fasting glucose level >10%. Did the authors inform the participants (ethical concerns)?
I feel that the discussion is half-baked. Request the authors to enhance the rigor of the discussion.
Limitations are incomplete.
There is scope to improve the clarity of the conclusion.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Affective experiences, Behaviour decisions, Resilience, Solution-Focused approaches, and Research Analytics.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Reviewer Report 29 Nov 2023

Ivana Šarac, Centre of Research Excellence in Nutrition and Metabolism, Institute for Medical Research, National Institute of Republic of Serbia,, University of Belgrade, Belgrade, Serbia

Not Approved

https://doi.org/10.5256/f1000research.77470.r144653

Title:

Traffic congestion was not assessed in this study, nor level of stress, only the mode of transportation to university campus among students. So, we do not know if there was at all traffic congestion or traffic ... Continue reading

Title:

Traffic congestion was not assessed in this study, nor level of stress, only the mode of transportation to university campus among students. So, we do not know if there was at all traffic congestion or traffic jam, which is more related to vehicles than to pedestrians, or if there was a stress (no scale which measured the level of stress).

Therefore, this study assessed only the mode of transportation to university campus and related it metabolic health parameters, without considering other important confounders.

Additionally, we cannot conclude that traffic jam influenced metabolic health according to data from this study, there was no causality examination.
The study title should be: “Mode of transportation to university campus and its relation to metabolic health of medical students in Universitas Islam Bandung, Indonesia.”

Abstract:

Similarly to said above, it is incorrect to tell that “This study aimed to examine the metabolic responses induced by traffic congestion in young adults.” It should be: “This study aimed to examine the mode of transportation to university campus and to relate it to metabolic health of students in Indonesia.”

It is also incorrect to say: “We used bioimpedance analysis to measure fasting blood glucose, lipid profiles, and nutrient body status.” Bioimpedance only assesses body composition, not biochemical profiles. Therefore, the sentence should be “We to measured fasting blood glucose, lipid profiles, and body composition by bioimpedance analysis.”

It is also incorrect to conclude: "Based on the above results, metabolic responses to traffic congestion such as changes to total cholesterol and LDL levels depended the type of transportation and travel duration”, since there was no examination of traffic congestion (just mode and duration of travelling), and we cannot propose the causality (only association we estimated), so the conclusion should be “ Based on the above results, total cholesterol and LDL levels were associated with the type of transportation and travel duration.”

However, there are also numerous methodological issues.

All statistical analyses should be adjusted for BMI index, as well as other confounders, e.g., age, gender. Gender strongly influence HDL levels, visceral fat, skeletal muscle ratio. Also smoking should be considered as confounding, but smokers were excluded here, weren't they?

BMI is one of the most important determinants of glucose and lipid levels, as well the % of fat and visceral fat.

This is the most important weakness of this study, and needs to be corrected.

The authors should make linear regression models, with glucose and lipid levels as dependent variables, and BMI, fat%, visceral fat, age, gender, smoking (in case smokers were not excluded), duration of transportation (in minutes) and each specific mode of transportation (separate, not as a scale) as predictors. So, make the separate variables for each transportation mode (coded 0 and 1). Variance inflation factors (VIF) should be tested in regression models, and models should not have variables with VIF higher than 5 (in case you have for some anthropometric measures such BMI, Fat% and visceral fat, exclude one of them, until VIF is lower of 5 for each variable in the model, while keep the ones who are the most correlated with glucose and lipid levels). In case you are using the stepwise regression, the program will automatically choose the best predictors in the model.

There was also lack of information on % of students who are underweight, normal weight and overweight (and obese. Why obese were excluded? Were they really excluded?).

The authors treated the mode of transportation as a scale, which is not correct. You can treat duration of transportation as a scale, but not mode of transportation, since the mode of transportation does not have increasing categories. So, the duration of transportation can be used for Spearman’s correlation in table 6, but the type of transportation can be only used for Point-biserial correlation in table 6 (see below).
In table 6, the authors should better give correlation coefficients between BMI, fat percentages, visceral fat, skeletal muscle ratio, glucose level and lipids levels with all modes of transportation (separately!) and duration of travelling (in minutes!). Point biserial correlation can be used for each mode of transportation. Pearson or Spearman correlation should be used for other variables.

Even worse, in the table 4 they combined all the modes of transportation and all categories of duration into only one scale, and represented only one P value for Fisher exact test for high and very high body fat percentages, which is absurd and meaningless.

Table 3 does not include any statistical analysis, and does not give any observable information. It is better to give the figures the levels of glucose and lipids (in mmol/l) for each separate category.

One way ANOVA in table 5 should have post hoc analyses between the groups (not only in the text). In table 5, ANOVA should be also given for BMI, fat%, visceral fat and skeletal muscle ratio.

Table 2 contains duplication of the rows. Data should be merged (median / range should go to rows with N and %). Also, Table 2 contains some absurd information on “borderline”, “almost optimal” lipid levels. The authors should present in methods the references for standardized cut-off values for glucose and lipids in the studied population (please, pay attention for gender differences).

Table 7 is completely unnecessary, the same for table 3.

Also, here are some points in separate sections:

Introduction:

Again, it is also incorrect to say: “In this study, we investigated whether stress whilst driving would affect metabolic responses by examining several metabolic parameters such as fasting blood glucose, lipid profiles, and nutrient body status in young adult traffic users, which were active students at UNISBA Faculty of Medicine.”, because the authors did not measure the level of stress, they only assessed the mode of transportation and duration. The authors also did not assess the “nutrient body status”, only body composition (which is different, from nutrients, e.g., level of minerals, vitamins, fatty acids, etc. in the body). What is: “UNISBA” abbreviation? Universitas Islam Bandung? Please define.

Methods:

Why a personal history of obesity, dyslipidaemia and diabetes were used as exclusion criteria? Were they, really? I can see that some of the students had dyslipidaemia and diabetes, according to table 2.

References for cut-off values for glucose, lipid levels, and anthropometric indices are missing.

Statistics:

Linear regression models should be performed and adequate correlation analyses (not ANOVA or Fisher tests), please see above.

Results:

I have already had given some my comments above, however the sentence “In addition, the participants' average fasting glucose and lipid profiles were normal, but 63.4% of participants had pre-diabetic fasting glucose levels and 12.7% at a diabetic level (Table 2).” is absurd. A fasting blood sugar level of 99 mg/dL or lower is normal, 100 to 125 mg/dL indicates you have prediabetes, and 126 mg/dL or higher indicates you have diabetes.

It was completely unclear what the authors meant by.” The adequacy of the sample size for each metabolic parameter between travel duration and type of transportation was analyzed based on their correlation coefficient properties. The number of samples in the fasting glucose and HDL groups was inadequate for both travel duration and type of transportation classification (Table 7), as was the triglyceride sample for the type of transportation.”

Discussion:

Is absurd if statistical analyses were not performed correctly. Adjustments for confounders must have been performed. Again, authors discuss traffic congestion, while nowhere the level of traffic congestion was examined in the study.

Conclusions:

Not related to the presented data and results. No stress scale is used. No traffic congestion levels were measured.

Is the work clearly and accurately presented and does it cite the current literature?

No
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

No
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

No

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Obesity, metabolic syndrome, metabolic response to stress.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Version 1 23 Nov 21	read	read

Ivana Šarac, University of Belgrade, Belgrade, Serbia
Santhosh Kareepadath Rajan, CHRIST (Deemed to be University), Bangalore, Karnataka, India

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Reviewer Report

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23 Apr 2024 | for Version 1

Santhosh Kareepadath Rajan, CHRIST (Deemed to be University), Bangalore, Karnataka, India

4 Views Cite this report Responses(0)

Approved With Reservations

This manuscript presents an interesting investigation into the metabolic response and congestion-induced stress. Overall, the research question is relevant, and the chosen methodology seems appropriate. However, there are a few areas where further clarification and improvement could strengthen the overall quality of the work.
By addressing the following points, the authors can ensure their findings have a more robust foundation and contribute significantly to the field.

Some of the studies that have been used as evidence to defend the findings (in the discussion) have not been updated in the background. For instance, study number 14 by Jovanović J, Stefanović V, Stanković DN, et al. has not been updated in the background. This reduces the link between the background of the study and the discussion.
The rationale has been well placed.

Method

In the method, study design and setting, last statement “valid variable measurements were explained” is right.
In statistical analysis: “If the data observed had a normal distribution, with p < .05,” misguides as the probability is for the test of normality. I hope it is for the correlation.

Results

Why were the authors unable to transform the “data of duration of travel” to normal?
“…we converted the variable into categorical with the following categories: 1. <15, 2. 15 to 30, and 3. > 30.” What was the logic in transforming the data into three categories? Did the author/s follow a standardized process to categorize?
One-way ANOVA is not a bivariate analysis. It is comparative. Why did the authors refrain from saying that they did one-way ANOVA?
ANOVA tells about the between-group differences. Why did the authors interpret it in terms of “relationship” instead of differences?
The p-value definitely tells about the significance. An asterisk can represent it. But F is the real coefficient that represents ANOVA. Why did the authors not show the coefficient (F) in the explanation or the table?
Authors have indicated that they have done the Spearman rank test. The coefficient for the Spearman rank test is not ‘r.’ It is ‘ρ’.
Authors have already tested the difference in HDL, LDL, and triglycerides concerning the type of transportation and travel duration using ANOVA. Looks like redundant testing the correlation between them again.
“However, the strength of the correlation between these variables was low.” In my opinion, if the correlation is significant, even if it is low, it indicates the relationship. A low correlation is not a “bad” correlation!
Is Table 7 and its interpretation given to indicate the limitation of the study? After reading Table 7 and its interpretation, I doubt whether the reports from the study are reliable.

Discussion

Is it because of the lack of sufficient samples that the cholesterol levels are lower in non-pedestrians?
Authors have discussed that >50% of participants were pre-diabetic with a fasting glucose level >10%. Did the authors inform the participants (ethical concerns)?
I feel that the discussion is half-baked. Request the authors to enhance the rigor of the discussion.
Limitations are incomplete.
There is scope to improve the clarity of the conclusion.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Affective experiences, Behaviour decisions, Resilience, Solution-Focused approaches, and Research Analytics.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

Responses (0)

Back to all reports

Reviewer Report

4 Views

29 Nov 2023 | for Version 1

Ivana Šarac, Centre of Research Excellence in Nutrition and Metabolism, Institute for Medical Research, National Institute of Republic of Serbia,, University of Belgrade, Belgrade, Serbia

4 Views Cite this report Responses(0)

Not Approved

Title:

Traffic congestion was not assessed in this study, nor level of stress, only the mode of transportation to university campus among students. So, we do not know if there was at all traffic congestion or traffic jam, which is more related to vehicles than to pedestrians, or if there was a stress (no scale which measured the level of stress).

Therefore, this study assessed only the mode of transportation to university campus and related it metabolic health parameters, without considering other important confounders.

Additionally, we cannot conclude that traffic jam influenced metabolic health according to data from this study, there was no causality examination.
The study title should be: “Mode of transportation to university campus and its relation to metabolic health of medical students in Universitas Islam Bandung, Indonesia.”

Abstract:

Similarly to said above, it is incorrect to tell that “This study aimed to examine the metabolic responses induced by traffic congestion in young adults.” It should be: “This study aimed to examine the mode of transportation to university campus and to relate it to metabolic health of students in Indonesia.”

It is also incorrect to say: “We used bioimpedance analysis to measure fasting blood glucose, lipid profiles, and nutrient body status.” Bioimpedance only assesses body composition, not biochemical profiles. Therefore, the sentence should be “We to measured fasting blood glucose, lipid profiles, and body composition by bioimpedance analysis.”

It is also incorrect to conclude: "Based on the above results, metabolic responses to traffic congestion such as changes to total cholesterol and LDL levels depended the type of transportation and travel duration”, since there was no examination of traffic congestion (just mode and duration of travelling), and we cannot propose the causality (only association we estimated), so the conclusion should be “ Based on the above results, total cholesterol and LDL levels were associated with the type of transportation and travel duration.”

However, there are also numerous methodological issues.

All statistical analyses should be adjusted for BMI index, as well as other confounders, e.g., age, gender. Gender strongly influence HDL levels, visceral fat, skeletal muscle ratio. Also smoking should be considered as confounding, but smokers were excluded here, weren't they?

BMI is one of the most important determinants of glucose and lipid levels, as well the % of fat and visceral fat.

This is the most important weakness of this study, and needs to be corrected.

The authors should make linear regression models, with glucose and lipid levels as dependent variables, and BMI, fat%, visceral fat, age, gender, smoking (in case smokers were not excluded), duration of transportation (in minutes) and each specific mode of transportation (separate, not as a scale) as predictors. So, make the separate variables for each transportation mode (coded 0 and 1). Variance inflation factors (VIF) should be tested in regression models, and models should not have variables with VIF higher than 5 (in case you have for some anthropometric measures such BMI, Fat% and visceral fat, exclude one of them, until VIF is lower of 5 for each variable in the model, while keep the ones who are the most correlated with glucose and lipid levels). In case you are using the stepwise regression, the program will automatically choose the best predictors in the model.

There was also lack of information on % of students who are underweight, normal weight and overweight (and obese. Why obese were excluded? Were they really excluded?).

The authors treated the mode of transportation as a scale, which is not correct. You can treat duration of transportation as a scale, but not mode of transportation, since the mode of transportation does not have increasing categories. So, the duration of transportation can be used for Spearman’s correlation in table 6, but the type of transportation can be only used for Point-biserial correlation in table 6 (see below).
In table 6, the authors should better give correlation coefficients between BMI, fat percentages, visceral fat, skeletal muscle ratio, glucose level and lipids levels with all modes of transportation (separately!) and duration of travelling (in minutes!). Point biserial correlation can be used for each mode of transportation. Pearson or Spearman correlation should be used for other variables.

Even worse, in the table 4 they combined all the modes of transportation and all categories of duration into only one scale, and represented only one P value for Fisher exact test for high and very high body fat percentages, which is absurd and meaningless.

Table 3 does not include any statistical analysis, and does not give any observable information. It is better to give the figures the levels of glucose and lipids (in mmol/l) for each separate category.

One way ANOVA in table 5 should have post hoc analyses between the groups (not only in the text). In table 5, ANOVA should be also given for BMI, fat%, visceral fat and skeletal muscle ratio.

Table 2 contains duplication of the rows. Data should be merged (median / range should go to rows with N and %). Also, Table 2 contains some absurd information on “borderline”, “almost optimal” lipid levels. The authors should present in methods the references for standardized cut-off values for glucose and lipids in the studied population (please, pay attention for gender differences).

Table 7 is completely unnecessary, the same for table 3.

Also, here are some points in separate sections:

Introduction:

Again, it is also incorrect to say: “In this study, we investigated whether stress whilst driving would affect metabolic responses by examining several metabolic parameters such as fasting blood glucose, lipid profiles, and nutrient body status in young adult traffic users, which were active students at UNISBA Faculty of Medicine.”, because the authors did not measure the level of stress, they only assessed the mode of transportation and duration. The authors also did not assess the “nutrient body status”, only body composition (which is different, from nutrients, e.g., level of minerals, vitamins, fatty acids, etc. in the body). What is: “UNISBA” abbreviation? Universitas Islam Bandung? Please define.

Methods:

Why a personal history of obesity, dyslipidaemia and diabetes were used as exclusion criteria? Were they, really? I can see that some of the students had dyslipidaemia and diabetes, according to table 2.

References for cut-off values for glucose, lipid levels, and anthropometric indices are missing.

Statistics:

Linear regression models should be performed and adequate correlation analyses (not ANOVA or Fisher tests), please see above.

Results:

I have already had given some my comments above, however the sentence “In addition, the participants' average fasting glucose and lipid profiles were normal, but 63.4% of participants had pre-diabetic fasting glucose levels and 12.7% at a diabetic level (Table 2).” is absurd. A fasting blood sugar level of 99 mg/dL or lower is normal, 100 to 125 mg/dL indicates you have prediabetes, and 126 mg/dL or higher indicates you have diabetes.

It was completely unclear what the authors meant by.” The adequacy of the sample size for each metabolic parameter between travel duration and type of transportation was analyzed based on their correlation coefficient properties. The number of samples in the fasting glucose and HDL groups was inadequate for both travel duration and type of transportation classification (Table 7), as was the triglyceride sample for the type of transportation.”

Discussion:

Is absurd if statistical analyses were not performed correctly. Adjustments for confounders must have been performed. Again, authors discuss traffic congestion, while nowhere the level of traffic congestion was examined in the study.

Conclusions:

Not related to the presented data and results. No stress scale is used. No traffic congestion levels were measured.

Is the work clearly and accurately presented and does it cite the current literature?

No
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

No
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

No

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Obesity, metabolic syndrome, metabolic response to stress.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

Respond to this report

Responses (0)

[1] 1. Bitkina OV, Kim J, Park J, et al.: Identifying Traffic Context Using Driving Stress: A Longitudinal Preliminary Case Study. Sensors (Basel, Switzerland). 2019; 19. PubMed Abstract | Publisher Full Text | Free Full Text

[2] 2. Bandung BPSK: Kota Bandung Dalam Angka. Bandung: 2015.

[3] 3. Do Yup Lee EK, Choi MH: Technical and clinical aspects of cortisol as a biochemical marker of chronic stress. BMB Reports. 2015; 48: 209–216. Publisher Full Text

[4] 4. Chrousos GP: Stress and disorders of the stress system. Nature Reviews Endocrinology. 2009; 5: 374–381. PubMed Abstract | Publisher Full Text

[5] 5. Nicolaides NC, Charmandari E, Chrousos GP: The hypothalamic-pituitary-adrenal axis in human health and disease. Introduction to translational cardiovascular research. Springer; 2015; p. 91–107. Publisher Full Text

[6] 6. Diz-Chaves Y, Gil-Lozano M, Toba L, et al.: Stressing diabetes? The hidden links between insulinotropic peptides and the HPA axis. Journal of Endocrinology. 2016; 230: R77–R94. Publisher Full Text

[7] 7. Tsigos C, Stress KI: Endocrine Physiology and Pathophysiology - Endotext - NCBI Bookshelf.2019.

[8] 8. Shaw JE, Sicree RA, Zimmet PZ: Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes research and clinical practice. 2010; 87: 4–14. PubMed Abstract | Publisher Full Text

[9] 9. Mihardja L, Soetrisno U, Soegondo S: Prevalence and clinical profile of diabetes mellitus in productive aged urban Indonesians. J. Diabe. Invest. 2014; 5: 507–512. PubMed Abstract | Publisher Full Text | Free Full Text

[10] 10. Von Elm E, Altman DG, Egger M, et al.: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Epidemiology (Cambridge, Mass). 2007; 18: 800–804. PubMed Abstract | Publisher Full Text

[11] 11. Heyward VH, Wagner DR: Applied body composition assessment. Human Kinetics. 2004.

[12] 12. Salkind NJ: Encyclopedia of research design. sage; 2010.

[13] 13. Kremelberg D; Pearson’sr, chi-square, t-test, and ANOVA: Practical statistics: A quick and easy guide to IBM^® SPSS^® statistics, STATA, and other statistical software. 2011; 119–204.

[14] 14. Jovanović J, Stefanović V, Stanković DN, et al.: Serum lipids and glucose disturbances at professional drivers exposed to occupational stressors. Central European Journal of Public Health. 2008; 16: 54–58. PubMed Abstract | Publisher Full Text

[15] 15. Rodwell VW, Bender DA, Botham KM, et al.: Harper's illustrated biochemistry. New York (NY): McGraw-Hill Education; 2018.

[16] 16. Mahan LK, Escott-Stump S, Krause MV, et al.: Krause's Food & Nutrition Therapy. Saunders/Elsevier; 2008.

[17] 17. Hackett RA, Kivimäki M, Kumari M, et al.: Diurnal cortisol patterns, future diabetes, and impaired glucose metabolism in the Whitehall II cohort study. The Journal of Clinical Endocrinology & Metabolism. 2016; 101: 619–625. PubMed Abstract | Publisher Full Text | Free Full Text

[18] 18. Putri M, Rathomi HS, Yulianto FA, et al.: Data Measurement of The Metabolic Response Against Stress Induced Traffic Congestion. figshare. Dataset. 2021. Publisher Full Text

[19] 19. Putri M, Rathomi HS, Yulianto FA, et al.: Questionnaire Correlation of Driving Stress with Fasting Blood Sugar Level and Lipid Profile. figshare. Dataset. 2021. Publisher Full Text

[20] 20. Putri M, Yulianto FA, Rathomi HS, et al.: Information Sheet and Consent Form of Correlation of Driving Stress with Fasting Blood Sugar Level and Lipid Profile. figshare. Dataset. 2021. Publisher Full Text

Metabolic response against traffic congestion-induced stress

Abstract

Keywords

Introduction

Methods

Ethical considerations

Study design and setting

Participants

Measurement of travel duration

Blood sampling

Measurement of body composition

Measurement of blood parameters

Statistical analysis

Results

Participant characteristics

Table 1. Participant characteristics.

Table 2. Obtained parameters.

Table 3. Body fat, visceral fat, and skeletal muscle ratio.

Table 4. Differences in high and very high body fat percentages.

Table 5. Relationship between travel duration and transportation type with fasting glucose, total cholesterol, HDL, LDL, and triglyceride levels.

Table 6. Correlation between total cholesterol and LDL with travel duration and transportation type.

Table 7. Power analysis by correlation coefficient and sample size.

Discussion

Conclusions

Data availability

Underlying data

Extended data

Acknowledgments

References

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