*2.4. Statistical Analysis*

Data were processed using the SPSS statistical package (SPSS for windows version 15.0, 2008 SPSS INC, Chicago, IL, USA).

Quantitative variables with normal distribution were described as mean and standard deviation (Mean (SD)), quantitative variables with non-normal distribution were described as Median and interquartile range (Median (p25–p75)) and, finally, qualitative variables as total number and percentages (Total number (%)).

The inferential analysis tests used were Student's *t*-test to compare means of normal quantitative variables; the Mann-Whitney U test to compare means of non-normal variables. Chi-square test to compare qualitative variables. Linear regression analysis to compare continuous variables. Multivariate logistic regression analysis to assess the causal relationships between qualitative variables. The significance level was conventionally set at *p* < 0.05.

#### **3. Results**

#### *3.1. Description of the Sample*

A total of 250 postmenopausal women with a mean age of 56.17 (3.91) years and a median body mass index (BMI) of 32.27 (24.14–39.59) kg/m<sup>2</sup> were analyzed.

The presence or absence of obesity was established as a study parameter, so the comparison will be established based on BMI (patients with a BMI > 30 kg/m<sup>2</sup> (O) vs. patients with a BMI < 30 kg/m2 (NoO)). When we made this division, we had 124 patients (49.6%) with a BMI < 30 kg/m2; 126 patients (50.4%) with a BMI of > 30 kg/m2.

The differences between the different variables related to bone metabolism between the two cohorts are shown in Table 1. An increase in tobacco consumption was observed in patients with a BMI of less than 30 kg/m<sup>2</sup> and a decrease in the amount of physical activity among patients with a BMI greater or equal to 30 kg/m2. On the other hand, there was an increased dietary intake of calcium and vitamin D in the group of patients with a BMI greater than 30 kg/m2 (Table 1).


**Table 1.** Differences between the presence or absence of obesity in variables than may affect in bone metabolism.

<sup>1</sup> BMI: Body Mass Index.

#### *3.2. Differences in Bone Turnover Biochemical Parameters*

In obese patients (BMI > 30 kg/m2) there is a lower 25OHvitamin D levels (O:17.27 (7.85) ng/mL; NoO: 24.51 (9.60); *p* < 0.01), and a higher in intact PTH levels (O:53.24 (38.44–65.96) ng/mL; NoO: 35.24 (25.36–42.40) ng/mL *p* < 0.01). It was also observed to have lower P1NP levels (O: 45.46 (34.39) ng/mL; NoO: 56.74 (45.34–70.74) ng/mL; *p* < 0.01), a marker of bone formation, and we did not observe an effect on beta-crosslaps (O:0.34 (0.14) ng/mL; NoO: 0.33 (0.14) ng/mL; *p* > 0.05).

A stratified analysis by age quartiles was performed (Q1: under 53 years: 89 (35.6%); Q2: 53–56 years: 69 (27.6%); Q3: 56–59 years: 65 (26%); Q4: older than 60 years: 27 (10.8%) patients). The differences in the parameters according to the quartiles are shown in Table 2. A lower vitamin D value and a higher PTH maintained in the four quartiles were observed in patients with BMI > 30 kg/m2. In these patients, P1NP was observed to be lower in the lowest age quartiles, while crosslaps were found to be high in the highest age quartile Table 2.

**Table 2.** Differences in bone metabolism biochemical markers between BMI agruped by age Quartiles.


<sup>1</sup> BMI: Body Mass Index; PTH: Parathyroid Hormone; P1NP: amino-terminal propeptide of type I procollagen. *p-value < 0.05 (bold and italics)*.

#### *3.3. Correlation between BMI and Bone Turnover Biochemical Markers*

A negative correlation was observed between the vitamin D levels and P1NP, while a positive correlation was observed between PTH and body mass index (Table 3).

When stratifying according to age quartiles, a negative correlation was observed that was maintained in all quartiles of vitamin D and in the youngest age quartiles of P1NP. PTH maintained the correlation in all quartiles except in the highest age quartile, and the resorption parameter showed a correlation in the third age quartile (Table 3).


**Table 3.** Correlation analysis of body mass index (BMI) with bone metabolism parameters in total sample and grouped by age quartiles.

<sup>1</sup> PTH: Parathyroid Hormone; <sup>2</sup> P1NP: amino-terminal propeptide of type I procollagen. *p-value < 0.05 (bold and italics)*.

#### *3.4. Multivariate Analysis*

No significant differences were observed in fractures at five years of any type according to BMI (Figure 1).

**Figure 1.** Differences in type fracture depending on body mass index (BMI).

A multivariate analysis was performed based on age, body mass index, and risk factors for fracture (age, smoking, physical activity, and alcohol consumption) to assess the risk factors for fracture without detecting data on the relationship between BMI and risk of total fracture risk (OR = 0.90 (95%CI 0.30–2.72); *p* = 0.85) (Osteoporotic Fracture: OR = 1.61 (95%CI 0.17–15.06); *p* = 0.68. Non-osteoporotic Fracture: OR = 0.77 (95%CI 0.23–2.65), *p* = 0.68).
