Chapter 2

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*3.3. Statistical Analyses*

We applied different statistical tools and tests to analyze the relationship between *Ts* and *Ta*. Firstly, the coefficient of correlation (*r*), coefficient of determination (R2 ), standard error of regression (SE), and root mean square difference (RMSD) between *Ts* and *Ta* for all the stations was calculated. The SE is the standard deviation of the difference between two datasets while RMSD is the square root of mean of squared difference between two datasets. The R2 explains the efficiency of the regression model. In other words, it is the degree to which the independent variable will be able to explain the dependent variable. During the analysis, the *Ta* was considered to be the dependent variable (y) and *Ts* was considered as the independent variable (x). The *p*-values for all the analyses were <0.01 at 99% confidence level. Additionally, we estimated these statistical parameters for all available data for different climate zones namely monsoon-dominated, transition, westerlies-dominated, and precipitation shadow and for all the stations. The value of modified R2 which is adjusted for the number of predictors was observed to be around unadjusted R2 and therefore was not shown in the table. The *p*-value for each of the analyses was found to be less than 0.01 at 99% confidence level showing the effect of predictors. In addition, we analyzed the variation in the magnitude of the coefficient of the difference between *Ts* and *Ta* observed after the multiple regression taking January as the base month. We also plotted the box and whisker plots for the daily difference between *Ts* and *Ta* to graphically represent the overall range of the data, median of the data, and distribution of the data in different quartiles.

#### **4. Results**

### *4.1. Ts vs. Ta Relationship*

We performed different statistical analyses to derive several first-hand conclusions regarding the relationship between *Ts* and *Ta* in the Himalayan region. The results show a strong relationship between observed daily mean *Ta* and its respective daily mean *Ts* in general for all the stations (R2 = 0.77, RMSD = 5.9 ◦C, SE = 4.76, *n* = 11,101, *p*-value <0.01 at 99% confidence level) with variations corresponding to the altitudinal locations of the stations (Figure 2 and Table 3). The strongest relationship between *Ta* and *Ts* at daily scale was observed for Shimla (R2 = 0.94; RMSD = 1.5 ◦C, SE = 1.2 ◦C, *n* = 304, *p*-value <0.01 at 99% confidence level) and Mukteshwar stations (R2 = 0.94; RMSD = 1.6 ◦C, SE = 1.2 ◦C, *n* = 355, *p*-value <0.01 at 99% confidence level) which are located on the southern slopes in monsoon-dominated precipitation regime. The coefficient of determination is considerable for all the stations (R2 > 0.69, *p*-value <0.01 at 99% confidence level) at daily scale.

**Table 3.** Summary of all the statistical tests used for analysis between *Ts* and *Ta* for all the stations, climate regimes and for all observations at daily and 8-day scale. (R2 = Coefficient of determination; SE = Standard Error of Regression; RMSD = Root mean square difference).

**Figure 2.** The scatter plot between daily *Ts* (x-axis) and *Ta* (y-axis) for all the stations and overall observations with respective coefficient of determination (R2 ) and root mean square difference (RMSD) in ◦C.

The R2 and RMSD for all the stations show slight improvement for 8-day average (Figure 3 and Table 3). The relationship between *Ta* and *Ts* for 8-day average was also found to be strongest for Shimla (R2 = 0.97; RMSD = 1.4 ◦C, SE = 0.96 ◦C, *n* = 55, *p*-value <0.01 at 99% confidence level) and Mukteshwar stations (R2 = 0.96; RMSD = 1.2 ◦C, SE = 1.05, *n* = 63, *p*-value <0.01 at 99% confidence level). Overall, the *Ta* and *Ts* relationship was found to be stronger (R2 = 0.96; RMSD = 5.7 ◦C, SE = 4.5, *n* = 3552, *p*-value <0.01 at 99% confidence level) at 8-day scale for all the stations as well. The regression equation for all the analyses was also given which can be used for estimating *Ta* for different climate regimes with continuity over large spatiotemporal domain using *Ts* (Table 3) at both daily and 8-day scales.

**Figure 3.** The scatter plot between 8-day *Ts* (x-axis) and *Ta* (y-axis) for all the stations and overall observations with respective coefficient of determination (R2 ) and RMSD in ◦C.

The number of data points available for 8-day analysis is significantly less in comparison to the daily analysis (Table 1). Additionally, the use of 8-day data gives spatiotemporal continuity due to correction of cloud contaminated pixels but poses a restriction on the frequency of comparisons. We decided to represent the analysis of the relationship between *Ta* and *Ts* at daily scale henceforth because there was very small improvement in the results observed after the use of 8-day data and the daily observation are crucial for different geophysical models as explained in the Introduction section. We noticed certain spatial patterns in the daily differences between *Ts* and *Ta* which are discussed in detail in the following paragraphs.

#### *4.2. Altitudinal Relationship*

First, we present the relationship between *Ts* − *Ta* by considering altitudinal positions of the stations. The variation in altitude affects the *Ta* due to difference in density of air which causes a reduced green-house effect in the higher reaches. The RMSD between *Ts* and *Ta* for stations has a direct correlation with the elevation of the station (Figures 2–4). Although, the RMSD increases systematically with increase in elevation in general, small variation in this trend is observed for northernmost stations (Skardu and Srinagar). The annual mean RMSD is strongly correlated to the elevation (R2 = 0.74) in general (Figure 5a) except for two stations (Skardu and Srinagar), which even when located at comparatively low elevations show higher magnitude of RMSD (Figure 4). The R2 is stronger for monsoon season (Figure 5b) in comparison to annual (Figure 5a) and summer season values (Figure 5c). The observed *Ta* was unavailable for Skardu for winter months (Figure 5d). Therefore, the R2 is highest for winter when compared to monsoon, summer, and annual analysis. Furthermore, the magnitude of *Ts* − *Ta* is observed to be higher in summer months in comparison to the winter months for all the stations in general (Figure 4).

**Figure 4.** Graph showing the monthly mean of RMSD between daily *Ts* and *Ta* for different stations with respective elevation. The RMSD is higher in summer months and increases with increase in elevation.

**Figure 5.** The graph showing the relationship between average of RMSD between daily *Ts* and *Ta* for each station and its corresponding elevation for (**a**) the entire study period, (**b**) monsoon (JASO), (**c**) summer (MAMJ), and (**d**) winter (NDJF). The map shows the precipitation intensity (mm/h) data from Tropical Rainfall Measuring Mission (GES DISC, 2016) for the period 1 January 1999 to 31 December 2017 plotted through GIOVAANI (https://giovanni.gsfc.nasa.gov/giovanni/) [41].


**Syed Mohammed Mustakim**

Investigation of the Electrical Properties of Graphene-Reinforced Geopolymer Composites Reprinted from: *Mater. Proc.* **2023**, *13*, 34, doi:10.3390/materproc2023013034 ............ **275**


both the maintenance of physical activity in adulthood and reducing body weight in youth. The path from youth physical activity to adult obesity through youth obesity seemed to be stronger than the path through adult physical activity. However, the level of youth physical activity did not predict adult abdominal obesity in either men or women (Figure 1). The authors concluded that the prevalence of abdominal obesity as defined by waist circumference during adulthood was directly related to adult physical activity and youth overall obesity in both sexes. Youth physical activity had an indirect effect on abdominal obesity through both the maintenance of physical activity in adulthood and reduction in body weight in youth. Participation in and maintaining physical activity from youth into adulthood might play an important role in reducing obesity in adulthood.

Fig. 1. Estimadtd parameters (stan dardized solution) in structural equation for males (females)

In another study by Yang et al. [83], 1 493 Finnish children and adolescents aged 3 to 18 years were followed over a 21-year period. Participation in sport-club training and competitions were assessed by use of a self-report physical activity questionnaire. Participants were divided into athletes and non-athletes at each measurement point (1980 and 1983), and then classified into four groups: persistent athlete, starter, leaver and nonathlete. A mean score of youth sport was assessed by calculating the average of four consecutive measurements (1980-1989). The metabolic syndrome in adulthood was defined as a categorical variable based on the guidelines of the European Group for the Study of Insulin resistance and as a continuous metabolic syndrome risk score by summing the zscores of individual metabolic variables. The results indicated that the mean score of youth sport across the four time points and covariate variables were simultaneously entered as predictors for the adult metabolic syndrome risk score. Mean youth sport level emerged as a significant predictor of the metabolic syndrome in men (*β* = -0.149, *P* = 0.001) and women (*β* = -0.118, *P* = 0.005). Furthermore, non-athletic males and females had a significantly higher prevalence of the metabolic syndrome in adulthood than persistent athletes (Table 3). The relationships remained significant after adjustment for age and baseline clustered metabolic risk scores. The adds ratios and 95% confidence intervals for non-athletic males and females were 2.94 (1.01–8.99) and 4.04 (1.18–13.85), respectively. After additional adjustment for adult leisure-time physical activity, the trend of the associations was the same but

Physical Activity, Physical Fitness and Metabolic Syndrome 173

followed over a 28-year period. Physical activity was assessed by means of a sports participation inventory in youth and the Tecumseh community health study questionnaire in adulthood. The results found that sports participation during adolescence was not related to levels of cardiovascular risk factors at 40 years of age. In the Danish Youth and Sport Study [85], 101 adolescents aged 15–19 years were followed over an 8-year period. Physical activity was assessed by a questionnaire including the number of hours per week of sports participation and physical education lessons. Physical fitness in terms of aerobic fitness was calculated asVO2max relative to body weight (ml/min/kg). It was showed that the relationships between the absolute levels of physical fitness and activity in adolescence and the subsequent level of CVD risk factors were generally weak. However, the changes in physical fitness and physical activity were related to the absolute levels of CVD risk factors in young adulthood, especially in men. A subsequent study conducted by the same investigators [86] reported similar results when a physical activity index was constructed from the intensity and duration of the organized and unorganized sports activities. The results showed that the youth sports activities and fitness were not associated with clustered risk in adulthood. The lack of significant associations could be due to methodological limitations such as small samples and assessment of physical activity based on self-reported

minutes spent on sports activities rather than objective measure to physical activity.

changes in physical activity decrease the metabolic risk for different lifespan.

physical activity in association with adulthood metabolic syndrome.

**adulthood**

Moreover, future study is indicated in distinguishing between active and inactive on the adulthood risk of developing the metabolic syndrome. Future research is also need to develop and evaluate objective measures of physical activity (e.g., pedometers, accelerometers) in youth, define and measure the criteria of metabolic syndrome and clarify whether long-lasting

**6. Effect of change in physical activity and fitness on metabolic syndrome in**

According to the model [80], the most probable path is from childhood physical activity to adult physical activity and further to adult metabolic health. This supported by the studies on tracking of physical activity [87–90], and also studies on the relationship between physical activity and metabolic syndrome in either youth or adults as has just been mentioned above. Another potential path is physical activity in youth through youth clustered metabolic risk to adult metabolic syndrome. This path is supported by the finding that physical activity and physical fitness correlate negatively with the metabolic risk in youth [49]. Further, metabolic risk variables, especially obesity, likely track rather well from childhood to adulthood [82,91]. Only a few prospective population-based studies have reported long-term physical activity in predicting the prevalence of metabolic syndrome over time and especially of changes in

Laaksonen et al. [92] followed a cohort of 612 middle-aged men in the Kuopio Ischemic Heart Disease Risk Factor Study over a 4-year follow-up period. Leisure-time physical activity was measured by a questionnaire with the dose calculated as MET-minutes per week. Physical activity was then grouped into three levels: low-intensity (< 4.5 METsmin/wk), moderate- and high intensity (≥ 4.5 METs-min/wk) and high-intensity physical activity (≥ 7.5 METs-min/wk). Cardiorespiratory fitness (VO2max, ml·kg-1·min-1) was divided into three levels: low (≤ 28.9), moderate (29.0–35.6) and high (≥ 35.7). Men with moderate and vigorous physical activity were significant lower prevalence of the metabolic syndrome than those with low physical activity. The odds ratios were 0.60 (0.37–0.99) for physical activity ≥ 4.5 METs (>3 h/wk vs. ≤60 min/wk) and 0.48 (0.29–0.77) for physical activity ≥ 7.5

significant difference between persistent athletic and non-athletic males disappeared. Those males who dropped out from organized sport during the 3 years had higher prevalence of the metabolic syndrome compared with persistently athletic counterparts. The difference remained significant after adjustment for age, baseline clustered metabolic risk scores and adult leisure-time physical activity (OR = 4.52, 95% CI = 1.29–15.84).

The mechanisms explaining the relationship between sport participation in youth and the prevalence of metabolic syndrome in adulthood are not well understood. One of the more obvious explanations can be that youth sport may reduce the risk of metabolic syndrome in youth, which then tracks into adulthood. However, the explanation is not clear because adjustment for youth clustered metabolic risk does not change the result. In addition, sustained youth sport seems to predict low prevalence of the metabolic syndrome in adulthood 21 years later independently of adult physical activity. It is possible that participation in organized youth sport may establish lifelong habits for good health that in turn reduces risk for the metabolic syndrome. Participation in sustained youth sport may also lead to improved cardiovascular function and physical fitness that carries over into adulthood. Furthermore, children and adolescents who want to maintain their athletic abilities may have better awareness of other health related habits such as diet, smoking and sedentary lifestyle, all of which have been found to be related to risk for the metabolic syndrome. Finally, selfselection and genetic factors shall be taken into account as possible explanations for the direct relationship between youth sport and adult health. Thus, intensive and sustained participation in youth sport may benefit adult cardiovascular health and prevent the development of metabolic syndrome. Organizers of youth sport may have a significant impact on public health by paying attention to the factors that increase adherence in youth sport.

1) 3-year follow-up youth sport: persistently athlete (did between 1980 and 1983); starter (did not in 1980 but did in 1983); leaver (did in 1980 but did not in 1983); and non-athlete (did in neither 1980 nor 1983). 2) Adjusted for baseline age, smoking, total caloric intake and baseline-clustered risk for metabolic syndrome.

3) Additionally adjusted for adult leisure-time physical activity. \* *p*< 0.05.

Table 3. Odds ratios for the prevalence of metabolic syndrome (European Group for the Study of Insulin Resistance) according to organized youth sport 1) over 3 years in boys and girls

Contrary to the expected beneficial effect, no association between the level of physical activity and the risk of metabolic syndrome was found. In the Leuven Longitudinal Study on Lifestyle, Fitness and Health [84], 166 Belgian adolescent boys aged 13–18 years were

significant difference between persistent athletic and non-athletic males disappeared. Those males who dropped out from organized sport during the 3 years had higher prevalence of the metabolic syndrome compared with persistently athletic counterparts. The difference remained significant after adjustment for age, baseline clustered metabolic risk scores and

The mechanisms explaining the relationship between sport participation in youth and the prevalence of metabolic syndrome in adulthood are not well understood. One of the more obvious explanations can be that youth sport may reduce the risk of metabolic syndrome in youth, which then tracks into adulthood. However, the explanation is not clear because adjustment for youth clustered metabolic risk does not change the result. In addition, sustained youth sport seems to predict low prevalence of the metabolic syndrome in adulthood 21 years later independently of adult physical activity. It is possible that participation in organized youth sport may establish lifelong habits for good health that in turn reduces risk for the metabolic syndrome. Participation in sustained youth sport may also lead to improved cardiovascular function and physical fitness that carries over into adulthood. Furthermore, children and adolescents who want to maintain their athletic abilities may have better awareness of other health related habits such as diet, smoking and sedentary lifestyle, all of which have been found to be related to risk for the metabolic syndrome. Finally, selfselection and genetic factors shall be taken into account as possible explanations for the direct relationship between youth sport and adult health. Thus, intensive and sustained participation in youth sport may benefit adult cardiovascular health and prevent the development of metabolic syndrome. Organizers of youth sport may have a significant impact on public health

adult leisure-time physical activity (OR = 4.52, 95% CI = 1.29–15.84).

by paying attention to the factors that increase adherence in youth sport.

3) Additionally adjusted for adult leisure-time physical activity. \* *p*< 0.05.

Boys

Girls

syndrome.

Group Unadjusted OR (CI) Adjusted OR (CI) 2) Adjusted OR (CI) 3)

Persistent athlete 1.00 1.00 1.00 Starter 1.62 0.45 – 5.80 1.49 0.39 – 5.71 1.55 0.41 – 5.85 Leaver 4.55 1.39 – 14.87\* 4.70 1.31 – 16.91\* 4.52 1.29 – 15.84\* Non-athlete 3.06 1.08 – 8.64\* 2.94 1.01 – 8.99\* 2.72 0.90 – 8.17

Persistent athlete 1.00 1.00 1.00 Starter 1.67 0.38 – 7.27 1.63 0.35 – 7.64 1.66 0.36 – 7.69 Leaver 2.24 0.60 – 8.35 1.98 0.47 – 8.30 1.57 0.38 – 6.44 Non-athlete 3.54 1.08 – 11.55\* 4.04 1.18 – 13.85\* 3.46 1.03 – 11.66\* 1) 3-year follow-up youth sport: persistently athlete (did between 1980 and 1983); starter (did not in 1980 but did in 1983); leaver (did in 1980 but did not in 1983); and non-athlete (did in neither 1980 nor 1983). 2) Adjusted for baseline age, smoking, total caloric intake and baseline-clustered risk for metabolic

Table 3. Odds ratios for the prevalence of metabolic syndrome (European Group for the Study of Insulin Resistance) according to organized youth sport 1) over 3 years in boys and girls

Contrary to the expected beneficial effect, no association between the level of physical activity and the risk of metabolic syndrome was found. In the Leuven Longitudinal Study on Lifestyle, Fitness and Health [84], 166 Belgian adolescent boys aged 13–18 years were followed over a 28-year period. Physical activity was assessed by means of a sports participation inventory in youth and the Tecumseh community health study questionnaire in adulthood. The results found that sports participation during adolescence was not related to levels of cardiovascular risk factors at 40 years of age. In the Danish Youth and Sport Study [85], 101 adolescents aged 15–19 years were followed over an 8-year period. Physical activity was assessed by a questionnaire including the number of hours per week of sports participation and physical education lessons. Physical fitness in terms of aerobic fitness was calculated asVO2max relative to body weight (ml/min/kg). It was showed that the relationships between the absolute levels of physical fitness and activity in adolescence and the subsequent level of CVD risk factors were generally weak. However, the changes in physical fitness and physical activity were related to the absolute levels of CVD risk factors in young adulthood, especially in men. A subsequent study conducted by the same investigators [86] reported similar results when a physical activity index was constructed from the intensity and duration of the organized and unorganized sports activities. The results showed that the youth sports activities and fitness were not associated with clustered risk in adulthood. The lack of significant associations could be due to methodological limitations such as small samples and assessment of physical activity based on self-reported minutes spent on sports activities rather than objective measure to physical activity.

Moreover, future study is indicated in distinguishing between active and inactive on the adulthood risk of developing the metabolic syndrome. Future research is also need to develop and evaluate objective measures of physical activity (e.g., pedometers, accelerometers) in youth, define and measure the criteria of metabolic syndrome and clarify whether long-lasting changes in physical activity decrease the metabolic risk for different lifespan.

#### **6. Effect of change in physical activity and fitness on metabolic syndrome in adulthood**

According to the model [80], the most probable path is from childhood physical activity to adult physical activity and further to adult metabolic health. This supported by the studies on tracking of physical activity [87–90], and also studies on the relationship between physical activity and metabolic syndrome in either youth or adults as has just been mentioned above. Another potential path is physical activity in youth through youth clustered metabolic risk to adult metabolic syndrome. This path is supported by the finding that physical activity and physical fitness correlate negatively with the metabolic risk in youth [49]. Further, metabolic risk variables, especially obesity, likely track rather well from childhood to adulthood [82,91]. Only a few prospective population-based studies have reported long-term physical activity in predicting the prevalence of metabolic syndrome over time and especially of changes in physical activity in association with adulthood metabolic syndrome.

Laaksonen et al. [92] followed a cohort of 612 middle-aged men in the Kuopio Ischemic Heart Disease Risk Factor Study over a 4-year follow-up period. Leisure-time physical activity was measured by a questionnaire with the dose calculated as MET-minutes per week. Physical activity was then grouped into three levels: low-intensity (< 4.5 METsmin/wk), moderate- and high intensity (≥ 4.5 METs-min/wk) and high-intensity physical activity (≥ 7.5 METs-min/wk). Cardiorespiratory fitness (VO2max, ml·kg-1·min-1) was divided into three levels: low (≤ 28.9), moderate (29.0–35.6) and high (≥ 35.7). Men with moderate and vigorous physical activity were significant lower prevalence of the metabolic syndrome than those with low physical activity. The odds ratios were 0.60 (0.37–0.99) for physical activity ≥ 4.5 METs (>3 h/wk vs. ≤60 min/wk) and 0.48 (0.29–0.77) for physical activity ≥ 7.5

Physical Activity, Physical Fitness and Metabolic Syndrome 175

and diabetes (OR = 0.68, 95%CI = 0.52–0.91) over 28 years when adjusted for age and educational attendance. However, these associations were markedly attenuated when

Yang et al. [97] followed six cohorts of 2 060 (961 males) young adults aged 24–39 years in the Young Finns Study. Leisure-time physical activity was assessed using a self-report questionnaire completed in connection with a medical examination at two consecutive measurements in 1992 and 2001. By summing the physical activity items, a physical activity index was formed for both measurement points according to which the participants were divided into tracking groups: persistently active, increasingly active, decreasingly active, and persistently inactive. The prevalence of the metabolic syndrome on all three definitions was significantly lower in men and women who were persistently active during the 9 yr of follow-up compared with persistently inactive ones. In men, the odds ratios and their 95% confidence intervals were 0.23 (0.11–0.49) for the European Group for the Study of Insulin Resistance (EGIR) criteria, 0.54 (0.31–0.93) for the National Cholesterol Education Program-Adult Treatment Panel III (NCEP) criteria, and 0.49 (0.29–0.83) for the International Diabetes Federation (IDF) criteria. In women, the odds ratios were 0.33 (0.12–0.94) for EGIR, 0.21 (0.06–0.67) for NCEP, and 0.27 (0.11–0.63) for IDF. Also, women who were increasingly active were less likely on all definitions to have the metabolic syndrome than their persistently inactive counterparts. The associations remained significant for EGIR (OR = 0.28, 95% CI = 0.09–0.92), NCEP (OR = 0.22, 95% CI = 0.07–0.73), and IDF (OR = 0.42, 95% CI = 0.19–0.94). All of these associations remained significant after adjustment for potential confounders such as age, smoking and education (Table 4). The authors concluded that

> NCEP-ATP III Adjusted OR (CI)

IDF Adjusted OR (CI)

additional adjusted for baseline clustered metabolic risks.

EGIR Adjusted OR (CI) 2)

Persistently inactive 1.00 1.00 1.00

Persistently inactive 1.00 1.00 1.00

Decreasingly active 0.69 0.41 – 1.15 0.99 0.62 – 1.57 1.07 0.69 – 1.65 Increasingly active 0.73 0.30 – 1.79 0.58 0.22 – 1.52 0.48 0.19 – 1.27 Persistently active 0.23 0.11 – 0.49\*\*\* 0.54 0.31 – 0.93\* 0.49 0.29 – 0.83\*\*

Decreasingly active 0.68 0.35 – 1.34 0.65 0.35 – 1.21 0.59 0.33 – 1.05 Increasingly active 0.28 0.09 – 0.92\* 0.22 0.07 – 0.73\* 0.42 0.19 – 0.94\* Persistently active 0.33 0.12 – 0.94\* 0.21 0.06 – 0.67\*\* 0.27 0.11 – 0.63\*\*

EGIR, European Group for the Study of Insulin Resistance; NCEP-ATP III, National Cholesterol

1)Physical activity groups: persistently inactive (inactive both in 1992 and 2001); decreasingly active (change 1992-2001 from active to inactive); increasingly active (change 1992-2001 from inactive to

Table 4. Adjusted odds ratios for three definitions of metabolic syndrome according to

education Program-Adult Treatment Panel III; IDF, International Diabetes Federation.

2) Adjusted for age, smoking and education. \* *p*< 0.05, \*\* *p*<0.01, \*\*\* *p*<0.001.

active); and persistently active (active both 1992 and 2001).

change in physical activity groups 1) over a 9-yr period

Group

Women

Men

METs after adjustment for major confounding variables (age, body mass index, smoking, alcohol, and socioeconomic status) or potentially mediating variables (insulin, glucose, lipids, and blood pressure), especially in high-risk men. Vigorous physical activity had an even stronger inverse association, particularly in unfit men. Men in the highest tertile of cardiorespiratory fitness were 75% less likely to develop the metabolic syndrome than men in the lowest tertile of cardiorespiratory fitness after adjustment for major confounders, but the association was attenuated after adjustment for possible mediating variables. it was included that physical activity and cardiorespiratory fitness predicted directly or indirectly the development of metabolic syndrome.

A randomized controlled trial was used to investigate the effectiveness of supervised aerobic exercise training in 105 participants with the metabolic syndrome before and after 20 weeks [93]. 30.5% (32 participants) of the participants were no longer classified as having the metabolic syndrome following exposure to a standardized 20-week exercise program. It was suggested that aerobic exercise training showed prolonged vigorous exercise programs and reduced substantially in those with the metabolic syndrome. Although limited by lack of a control group, this study supported that the effectiveness of aerobic exercise training could be useful as a treatment strategy in prevention of the metabolic syndrome. In their further study of the impact of cardiorespiratory fitness on the risk of metabolic syndrome, obesity and mortality among 19 173 American men aged 20–83 years, Katzmarzyk et al. [94] reported that the odds ratios and their 95% confidence intervals for having risks of all-cause mortality were 1.11 (0.75-1.17) in normal weight, 1.09 (0.82-1.47) in overweight, and 1.55 (1.14-2.11) in obese men with the metabolic syndrome, compared with normal weight healthy men. The corresponding risks for cardiovascular disease mortality were 2.06 (0.92- 4.63) in normal weight, 1.80 (1.10-2.97) in overweight, and 2.83 (1.70-4.72) in obese men with the metabolic syndrome, compared with normal weight healthy men. However, the risks of all-cause mortality associated with obesity and metabolic syndrome were no longer significant after adjustment for cardiorespiratory fitness, which suggested that these risks were largely explained by overall physical fitness levels.

In the Medical Research Council Ely Study [95], 605 (249 males) middle-aged adults in England were followed over the past 5.6 years. Physical activity energy expenditure was measured objectively by individually calibrated heart rate against energy expenditure and was then divided into quartiles: < 44 kJ/kgFFM/d, 44-70 kJ/kgFFM/d, 71-100 kJ/kgFFM/d, and > 100 kJ/kgFFM/d. Aerobic fitness was predicted from a submaximal exercise stress test. Physical activity energy expenditure predicted progression toward the metabolic syndrome after adjusting for sex, baseline age, smoking, socioeconomic status, follow-up time, and baseline phenotypes. The associations remained significant after additional adjustment for aerobic fitness. While the relationship between aerobic fitness and metabolic syndrome was attenuated after adjusting for physical activity. In a cohort of the Oslo study, Holme et al. [96] followed 6 410 Norwegian middle-aged men from 1972/3 to 2000 in the city of Oslo. Leisure-time physical activity was measured by a questionnaire to classify men into four groups as follows: sedentary/light (usually reading, watching television or other sedentary occupations at leisure), moderate (walking, bicycling or other forms of physical activity including walking or bicycling to and from the place of work and a Sunday walk totalling at least four hours a week), moderately vigorous (exercise, sports, heavy gardening and similar activities totalling at least 4 hours a week), and vigorous (hard training or competition sports regularly several times a week). Physical activity was a significant predictor of the prevalence of metabolic syndrome (OR = 0.65, 95%CI = 0.54–0.80)

METs after adjustment for major confounding variables (age, body mass index, smoking, alcohol, and socioeconomic status) or potentially mediating variables (insulin, glucose, lipids, and blood pressure), especially in high-risk men. Vigorous physical activity had an even stronger inverse association, particularly in unfit men. Men in the highest tertile of cardiorespiratory fitness were 75% less likely to develop the metabolic syndrome than men in the lowest tertile of cardiorespiratory fitness after adjustment for major confounders, but the association was attenuated after adjustment for possible mediating variables. it was included that physical activity and cardiorespiratory fitness predicted directly or indirectly

A randomized controlled trial was used to investigate the effectiveness of supervised aerobic exercise training in 105 participants with the metabolic syndrome before and after 20 weeks [93]. 30.5% (32 participants) of the participants were no longer classified as having the metabolic syndrome following exposure to a standardized 20-week exercise program. It was suggested that aerobic exercise training showed prolonged vigorous exercise programs and reduced substantially in those with the metabolic syndrome. Although limited by lack of a control group, this study supported that the effectiveness of aerobic exercise training could be useful as a treatment strategy in prevention of the metabolic syndrome. In their further study of the impact of cardiorespiratory fitness on the risk of metabolic syndrome, obesity and mortality among 19 173 American men aged 20–83 years, Katzmarzyk et al. [94] reported that the odds ratios and their 95% confidence intervals for having risks of all-cause mortality were 1.11 (0.75-1.17) in normal weight, 1.09 (0.82-1.47) in overweight, and 1.55 (1.14-2.11) in obese men with the metabolic syndrome, compared with normal weight healthy men. The corresponding risks for cardiovascular disease mortality were 2.06 (0.92- 4.63) in normal weight, 1.80 (1.10-2.97) in overweight, and 2.83 (1.70-4.72) in obese men with the metabolic syndrome, compared with normal weight healthy men. However, the risks of all-cause mortality associated with obesity and metabolic syndrome were no longer significant after adjustment for cardiorespiratory fitness, which suggested that these risks

In the Medical Research Council Ely Study [95], 605 (249 males) middle-aged adults in England were followed over the past 5.6 years. Physical activity energy expenditure was measured objectively by individually calibrated heart rate against energy expenditure and was then divided into quartiles: < 44 kJ/kgFFM/d, 44-70 kJ/kgFFM/d, 71-100 kJ/kgFFM/d, and > 100 kJ/kgFFM/d. Aerobic fitness was predicted from a submaximal exercise stress test. Physical activity energy expenditure predicted progression toward the metabolic syndrome after adjusting for sex, baseline age, smoking, socioeconomic status, follow-up time, and baseline phenotypes. The associations remained significant after additional adjustment for aerobic fitness. While the relationship between aerobic fitness and metabolic syndrome was attenuated after adjusting for physical activity. In a cohort of the Oslo study, Holme et al. [96] followed 6 410 Norwegian middle-aged men from 1972/3 to 2000 in the city of Oslo. Leisure-time physical activity was measured by a questionnaire to classify men into four groups as follows: sedentary/light (usually reading, watching television or other sedentary occupations at leisure), moderate (walking, bicycling or other forms of physical activity including walking or bicycling to and from the place of work and a Sunday walk totalling at least four hours a week), moderately vigorous (exercise, sports, heavy gardening and similar activities totalling at least 4 hours a week), and vigorous (hard training or competition sports regularly several times a week). Physical activity was a significant predictor of the prevalence of metabolic syndrome (OR = 0.65, 95%CI = 0.54–0.80)

the development of metabolic syndrome.

were largely explained by overall physical fitness levels.

and diabetes (OR = 0.68, 95%CI = 0.52–0.91) over 28 years when adjusted for age and educational attendance. However, these associations were markedly attenuated when additional adjusted for baseline clustered metabolic risks.

Yang et al. [97] followed six cohorts of 2 060 (961 males) young adults aged 24–39 years in the Young Finns Study. Leisure-time physical activity was assessed using a self-report questionnaire completed in connection with a medical examination at two consecutive measurements in 1992 and 2001. By summing the physical activity items, a physical activity index was formed for both measurement points according to which the participants were divided into tracking groups: persistently active, increasingly active, decreasingly active, and persistently inactive. The prevalence of the metabolic syndrome on all three definitions was significantly lower in men and women who were persistently active during the 9 yr of follow-up compared with persistently inactive ones. In men, the odds ratios and their 95% confidence intervals were 0.23 (0.11–0.49) for the European Group for the Study of Insulin Resistance (EGIR) criteria, 0.54 (0.31–0.93) for the National Cholesterol Education Program-Adult Treatment Panel III (NCEP) criteria, and 0.49 (0.29–0.83) for the International Diabetes Federation (IDF) criteria. In women, the odds ratios were 0.33 (0.12–0.94) for EGIR, 0.21 (0.06–0.67) for NCEP, and 0.27 (0.11–0.63) for IDF. Also, women who were increasingly active were less likely on all definitions to have the metabolic syndrome than their persistently inactive counterparts. The associations remained significant for EGIR (OR = 0.28, 95% CI = 0.09–0.92), NCEP (OR = 0.22, 95% CI = 0.07–0.73), and IDF (OR = 0.42, 95% CI = 0.19–0.94). All of these associations remained significant after adjustment for potential confounders such as age, smoking and education (Table 4). The authors concluded that

EGIR, European Group for the Study of Insulin Resistance; NCEP-ATP III, National Cholesterol education Program-Adult Treatment Panel III; IDF, International Diabetes Federation.

1)Physical activity groups: persistently inactive (inactive both in 1992 and 2001); decreasingly active (change 1992-2001 from active to inactive); increasingly active (change 1992-2001 from inactive to active); and persistently active (active both 1992 and 2001).

2) Adjusted for age, smoking and education. \* *p*< 0.05, \*\* *p*<0.01, \*\*\* *p*<0.001.

Table 4. Adjusted odds ratios for three definitions of metabolic syndrome according to change in physical activity groups 1) over a 9-yr period

Physical Activity, Physical Fitness and Metabolic Syndrome 177

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maintaining a high level of physical activity across the life span might decrease the prevalence of the metabolic syndrome not only in the short term but also in the long term. Individuals should be encouraged to participate in regular physical activity as early as possible to prevent the risk of developing the metabolic syndrome and related adult-onset diabetes and cardiovascular diseases.

## **7. Conclusion**

Outlined in this chapter is a brief overview of leisure-time physical activity, cardiorespiratory fitness and muscular strength which focus is on prevention and intervention of the prevalence of the metabolic syndrome in youth and adulthood. In addition, there is a brief summarizes on maintaining regular physical activity and aerobic exercise over time focused on the metabolic syndrome in adulthood.

It is worth highlighting that regular leisure-time physical activity, endurance training, and strength training are critically important for prevention of the metabolic syndrome and its components in both early life and later life. Evidence is beginning to accumulate in the epidemiological literature which suggests that participation in regular aerobic and strength exercises, particularly when combined with moderate- or vigorous-intensity activity may alter all metabolic risk factors. Leisure-time physical activity is an effective intervention or modulation to improve cardiovascular functional capacity and prevent or delay the development of metabolic syndrome, which in turn maintains health status and reduces the incidence of diabetes and cardiovascular diseases. According to the recommendations of the American College of Sports medicine and the American Heart Association, all adults shall participate in accumulated moderate-intensity physical activity during leisure time for a minimum of 30 minutes or more on 5 days per week or vigorous intensity activity for a minimum of 20 minutes on 3 days per week [42]. It may be that the combination of reductions in energy intake and increases in energy expenditure, through structured exercise and other forms of physical activity, is one of the most effective way to prevent or delay the development of metabolic syndrome over time. This chapter focuses mainly on physical activity and aerobic exercise during leisure time related the prevalence of metabolic syndrome, however there is limited literature relating to specific sport activities, workrelated physical activity, commuting physical activity and household physical activity. Emphasis will be placed on the impact of these activities in the future.

## **8. Acknowledgments**

This study was financially supported by the Academy of Finland (grants no. 77841, 210283, 123621 [L.P.-R.], 121584, 124282), Social Insurance Institution of Finland, Ministry of Education, Turku University Foundation, Special Federal Grants for Turku University Hospital, Research Funds of the University of Helsinki (project no 2106012 [L.P.-R.], Juho Vainio Foundation, Finnish Foundation of Cardiovascular Research, Emil Aaltonen Foundation (M.H.), Finnish Medical Foundation, Finnish Cultural Foundation, Yrjö Jahnsson Foundation, and Signe and Ane Gyllenberg Foundation (M.H.).

## **9. References**

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Outlined in this chapter is a brief overview of leisure-time physical activity, cardiorespiratory fitness and muscular strength which focus is on prevention and intervention of the prevalence of the metabolic syndrome in youth and adulthood. In addition, there is a brief summarizes on maintaining regular physical activity and aerobic

It is worth highlighting that regular leisure-time physical activity, endurance training, and strength training are critically important for prevention of the metabolic syndrome and its components in both early life and later life. Evidence is beginning to accumulate in the epidemiological literature which suggests that participation in regular aerobic and strength exercises, particularly when combined with moderate- or vigorous-intensity activity may alter all metabolic risk factors. Leisure-time physical activity is an effective intervention or modulation to improve cardiovascular functional capacity and prevent or delay the development of metabolic syndrome, which in turn maintains health status and reduces the incidence of diabetes and cardiovascular diseases. According to the recommendations of the American College of Sports medicine and the American Heart Association, all adults shall participate in accumulated moderate-intensity physical activity during leisure time for a minimum of 30 minutes or more on 5 days per week or vigorous intensity activity for a minimum of 20 minutes on 3 days per week [42]. It may be that the combination of reductions in energy intake and increases in energy expenditure, through structured exercise and other forms of physical activity, is one of the most effective way to prevent or delay the development of metabolic syndrome over time. This chapter focuses mainly on physical activity and aerobic exercise during leisure time related the prevalence of metabolic syndrome, however there is limited literature relating to specific sport activities, workrelated physical activity, commuting physical activity and household physical activity.

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**5. Conclusions**

This review article provided a broad overview of the various types of organic and inorganic nanobiomaterials and their applications in the field of hard tissue engineering. Besides classifying nanobiomaterials, the survey covered several key aspects like bone/cartilage regeneration, drug/gene delivery, anti-infection properties, coatings, scaffold fabrication, and cancer therapy. A total of 550 articles selected by means of web search engines widely used in science and engineering were reviewed in this study. Interestingly, the number of reviewed articles was approximately the same for organic and inorganic biomaterials.

Biomaterials science is a highly multidisciplinary area. Developments in life science and nanotechnology enabled scientists and engineers to conceive new designs and improve the existing bone structure. For example, advances in nanotechnology allowed for the development of novel methods for fabricating new nanostructured scaffolds possessing a higher efficiency in tissue regeneration.

Nanomaterials represent an excellent tool for research and therapeutic approaches in bone tissue engineering. Organic nanomaterials are more biocompatible, nontoxic, and help more with cell regeneration than inorganic nanomaterials. However, inorganic nanomaterials provide better mechanical strength and inertness to chemical agent. From the references cited in this survey it appears that nanoparticles, graphene and nanocomposites are the most diffused types of nanostructures used for hard tissue applications. An important research trend which results in a rapidly growing number of published articles is the development of new composite nanobiomaterials especially for scaffold applications.

Interactions between bone cells and nanomaterials depend on the composition of nanoparticles. Proper selection of nanoparticles may result in faster bone regeneration and recovery. Besides composition, the overall performance of a nanobiomaterial depends on porosity, microstructure, mechanical properties and functionality. Nanomaterials-based scaffolds also play a major role in three-dimensional tissue growth. Nanostructural modifications provide a favorable environment for bone regeneration.

The survey presented in the article proved that tissue engineering supports (i) application of engineering design methods to functionally engineered tissues, (ii) development of novel biomaterials for constructing scaffolds that mimic extracellular matrix, and (iii) creating artificial microenvironments. Nanobiomaterials represent an excellent tool for research and therapeutic approaches in bone tissue engineering. However, further investigations should be aimed at producing advanced nanobiomaterials suitable for hard tissue engineering that can fill the gap between biomaterial fabrication and clinical implementation.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.