{"id":43451,"date":"2022-03-31T11:38:51","date_gmt":"2022-03-31T11:38:51","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=43451"},"modified":"2022-04-07T07:34:20","modified_gmt":"2022-04-07T07:34:20","slug":"association-between-dietary-pattern-level-of-physical-activity-obesity-and-metabolic-syndrome-in-adolescents-a-cross-sectional-study","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol15no1\/association-between-dietary-pattern-level-of-physical-activity-obesity-and-metabolic-syndrome-in-adolescents-a-cross-sectional-study\/","title":{"rendered":"Association between Dietary Pattern, Level of Physical Activity, Obesity and Metabolic Syndrome in Adolescents: A Cross-Sectional Study"},"content":{"rendered":"

Introduction<\/strong><\/p>\n

Prevalence of Overweight and obesity have reached epidemic degree worldwide, impacting children and adolescents 1<\/sup>. According to estimations 254 million children aged 5\u201319 years will be obese by 20302<\/sup>. The health and nutrition transition in Egypt has been evident previously3<\/sup>. Obesity is linked to a number of serious health risks and comorbidities, including cardiovascular disease, hypertension, hyperlipidemia, type 2 diabetes, and metabolic disturbance4<\/sup>.<\/p>\n

The number of stunted children may be decreasing as UNICEF\/WHO\/World Bank indicate that two-thirds of nations in all regions, with the exception of Africa, are making some progress. In addition, 45.4 million children under the age of five are affected by wasting, with 13.6 million seriously impacted 5<\/sup>. Nutritional counselling is regarded as a critical intervention in clinical practice for increasing food intake and the quality of food choices in obese patients, which can enhance the regulation of metabolic syndrome parameters and, as a result, cardiovascular risks6<\/sup>. Obesity has a complex etiology. Interactions exist between genetic, neuroendocrine, metabolic, psychological, environmental, behavioral, and sociocultural factors. 7<\/sup>8<\/sup>9<\/sup>. \u00a0Treatment for obese adolescents focused mostly on encouraging a healthy lifestyle and healthy food intake. However, it is still unclear whether there are particular risk factors of metabolic health linked to lifestyle variables and dietary behavior among Egyptian adolescents.<\/p>\n

The aim of our study was to examine dietary factors and the potential risk factors for MS components and dyslipidemia in apparently healthy Egyptian adolescents.<\/p>\n

Subjects and methods<\/strong><\/p>\n

\u00a0<\/strong>The cross-sectional study was carried out on 600 subjects (250 males and 350 females), aged between 13 and 17 years, with no significant sex difference in BMI and age. Subjects were collected from March 2019 to April 2021. They were 300 obese and 300 with normal weight. They grouped according to WHO classification 10<\/sup>. Metabolic syndrome (MS) components were found in 60% of obese cases. Body fat % was carried out by body composition analyzer TANITA SC – 330. All subjects were volunteers, registering to participate in the study and informed consent was taken. BMI was calculated as weight (kg) divided by height squared (m2<\/sup>). Serum lipids (total cholesterol, high-density lipoprotein cholesterol (HDL-C) triglycerides (TG) were measured by enzymatic colorimetric. The BMI z-score represents an age- and sex-specific BMI according to the WHO reference and allows for directly comparing of BMI changes between boys and girls of different ages. This study protocol was approved by the ethical committee board of the National Research Centre of Egypt (no. 10\/223).<\/p>\n

Dietary assessment <\/strong><\/p>\n

The 24- hour dietary recall was used.\u00a0 The subjects were interviewed in person by an experienced dietician; using the multi-pass, 24-h dietary interview method. The respondent was asked to remember all the types and amounts of foods and beverages consumed during the previous 24-h period. Extra emphasis was given to fat type and fat content, added sugars and salt intakes. Due to logistical constraints a repeat dietary recall was not possible. The completed 24 h dietary recall were reviewed, checked for errors and\u00a0\u00a0 were judged reliable by the interviewer. Unreliability was defined as the inability of the respondent to recall one or more meals or if less than 500 Kcal\/day. The data were processed by data entry using the software Nutri survey computer – based database11<\/sup>.\u00a0 Moreover, dietary assessment using the 24-h dietary recall data was reported previously 12<\/sup>.\u00a0<\/strong><\/p>\n

Physical activity (PA) and sedentary behavior<\/strong><\/p>\n

PA was assessed by the validated short version of the International Physical Activity Questionnaire13<\/sup>14<\/sup>, a self-reported questionnaire based on the declaration of PA performed in the previous7 days. The results allow for estimating the frequency (number\/week)<\/p>\n

and the duration (minutes\/day) for three types of activities: vigorous, moderate and walking. Frequency, type and duration of PA were used to calculate the energy expenditure due to PA by using scoring guidelines (IPAQ scoring protocol – International Physical Activity<\/p>\n

Questionnaire, n.d.)15<\/sup>. Sedentary lifestyle is defined as a moderate practice of physical activity (PA) less than 300 min per week16<\/sup> . Therefore, light physical activity (walking) was not considered for the classification of sedentary lifestyle.\u00a0<\/strong><\/p>\n

Statistical<\/strong> analysis<\/strong>\u00a0<\/strong><\/p>\n

Continuous variables were expressed as mean \u00b1 standard deviation. To compare dietary patterns, participants were categorized into quintiles. ANOVA or chi-square testing was applied as appropriate. Logistic regression was used to obtain adjusted ORs, and to assess assoction between MS and risk factors, odds ratios (ORs) were reported along with their 95% CIs, and a P value of < 0.05 was considered statistically significant. Data were analyzed using the Statistical Package for Social Sciences, version 20. To eliminate skewed data distribution and heteroscedasticity, data are presented as medians with 95% confidence.<\/p>\n

Results<\/strong><\/p>\n

Table 1 shows dietary consumption as divided into quintiles in obese adolescents. Participants in the highest quintiles (Q5) of food consumption were being obese while subjects in the lowest quintiles (Q1) have normal weight subjects (p<0.001). Adolescents in the highest quintiles (Q5) showed significantly higher values of carbohydrates, sugar, fats, snakes, SFA, body fat % and frequency of MS components than Q1. The age (mean \u00b1 standard deviation) of the participants was 14 \u00b1 3.4 year, ranging from 13 to 17 years old. The mean BMI was 15.77\u00b12.55 ranging from 14.56\u00b1 2.12 to 25.78 \u00b1 3.65.<\/p>\n

Table 2 shows odds ratios of risk factors for MS components. High consumption of fats, carbohydrates, sweat snakes, SFA, sugar as well as sedentary life style, obesity and dyslipidemia increased the risk of MS.<\/p>\n

Table 1: Characteristics of dietary factors and body composition across quintiles of obese adolescents<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Nutrient\u00a0<\/strong><\/td>\nObese<\/strong><\/p>\n

Quintiles of food intake<\/strong><\/p>\n

Normal weight<\/strong><\/td>\n

P \u00a0<\/strong><\/td>\n<\/tr>\n
 <\/p>\n

Q5 (Highest)<\/td>\n

 <\/p>\n

Q3<\/td>\n

 <\/p>\n

Q1 (Lowest)<\/td>\n

<\/td>\n<\/tr>\n
Age (y) (mean\u00b1 SD)<\/strong><\/p>\n

\u00a0<\/strong><\/td>\n

9 \u00b12.9<\/td>\n9\u00b12.5<\/td>\n9 \u00b12.1<\/td>\n0.07<\/td>\n<\/tr>\n
BMI (kg\/m2<\/sup>) (<\/strong>mean\u00b1 SD)<\/strong><\/td>\n27.33\u00b14.6<\/td>\n26.33\u00b13.5<\/td>\n20.92\u00b12.1<\/td>\n0.06<\/td>\n<\/tr>\n
BMI z-score<\/strong><\/td>\n2.5\u00b1 0.98<\/td>\n2.4\u00b1 0.18<\/td>\n2.1\u00b1 0.23<\/td>\n0.07<\/td>\n<\/tr>\n
Total Food Intake, g<\/strong><\/td>\n1489.16<\/p>\n

(978.89:156.15)<\/td>\n

1369.2<\/p>\n

(948.71:1486.97)<\/td>\n

1274.01<\/p>\n

(990.22:1574.8)<\/td>\n

\u00a00.01<\/td>\n<\/tr>\n
Total Energy,\u00a0\u00a0 kcal<\/strong><\/td>\n2691<\/p>\n

(1626.07:2605.7)<\/td>\n

2351.4<\/p>\n

(1751.5:27.85)<\/td>\n

2027.75<\/p>\n

(1597.9:2674.6)<\/td>\n

0.01<\/td>\n<\/tr>\n
Food Density, kcal\/g<\/strong><\/td>\n1.9716<\/p>\n

(1.5236:1.8204)<\/td>\n

1.7798<\/p>\n

(1.594:2.0141)<\/td>\n

1.4436<\/p>\n

(1.493:1.8733)<\/td>\n

\u00a00.01<\/td>\n<\/tr>\n
Protein, g<\/strong><\/td>\n92.2<\/p>\n

(55.175:92.225)<\/td>\n

82.5<\/p>\n

(59.325:109.85)<\/td>\n

72.65<\/p>\n

(53.4:94.3)<\/td>\n

\u00a00.06<\/td>\n<\/tr>\n
Fat, g<\/strong><\/td>\n69.8<\/p>\n

(47.3:77.725)<\/td>\n

62.8<\/p>\n

(36.9:84.725)<\/td>\n

53.7<\/p>\n

(36.5:87.6)<\/td>\n

0.01<\/td>\n<\/tr>\n
Fat,%kcal<\/strong><\/td>\n30.0907<\/p>\n

(23.6413:29.88)<\/td>\n

27.7231<\/p>\n

(23.4308:29.70)<\/td>\n

26.319<\/p>\n

(21.3876:30.70)<\/td>\n

0.01<\/td>\n<\/tr>\n
Carbohydrate,\u00a0\u00a0\u00a0\u00a0\u00a0 g<\/strong><\/td>\n398.7<\/p>\n

(257.85:425.5)<\/td>\n

352.6<\/p>\n

(248.7:44.875)<\/td>\n

332.7<\/p>\n

(258.4:405.7)<\/td>\n

0.01<\/td>\n<\/tr>\n
Carbohydrate,\u00a0 %kcal<\/strong><\/td>\n72.8515<\/p>\n

(59.9634:68.3366)<\/td>\n

63.0261<\/p>\n

(57.9158:68.176)<\/td>\n

61.4524<\/p>\n

(59.6948:68.443)<\/td>\n

0.01<\/td>\n<\/tr>\n
Dietary Fiber, g<\/strong><\/td>\n22.1<\/p>\n

(15.875:30.75)<\/td>\n

21.5<\/p>\n

(16.4:31.95)<\/td>\n

23.1<\/p>\n

(13.9:31.6)<\/td>\n

0.06<\/td>\n<\/tr>\n
Sugars, g<\/strong><\/td>\n3.1<\/p>\n

(1:3.7)<\/td>\n

2.5<\/p>\n

(.9:4.425)<\/td>\n

1.15<\/p>\n

(0:4.32)<\/td>\n

0.01<\/td>\n<\/tr>\n
Sugars,\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0%kcal<\/strong><\/td>\n0.18232\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (0:0.88965)<\/td>\n0.05424\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (0:0.70778)<\/td>\n0.0274<\/p>\n

(0:0.92067)<\/td>\n

0.01<\/td>\n<\/tr>\n
Cholesterol, mg<\/strong><\/td>\n168.6<\/p>\n

(95.6:211.7)<\/td>\n

151.9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (79.15:220.1)<\/td>\n140.65<\/p>\n

(87.5:213)<\/td>\n

0.01<\/td>\n<\/tr>\n
SFA<\/strong><\/td>\n5.4<\/p>\n

(1.8:5.25)<\/td>\n

3.5<\/p>\n

(1.35:5.65)<\/td>\n

3.1<\/p>\n

(1.8:7.4)<\/td>\n

 <\/p>\n

0.01<\/td>\n<\/tr>\n

Components \u00a0of MS %<\/strong><\/td>\n70%<\/td>\n\u00a025%<\/td>\n5%<\/td>\n0.001<\/td>\n<\/tr>\n
Body fat % (kg) (mean \u00b1SD)<\/strong><\/td>\n31 \u00b1 7.8<\/td>\n25\u00b1 11.89<\/td>\n19 \u00b19.55<\/td>\n0.01<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Data are presented as medians (95% confidence intervals).<\/p>\n

Table 2: Multivariate odd ratios (OR) for and 95 % confidence intervals (CI) of MS, adjusted for age, BMI and body fat % .<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n
Characteristics<\/strong><\/td>\nOR*<\/strong><\/td>\n95.0 % CI<\/strong><\/td>\nP value<\/strong><\/td>\n<\/tr>\n
High fat consumption<\/p>\n

 <\/td>\n

1.72<\/td>\n1.558-1.948<\/td>\n0.001<\/td>\n<\/tr>\n
High consumption of carbohydrates<\/td>\n1.52<\/p>\n

 <\/td>\n

1.658-1.977<\/td>\n0.001<\/td>\n<\/tr>\n
Obesity<\/td>\n1.57<\/p>\n

 <\/td>\n

1.362-1.871<\/td>\n0.001<\/td>\n<\/tr>\n
High \u00a0sweat snakes<\/td>\n1.62<\/p>\n

 <\/td>\n

1.711-1.934<\/td>\n0.001<\/td>\n<\/tr>\n
High consumption of SFA<\/td>\n1.56<\/p>\n

 <\/td>\n

1.458-1.574<\/td>\n0.002<\/td>\n<\/tr>\n
High consumption of sugar<\/td>\n1.62<\/p>\n

 <\/td>\n

1.356-1.781<\/td>\n0.004<\/td>\n<\/tr>\n
Sedentary\u00a0 life<\/td>\n1.42<\/p>\n

 <\/td>\n

1.255-1.375<\/td>\n0.005<\/td>\n<\/tr>\n
Dyslipidemia<\/td>\n1.63<\/p>\n

 <\/td>\n

1.345-1.670<\/td>\n0.004<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Discussion<\/strong>\u00a0<\/strong><\/p>\n

Overweight and obesity were more prevalent among Egyptian adolescents in 12.1 and 6.2% of cases, respectively 17<\/sup>. Due to increase of dietary energy availability in Egypt a nutrition shift has been occurred \u00a03<\/sup>. Overweight and obesity are strongly linked with specific types of diets, including high consumption of fats, animal-based meals and processed foods18<\/sup>. Adolescents’ lifestyles are characteristically defined by physical inactivity, sedentary behavior, and unhealthy eating habits in general19<\/sup>. Obesity is linked with serious health risks and comorbidities, including cardio vascular disease, hypertension, hyperlipidemia, type 2 diabetes, and certain cancers4<\/sup>. Previously, it was assumed that levels of nutrient intakes may rise enough to stimulate pathogenic pathway \u00a0in obese subjects that leading to the activation of immune cells in the different metabolic tissues 20<\/sup> . Waist-BMI Ratio was independently associated with cardiovascular mortality , offering an enormous potential risk marker for obesity in the clinical setting21<\/sup>. Previous study reported\u00a0 week assoction between metabolic health and lifestyle factors in obese adolescents22<\/sup>. However, this study was only performed on Czech Caucasian population that might be not comparable with our results or other populations groups. Our findings are consistent with previous research that have found that a reduction in physical activity and a sedentary lifestyle are important risk factors for adolescent\u2019s obesity 23<\/sup>. Obesity and diabetes are on the rise, and a lack of regular physical activity has been highlighted as a key cause 24<\/sup>. Reduced regular physical activity has been identified as a major contributor to the rise in obesity and diabetes25<\/sup>. However, previous study found26<\/sup> \u00a0in this cohort of participants that a healthy dietary pattern resembling the Mediterranean diet was inversely associated with such characteristics of the MS27<\/sup> . Together with standard care of lifestyle changes and medication, dietary supplements derived from herbal resources could be an alternative therapeutic strategy that is safe, efficient, culturally acceptable, and has few side effects28<\/sup>. A detailed examination of the eating habits and the factors associated with them revealed similar risks that in consistent with our findings29<\/sup>. Many studies conducted in high-income countries discovered that adolescents with a higher socioeconomic status was more obese 30\u201332<\/sup> and more likely to choice carbohydrate-rich meals that are enjoyable or comforting, which\u00a0 lead to the increase of sedentary activity and influence on sleeping problem 33<\/sup>. Obese children have a high prevalence of dyslipidemia, with hypertriglyceridemia 34<\/sup>. Moreover, several previous studies reported that obese adolescents are more likely to be younger and in the early stages of puberty35<\/sup>.<\/p>\n

Conclusion<\/strong><\/p>\n

The present study provides evidence of causal relationship between obesity, dietary factors, dyslipidemia and risk of MS. Improvement of dietary habits and regular evaluation of blood lipids might prevent developing of MS in Egyptian adolescents. Physical activity less than 300 min per week was found an important risk factor of MS.<\/p>\n

Conflict of Interest<\/strong><\/p>\n

There is no conflict of interest<\/p>\n

Funding Sources<\/strong><\/p>\n

There is no funding sources<\/p>\n

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