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INTRODUCTION
Coronavirus disease-2019 (COVID-19) is the infectious disease caused by the coronavirus SARS-CoV-2. The first case of COVID-19 was reported to the World health Organization (WHO) by Chinese authorities on December 31th, 2019. COVID-19 produces a respiratory infection characterized by mild to severe symptoms. According to the National Center for Immunization and Respiratory Diseases, the high-risk categories for severe illness from COVID-19 include people aged 65 years or older, immunocompromised individuals, and people with chronic diseases such as serious heart conditions, diabetes mellitus, chronic lung diseases, chronic liver or kidney diseases, and obesity (1).
Of this pandemic, one of the multiple implications is the abrupt cessation of outside activities for the population in Spain, who by mandate had to remain in their homes during a “lockdown” aimed at containing and mitigating COVID-19 spread. There are reasons to worry about housebound patients who have obesity: some previous studies have shown that younger people had worse weight control while at home when compared to when allowed their usual activities (3). These observations (1,3) and the well-known relationship between sedentary habits and obesity (4) advance the argument that the COVID-19 pandemic, by restricting population mobility, will exacerbate the risk factors for weight gain associated with physical inactivity. This decrease in activity plus an increased consumption of calorie-dense foods (snacking) and ultra-processed food, a second risk factor strongly supported by observations in real-world settings (5), allow to hypothesize a weight gain during these weeks of confinement that will likely have metabolic repercussions in the near future, and then an increased risk of cardiovascular events.
The aim of this cross-sectional study was to test the hypothesis that risk factors related to body weight gain among obese subjects are exacerbated during a pandemic-associated lockdown, and a significant increase in body weight may be detected.
MATERIALS AND METHODS
SUBJECTS AND CLINICAL INVESTIGATION
The population studied was selected from obese patients seen during the past year in our Department, including those referred by other physicians in our Health Area. Obesity is defined by a body mass index (BMI) ≥ 30 kg/m2. We retrieved the relevant anthropometric and biochemical information from the electronic medical records during the last visit to our clinic, and a telephone survey was conducted during the week of May 4-8, 2020. A total of 284 obese Caucasian subjects were enrolled using a non-probabilistic, consecutive sampling approach. The obese subjects recruited fulfilled the following inclusion criteria: BMI ≥ 30 kg/m2; no a history of cardiovascular disease, thyroid disease, renal or hepatic disorders; no history of alcoholism or malignancies. Exclusion criteria included age under 18 years or above 65 years, BMI over 45 kg/m2; and COVID-19 disease during the study.
The Ethics Committee (HCUVA Committee) approved the study, which was in accordance with the guidelines laid down in the Declaration of Helsinki (PIP-201766). All participants provided a written informed consent. For all subjects, we recovered from the electronic medical records the following data as of the last face-to-face visit: systolic and diastolic blood pressure, and anthropometric parameters (weight, height, body mass index (BMI), and waist circumference). The following biochemical data were recovered from the medical records: glucose, insulin, total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglyceride levels.
After a 7-week confinement period from March 17, a telephone interview (May 4 to 7) was conducted with the following questions: 1) Have you performed regular physical exercise? (yes/no); 2) How many minutes per week of exercise? (numerical); 3) What is your current weight? (numerical); 4) According to your perception, have you gained weight? (yes/no); 5) If the answer was “yes”, how much weight have you gained? (numerical); 6) Did you take snacks between meals during the lockdown? (numerical); 7) How many meals do you eat per day since confinement? (numerical); 8) How many main meals did you eat before confinement? (numerical); 9) What is the surface area (m2) of your home? (numerical); 10) How many members make up your family? (numerical); 11) Do you have a pet? (yes/no); 12) If you were allowed to go outside with your pet, how many minutes per week? (numerical); 13) How many hours a day did you watch TV before confinement? (numerical); 14) How many hours a day do you watch TV now? (numerical). This telephone interview lasted about 15 minutes.
ANTHROPOMETRIC AND BIOCHEMICAL PARAMETERS FROM ELECTRONIC MEDICAL RECORDS
In order to obtain the baseline data of this population, the following parameters were collected from electronic medical records: body weight was measured using scales (Omrom, LA, CA, USA) and recorded to the nearest 50 g; height was measured with a tape measure (Omrom, LA, CA, USA); body mass index (BMI) was calculated as body weight (in kg) divided by height (in m2). Waist circumference (WC) was measured at the umbilical level. The following biochemical parameters were obtained from the medical records: serum total cholesterol and triglyceride levels were determined by enzymatic colorimetric assay (Technicon Instruments, Ltd., New York, N.Y., USA), while HDL-cholesterol was measured in the supernatant after precipitation of other lipoproteins by enzymatic methods; LDL-cholesterol was calculated using Friedewald’s formula (LDL-cholesterol = total cholesterol - HDL cholesterol - triglycerides / 5) (6); glucose levels were measured by an automated glucose oxidase method (Glucose analyser 2, Beckman Instruments, Fullerton, CA, USA); insulin was determined by radioimmunoassay (RIA) (RIA Diagnostic Corporation, Los Angeles, CA, USA) with a sensitivity of 0.5 mIU/L (normal range, 0.5-30 mIU/L) (7), and the homeostasis model assessment for insulin resistance (HOMA-IR) was calculated using these values (8).
STATISTICAL ANALYSIS
Data were analyzed using the SPSS for Windows, version 19.0, software package (SPSS Inc. Chicago, IL, USA). Sample size was calculated to detect an increase in self-reported body weight gain during confinement of 1.5 kg with 90 % power and 5 % significance. The results were expressed as average ± standard deviation. The Chi-squared test was used for the analysis of categorical parameters. Numerical variables were analyzed with Student’s t-test, the ANOVA test, or the Kruskal-Wallis test. Pearson’s test and Spearman’s test were used to correlate numerical variables. Multiple regression analyses adjusted by age and gender were used to calculated the “beta” and 95 % confidence intervals (CI) to estimate the association of self-reported body weight gain with different variables of the phone questionnaire. A p-value under 0.05 was considered statistically significant.
RESULTS
The sample was comprised of 284 Caucasian obese subjects. Mean age was 60.4 ± 10.8 years (range: 23-71), and mean body mass index (BMI) was 35.4 ± 4.7 kg/m2 (range: 30.6-41.2). Gender distribution was 211 females (74.3 %) and 73 males (25.7 %).
Table I shows the biochemical and epidemiological data of the study population in the last face-to-face visit at the Hospital. Patients were predominantly female, with a BMI within the grade-II obesity range. The following parameters show statistical differences between genders (male vs female: delta: 13.4 ± 2.1 kg; p = 0.01): height (male vs female: delta: 0.11 ± 0.08 kg; p = 0.01), and HDL-cholesterol levels (male vs female: delta: 5.9 ± 1.1 kg; p = 0.04).
Table I. Epidemiological and biochemical variables
BMI: body mass index; DBP: diastolic blood pressure; SBP: systolic blood pressure; WC: waist circumference; LDL-cholesterol: low-density lipoprotein cholesterol; HDLcholesterol: high-density lipoprotein cholesterol; HOMA-IR: homeostasis model assessment. *p < 0.05, between genders.
Table II shows the results of the answers to the telephone questionnaire. There were no statistically significant differences between the variables analyzed according to the gender of the participants. The sample analyzed showed a low physical activity rate (11.8 %). The patients who reported doing a lot of exercise had an average of physical activity, in minutes, higher than those who reported doing not enough exercise (no regular exercise vs regular exercise: 134.42 ± 22.1 vs 208.63 ± 17.9 min/week; p = 0.01). The increase in self-reported body weight was 1.58 ± 2.7 kg during the 7 weeks of confinement, and self-reported body weight gain was lower among the latter (no regular exercise vs regular exercise: 1.62 ± 0.2 vs 1.12 ± 0.3 kg; p = 0.02).
Regarding eating habits, the patients recognized snacking in 17 % of the sample. Those who reported snacking presented a higher self-reported body weight gain (no snacking vs snacking: 2.60 ± 0.36 vs 1.30 ± 0.17 kg; p = 0.001). The number of meals per day that the subjects took prior to confinement was similar to the number had during confinement. There is no difference between genders.
The correlation analysis between the self-reported gained body weight of the patients and the variables “house area in m2” (r = 0.12; p = 0.44) and “number of family members at home” (r = 0.19; p = 0.41) was not significant. The presence of pets in the home was not related to self-reported body weight gain (no pet vs pet: 1.62 ± 0.17 vs 1.12 ± 0.31 kg; p = 0.21). The minutes invested in activity outside the home with the pet did not correlate with self-reported body weight gain either (r = 0.10; p = 0.53).
Regarding self-reported TV hours per day, there was a significant increase in hours during confinement (TV hrs before vs TV hrs now: 4.3 ± 1.9 vs 5.4 ± 1.2 hrs; p = 0.01). There was no correlation between number of hours watching TV and self-reported weight gain (r = 0.23; p = 0.22).
In the multiple regression analysis with self-reported body weight gain as dependent variable and adjustment for age, sex, and physical activity, the snacking habit remained a risk factor with a beta coefficient of 1.21 (95 % CI: 1.11-2.13; p = 0.01).
DISCUSSION
The main finding of this cross-sectional study was the fact that the lockdown mandated by the Spanish Health Authorities for a period of 7 weeks produced a significant increase in self-reported body weight gain in this sample of obese patients, and that this increase was associated with the snacking habit.
The World Health Organization considers non-communicable diseases, such as obesity, a major risk factor for becoming seriously ill with the novel coronavirus (9). Recently, a study in intensive care units indicates that two thirds of the people who developed serious COVID-19-related complications were obese (10), and his study shows that almost 75 % of those in critical care units are either obese or overweight. Reports from Italy indicate that almost 90 % of deaths occurred in patients with non-communicable diseases such as obesity, diabetes mellitus type 2, hypertension, heart disease, and cancer (11). Lighter et al. (12) have demonstrated that obesity in patients younger than 60 years is a risk factor for COVID-19-related hospital admission. Taking into account everything previously commented, obesity seems to be a risk factor for an adverse outcome of COVID-19, and this propensity of subjects with obesity to develop more complications that are serious could be due to some factors such as delayed, ineffective immune response and chronic inflammatory status. As endocrinologists, we therefore have to be able to identify the risk factors that may increase obesity during the lockdown mandated by health authorities in different countries worldwide. Countries have taken various actions to flatten the curve, and to allow health care systems to cope with their demands, but these actions may produce a deleterious effect on non-communicable diseases such as obesity, which is a risk factor for having a worse course of infection.
In our study, the self-reported body weight gain has been related to variables such as minutes of exercise and snacking. Moreover, only snacking remained in the multivariate model as an independent factor. The effect of lockdown for COVID-19 on lifestyle among obese children has been demonstrated in some studies (13). In this small study of 41 Italian children with obesity (13), an increase in sleep time, a decrease in sports activities, and an increase in eating food such as fried potatoes, sugary drinks, and red meat were reported. However, the effect of lockdown and these changes in lifestyle habits on weight was not evaluated in this work. This study is interesting because it showed that lifestyle habits changed in an unfavorable direction only after 3 weeks into confinement during the Italian lockdown. Our work was carried out after 7 weeks of confinement and with a greater number of patients, in this case obese adults. The self-reported weight gain was significant, and was independently related to the snacking habit. Snacking is defined in the literature as consuming food between regular meals. And there is consensus that nutrient-poor and energy-dense snacks should be regarded as unhealthful (14), linked to increased risk of cardiovascular disease and obesity (15,16). In our study we see how snacking increases the risk of self-reported body weight gain. In the literature, many factors that influence this habit have been described, such as personal factors, both psychological (e.g., self-efficacy, emotions, knowledge) and biological (e.g., sex, age, genes). Perhaps during lockdown, psychological factors have been very important, as it has been demonstrated that individuals under psychological stress consume higher amounts of energy and dense snacks, particularly fatty and sweet snacks (17). Furthermore, the interviewed subjects reported increased TV hours during confinement, although it was not directly related to the self-reported weight gain reported in our study. Perhaps the high number of hours watching TV that subjects already reported before confinement have not allowed us to detect this influence. Moreover, in the literature, it has been demonstrated that, when distracted (e.g., by watching movies or TV), individuals often overconsume and are not necessarily cognizant of the dietary quality of the snacks eaten (18). Nowadays, COVID-19 has spread to several countries around the world and is presently a major global concern. On the other hand, the confinement of the population as a prevention measure for the spread of infection means that the mobility of obese patients is limited, and that they have a greater number of hours a day with access to snacks in their homes. We can hypothesize that, if the mobility limitation is prolonged as a preventive measure, body weight will continue to increase, as well as the alterations secondary to cardiovascular risk factors.
As regards the limitations of our study, firstly it was a cross-sectional study, so we cannot draw any causal conclusions. Secondly, the body weight of patients was self-reported, with under-estimation biases. However, there is generally a strong agreement between self-reported body weight and clinical weight (19). Thirdly, we cannot evaluate the effect on metabolic variables as we cannot perform a second blood extraction due to confinement. Fourthly, exercise, snacking, and TV hours were also self-reported, with their corresponding biases. Finally, we only evaluated Caucasian subjects, and ethnicity, differences in genetic background, and living environment would play a crucial role in our results. All of these limitations are understandable given the exigencies operating during this pandemic. In addition, the strength of this work is that, in an era of uncertainty such as the one we are living in, having data on a large sample of obese patients may allow us to design prevention strategies. Our study offers a local perspective, and is therefore helpful, although it has limitations. It is important to compare data in different healthcare systems, and to focus attention on this risk factor during all the phases of this pandemic. Lack of information regarding the increased risk of illness subjects with morbid obesity (BMI > 40 kg/m2) have might increase anxiety, given that these subjects have now been categorized as vulnerable to severe illness if they contact COVID-19, and might also give a false feeling of safety to obese people under this BMI cutoff (20).
In conclusion, the confinement decreed during the pandemic by SARS-CoV-2 has produced an increase in self-reported body weight in obese subjects, an increase that is related to the habit of taking snacks. These observations point to an important need for implementing preventive tools during periods of lockdown, particularly when their duration is uncertain. Such measures could be telemedicine lifestyle programs, and endocrinologists may offer online guidance encouraging healthy family habits as well as recommending healthy foods and exercise programs (21).
