Vol 11, No 2 (2022)
Research paper
Published online: 2022-02-15

open access

Page views 4941
Article views/downloads 441
Get Citation

Connect on Social Media

Connect on Social Media

Influence of Physical Activity, Mediterranean Diet, Smoking, and Different Sociodemographic Variables on the Prevalence of Diabesity

Pere Riutord Sbert1, Bartomeu Riutord Fe1, Neus Riutord Fe1, Sebastiana Arroyo Bote1, Ángel Arturo López González1, José Ignacio Ramírez Manent2
Clin Diabetol 2022;11(2):90-96.

Abstract

Background: Diabesity is a pathological entity that encompasses the presence of obesity and type 2 diabetes in the same individual and, as occurs with both entities, its prevalence is increasing in industrialized countries. The objective of this study is to determine the influence of healthy habits and sociodemographic variables on the occurrence of diabesity. Methods: Descriptive, cross-sectional study in 1457 Spanish workers in which the influence of healthy habits such as physical exercise determined with the International. Physical Activity Questionnaire (IPAQ), adherence to the Mediterranean diet, and tobacco consumption were assessed, as well as sociodemographic variables such as age, sex, and social class on the prevalence of diabesity. Results: The prevalence of diabesity determined with seven scales increased as the level of physical activity decreased and adherence to the Mediterranean diet decreased. In the multivariate analysis, the variables that most influenced the appearance of diabesity were, in order, age over 50 years, male sex, and low or moderate physical activity. High adherence to the Mediterranean diet also had an inverse influence on diabesity, although the results obtained were not always significant; while tobacco consumption, Mediterranean diet, and social class II-III had no influence on almost any scale. Conclusions: Age is the risk factor that most increases the risk of diabesity, followed by the male sex.

RESEARCH PAPER

ISSN 2450–7458
e-ISSN 2450–8187

Influence of Physical Activity, Mediterranean Diet, Smoking, and Different Sociodemographic Variables on the Prevalence of Diabesity

Pere Riutord Sbert1Bartomeu Riutord Fe1Neus Riutord Fe1Sebastiana Arroyo Bote1Ángel Arturo López González1José Ignacio Ramírez Manent2
1Department of Odontology, Faculty of Odontology, ADEMA University School, Palma, Spain
2Balearic Islands Health Service, Palma, Spain

Address for correspondence:

Ángel Arturo López González

Department of Odontology, Faculty of Odontology

ADEMA University School, Gremi Passamaners 11 2º 07009

Palma, Balearic Islands, Spain

e-mail: angarturo@gmail.com

Clinical Diabetology 2022, 11; 2: 90–96

DOI: 10.5603/DK.a2022.0011

Received: 12.06.2021 Accepted: 20.12.2021

ABSTRACT

Background: Diabesity is a pathological entity that encompasses the presence of obesity and type 2 diabetes in the same individual and, as occurs with both entities, its prevalence is increasing in industrialized countries. The objective of this study is to determine the influence of healthy habits and sociodemographic variables on the occurrence of diabesity.

Methods: Descriptive, cross-sectional study in 1457 Spanish workers in which the influence of healthy habits such as physical exercise determined with the International. Physical Activity Questionnaire (IPAQ), adherence to the Mediterranean diet, and tobacco consumption were assessed, as well as sociodemographic variables such as age, sex, and social class on the prevalence of diabesity.

Results: The prevalence of diabesity determined with seven scales increased as the level of physical activity decreased and adherence to the Mediterranean diet decreased. In the multivariate analysis, the variables that most influenced the appearance of diabesity were, in order, age over 50 years, male sex, and low or moderate physical activity. High adherence to the Mediterranean diet also had an inverse influence on diabesity, although the results obtained were not always significant; while tobacco consumption, Mediterranean diet, and social class II-III had no influence on almost any scale.

Conclusions: Age is the risk factor that most increases the risk of diabesity, followed by the male sex. (Clin Diabetol 2022, 11; 2: 90–96)

Keywords: obesity, diabetes mellitus, diabesity, physical activity, Mediterranean diet

Introduction

Obesity is nowadays considered a major public health problem in the western world and is growing exponentially to the point of pandemic proportions [1]. Many publications relate excess weight, especially obesity, to the increase in several chronic pathologies, among which cardiovascular diseases [2], different types of cancer [3–4], osteoarticular [5], and respiratory diseases [6] stand out. It is therefore considered an important risk factor for general morbidity.

Obesity is defined as a chronic process characterized by a significant increase in fat mass [7] and consequently an increase in body weight. In epidemiological studies, the methods most commonly used to establish the diagnosis are anthropometric, according to which a person is considered obese when his or her body mass index is equal to or exceeds 30 kg/m2.

Several publications have established the increased risk of suffering type 2 diabetes mellitus in people who are overweight and particularly obese, among other factors, due to greater insulin resistance. Individuals with a greater accumulation of fat in the abdominal waist, central obesity, present a higher risk of insulin resistance.

Our study focuses on diabesity, a new concept that brings together two serious pathologies, obesity and type 2 [8–10] diabetes. In most cases, obesity appears prior to diabetes and is often responsible for the onset of diabetes, to such an extent that it is considered the most important cause of the increase in the prevalence of type 2 diabetes worldwide. Although body mass index (BMI) is the most commonly used variable in epidemiological studies to evaluate overweight and obesity — as it is a mathematical ratio between the mass and the height of the individual — it does not evaluate other dimensions such as lean mass or muscle mass, so people who exercise regularly can be classified as overweight due to their higher percentage of muscle mass. Similarly, individuals with normal weight and low muscle mass may have excess body fat. This makes it necessary to use other obesity assessment measures such as waist circumference and body fat measurement.

In the approach and prevention of obesity and type 2 diabetes, interventions aimed at modifying lifestyles are fundamental [11]. The aim of this study was to assess the influence of physical activity assessed with the International. Physical Activity Questionnaire (IPAQ), the Mediterranean diet, tobacco consumption, age, sex, and social class on the appearance of diabesity (diabetes + obesity).

Methods

A retrospective, cross-sectional study was conducted on 1584 Spanish workers during the period January 2017 and December 2017. A total of 127 were excluded (69 not accepting to participate and 58 not having the required age) leaving 1457 workers who were included in the study, 718 of whom were women (mean age 43.30 years) and 739 men (mean age 46.02 years). The workers were selected from among those attending periodic occupational medical examinations.

Inclusion criteria:

  • aged between 18 and 67 years;
  • being an active worker;
  • belonging to one of the companies collaborating in the study;
  • agreeing to participate in the study.

The anthropometric measurements of height and weight, clinical and analytical, were performed by the health personnel of the different occupational health units participating in the study after the measurement techniques had been homogenized.

Parameters related to diabesity included in the assessment:

  • A height and weight-measuring scale: model SECA 700 with 200 kg capacity with an added SECA 220 telescopic measuring rod with 60–200 cm range was used to measure weight and height.
  • Abdominal waist and hip circumference were measured with a SECA 20 tape measure. The person stood upright, feet together and trunk erect, abdomen relaxed, and upper limbs hanging on both sides of the body. The tape measure was placed parallel to the ground at the level of the last floating rib (waist) and horizontal to the hip (hips). Blood pressure was measured in the supine position with a calibrated OMRON M3 automatic sphygmomanometer after 10 minutes of rest. Three determinations were made at one-minute intervals and the mean value of the three was obtained. Blood tests were obtained by peripheral venipuncture after a 12-hour fasting period. Samples were sent to the reference laboratories and processed within 48–72 hours. Automated enzymatic methods were used for blood glucose, total cholesterol, and triglycerides. Values are expressed in mg/dL. High-density lipoprotein (HDL) was determined by precipitation with dextran sulfate Cl2Mg, and values are expressed in mg/dL. Low-density lipoprotein (LDL) was calculated using the Friedewald formula (provided that triglycerides were less than 400 mg/dL). Values are expressed in mg/dL. Friedewald formula: LDL = total cholesterol – HDL – triglycerides/5.

Blood glucose figures were classified according to the recommendations of the American Diabetes Association [12]. Patients with a previous diagnosis, those with a blood glucose figure greater than or equal to 126 mg/dL, those with an HbA1c ≥ 6.5%, or who were taking hypoglycemic treatment were classified as diabetic. Body mass index is calculated by dividing weight by height in meters squared. Obesity is considered to be 30 kg/m2 or more.

Five formulas were used to estimate the percentage of body fat:

  1. Clínica Universidad de Navarra Body Adiposity Estimator (CUN BAE) [13]:

–44.988 + (0.503 × age) + (10.689 × sex) + (3.172 × BMI) – (0.026 × BMI2) + (0.181 × BMI × sex) – (0.02 × BMI × age) – (0.005 × BMI2 × sex) + (0.00021 × BMI2 × age)

Where male equals 0 and female equals 1. The CUN BAE cut-off points for obesity are from 25% in men and 35% in women.

  1. Equation Córdoba for Estimation of Body Fat (ECORE-BF) [14]

−97.102 + 0.123 (age) + 11.9 (sex) + 35.959 (LBMI)

Where male equals 0 and female equals 1. The cut-off points are the same as CUNBAE.

  1. Palafolls formula [15]

Women = ([BMI/AC] ×10) + BMI + 10; Men = ([BMI/AC] ×10) + BMI

The authors propose the same cut-off points as CUN BAE.

  1. Deuremberg fat mass index [16]

Fat mass % = 1.2 (BMI) + 0.23 × (Age in years) – 10.8 × (sex) – 5.4

Where female equals 0 and male equals 1. Obesity is considered as from 25% in men and 32% in women.

  1. Relative fat mass [17] is calculated with the following formula where height and waist circumference are expressed in meters.

Women: 76 – (20 × (height/waist)); Men: 64 – (20 × (height/waist))

The cut-off points for obesity are the same as in the Deuremberg scale.

Body fat was determined by bioelectrical impedance measurement using a Tanita BC-420MA monitor. The Gallagher criteria [18] were used to classify this percentage.

A smoker was defined as a person who had regularly consumed at least 1 cigarette/day (or the equivalent in other types of consumption) in the previous month or had quit smoking less than a year before.

Social class was determined from the 2011 National Classification of Occupations (CNO-11) and based on the proposal made by the social determinants group of the Spanish Society of Epidemiology [19]. We opted for classification in three categories: Class I. Directors/managers, university professionals, athletes, and artists. Class II. Intermediate occupations and self-employed workers without employees. Class III. Unskilled workers.

Diet was assessed by means of the “Mediterranean diet adherence questionnaire” [20] used in the Predimed study, which includes 14 questions rated with 0 or 1 point each. Scores below 9 are considered low adherence and above 9, good adherence.

Physical activity was determined by means of the IPAQ [21], a 7-question self-administered questionnaire that assesses the physical activity performed in daily life in the previous week.

Statistical analysis

A descriptive analysis of the categorical variables was performed, by calculating the frequency and distribution of responses for each one. For quantitative variables, the mean and standard deviation were calculated, while for qualitative variables, the percentage was calculated. The bivariate association analysis was performed using the Chi-square test (with correction using Fisher’s exact statistic when conditions required it) and Student’s t-test for independent samples. For the multivariate analysis, binary logistic regression was used with the Wald method, with the calculation of the odds ratio and the Hosmer-Lemeshow goodness-of-fit test. Statistical analysis was performed with the SPSS 27.0 program, with an accepted statistical significance level of 0.05.

Ethical considerations and aspects

The study was approved by the Clinical Research Ethics Committee of the Balearic Islands Health Area no. IB 4383/20. All procedures were performed in accordance with the ethical standards of the institutional research committee and with the 2013 Declaration of Helsinki. All patients signed written informed consent documents before participating in the study.

Results

Table 1 shows the values of the anthropometric, clinical, analytical, sociodemographic, and healthy habit variables of the population studied. It can be observed that the values were worse among men, except for total cholesterol and tobacco consumption.

Table 1. Characteristics of the population

Women (n = 718)
mean (SD)

Men (n = 739)
mean (SD)

Total (n = 1457)
mean (SD)

P

Age [years]

43.30 (8.44)

46.02 (8.50)

44.68 (8.57)

< 0.0001

Height [kg]

66.29 (12.29)

82.24 (13.81)

74.38 (15.32)

< 0.0001

Weight [m]

1.62 (0.06)

1.73 (0.07)

1.68 (0.09)

< 0.0001

BMI [kg/m²]

25.36 (4.61)

27.40 (4.13)

26.39 (4.49)

< 0.0001

Waist [cm]

89.44 (16.36)

97.00 (10.65)

93.27 (14.27)

< 0.0001

Relative fat mass [%]

38.56 (7.34)

28.51 (4.98)

33.46 (8.02)

< 0.0001

CUN BAE [%]

35.91 (6.20)

27.23 (5.69)

31.51 (7.36)

< 0.0001

ECORE-BF [%]

35.85 (6.46)

27.21 (5.56)

31.47 (7.41)

< 0.0001

Palafolls formula [%]

38.22 (4.73)

30.22 (4.28)

34.16 (6.03)

< 0.0001

Deuremberg formula [%]

34.99 (6.28)

27.26 (5.75)

31.07 (7.15)

< 0.0001

Body fat impedance [%]

34.34 (9.91)

29.97 (8.17)

32.12 (9.33)

< 0.0001

Hip [cm]

105.78 (13.22)

108.77 (10.27)

107.29 (11.91)

< 0.0001

Systolic blood pressure [mmHg]

121.31 (17.05)

133.76 (18.11)

127.62 (18.66)

< 0.0001

Diastolic blood pressure [mmHg]

75.03 (10.58)

80.63 (11.43)

77.87 (11.36)

< 0.0001

Cholesterol [mg/dL]

186.02 (31.14)

183.37 (31.72)

184.67 (31.46)

0.108

HDL [mg/dL]

60.18 (13.55)

49.83 (12.16)

54.93 (13.86)

< 0.0001

LDL [mg/dL]

107.88 (28.16)

108.94 (29.15)

108.42 (28.66)

0.483

Triglycerides [mg/dL]

86.57 (43.59)

119.55 (87.42)

103.30 (71.28)

< 0.0001

Glycemia [mg/dL]

92.16 (16.31)

98.68 (19.54)

95.47 (18.30)

< 0.0001

%

%

%

P

< 35 years

16.71

10.42

13.52

< 0.0001

35–49 years

57.80

51.01

54.36

≥ 50 years

25.49

38.57

32.12

Social class I

18.94

8.80

13.80

< 0.0001

Social class II

63.65

82.67

73.30

Social class III

17.41

8.53

12.90

No tobacco

71.87

72.94

72.41

< 0.0001

Yes tobacco

28.13

27.06

27.59

MET low

23.68

19.08

21.35

< 0.0001

MET moderate

48.05

36.4

42.14

MET high

28.27

44.52

36.51

Adherence to Mediterranean diet low

36.49

48.17

42.42

< 0.0001

Adherence to Mediterranean diet high

63.51

51.83

57.58

The c test was used to determine the difference between the prevalence. The T-Student test was used to determine the difference between the means

The prevalence of diabesity with the different scales decreased as the level of physical exercise increased; this situation was seen in both men and women. (Tab. 2).

Table 2. Prevalence of diabesity with different obesity scales according to physical activity by sex

Women

Men

MET low
n = 170

MET moderate
n = 345

MET high
n = 203

MET low
n = 141

MET moderate
n = 269

MET high
n = 329

Diabesity

%

%

%

P

%

%

%

P

Body mass index

2.35

0.87

0.00

< 0.0001

3.55

2.97

2.43

< 0.0001

Relative fat mass

4.71

1.45

0.49

< 0.0001

7.09

5.20

3.04

< 0.0001

Deuremberg formula

4.71

1.45

0.49

< 0.0001

7.09

6.32

2.74

< 0.0001

Palafolls formula

4.71

1.45

0.49

< 0.0001

7.09

6.69

3.34

< 0.0001

CUN BAE

4.71

1.45

0.49

< 0.0001

6.38

6.32

2.74

< 0.0001

ECORE-BF

4.71

1.45

0.49

< 0.0001

6.38

6.32

2.74

< 0.0001

Body fat impedance

36.47

1.45

28.57

< 0.0001

56.03

51.67

41.95

< 0.0001

The prevalence of diabesity with the different scales was lower in the group with high adherence to the Mediterranean diet in men and women, although the differences observed were not always statistically significant (Tab. 3).

Table 3. Prevalence of diabesity with different obesity scales according to healthy food by sex

Women

Men

Adherence to
Mediterranean
diet low
n = 262

Adherence to
Mediterranean
diet high
n = 456

Adherence to
Mediterranean
diet low
n = 356

Adherence to
Mediterranean
diet high
n = 383

Diabesity

%

%

P

%

%

P

Body mass index

0.76

0.70

< 0.0001

2.81

2.67

ns

Relative fat mass

1.91

1.77

< 0.0001

5.06

4.18

< 0.0001

Deuremberg formula

1.53

ns

< 0.0001

5.06

4.70

< 0.0001

Palafolls formula

1.53

ns

< 0.0001

5.62

4.96

< 0.0001

CUN BAE

1.53

ns

< 0.0001

4.78

4.70

ns

ECORE-BF

1.53

ns

< 0.0001

4.78

4.70

ns

Body fat impedance

38.93

31.14

< 0.0001

56.46

40.47

< 0.0001

In the multivariate analysis by means of binary logistic regression, the covariates established were male, aged 50 years or above, smoker, low or moderate physical activity, low adherence to the Mediterranean diet, and social class II–III.

Sex and age were the only variables to display an influence on all the diabesity scales analyzed. Age showed the greatest influence with odds ratios ranging from 1.91 (95% CI 1.51–2.42) for body fat with bioimpedance and 5.30 (95% CI 2.71–10.37) for the Deuremberg formula. All results are presented in Table 4.

Table 4. Binary logistic regression

Men

Age ≥ 50 years

Smokers

MET low-moderate

Adherence to Mediterranean
diet low

Adherence to Mediterranean
diet high

Diabesity

OR (CI 95%)

OR (CI 95%)

OR (CI 95%)

OR (CI 95%)

OR (CI 95%)

OR (CI 95%)

Body mass index

2.47 (1.02– 6.00)

5.29 (2.20– 12.75)

ns

ns

ns

ns

Relative fat mass

2.07 (1.08– 3.98)

4.80 (2.48– 9.28)

ns

ns

ns

ns

Deuremberg formula

2.49 (1.28– 4.85)

5.30 (2.71– 10.37)

ns

2.57 (1.22– 5.40)

ns

ns

Palafolls formula

2.64 (1.36– 5.10)

5.24 (2.74– 10.03)

ns

2.17 (1.08– 4.35)

ns

ns

CUN BAE

2.43 (1.25– 4.75)

5.14 (2.62– 10.08)

ns

2.55 (1.21– 5.37)

ns

ns

ECORE-BF

2.49 (1.28– 4.81)

5.15 (2.60– 10.11)

ns

2.50 (1.22– 5.40)

ns

ns

Body fat bioimpedance

1.58 (1.26– 1.99)

1.91 (1.51– 2.42)

0.77 (0.60– 0.99)

1.57 (1.23– 2.00)

1.59 (1.27– 1.99)

4.40 (2.88– 6.73)

Discussion

The results of our study show that the prevalence of diabesity with all the models analyzed and in both sexes is lower in people who exercise regularly, mainly intensely. The same trend is found in people with high adherence to the Mediterranean diet. The factors that most increase the risk of presenting diabesity are male sex and age over 50 years, according to the results of the multivariate analysis; while tobacco consumption and social class show no influence.

We have found no studies that assess the influence of physical exercise, diet, smoking, or sociodemographic variables on the prevalence of diabesity, so a direct comparison cannot be established with our results. However, we have seen studies that assess the influence of the aforementioned variables on obesity and type 2 diabetes, and it is these studies that we use to make a comparison with our results.

As in our study, where a higher prevalence of diabesity is observed in men, data from various Spanish population surveys in people over 16 years of age also found this association [22].

Some authors discovered a relationship between racial and ethnic aspects and the increase in the prevalence of type 2 diabetes and obesity, possibly due to different dietary habits, levels of physical activity, and sleep cycles caused by different lifestyles [23]. These aspects were not assessed in the present study.

Most of the risk factors for obesity and type 2 diabetes are related to unhealthy lifestyle habits, including diet and physical exercise, and it is, therefore, important to act preventively on these unhealthy habits from a very early age [24]. Public health interventions should focus primarily on diabesity and unhealthy lifestyles, acting preferentially on sedentary lifestyles, the dietary system, and socioeconomic factors in general in order to be more effective [25].

Our work found an inverse relationship between a high level of physical activity and the presence of type 2 diabetes and obesity; this effect of exercise was found in practically all the studies analyzed [26–28]. We discovered a similar effect to that of physical exercise with the Mediterranean diet and the prevalence of diabetes and obesity, and our results are supported by those obtained by other authors [29–30].

The strengths of this study include the large sample size (over 1400 people); the inclusion of the social class variable, which is little used by other authors; and the variety of scales used to determine diabesity, specifically seven.

The most important contribution of our study is the assessment of the influence of physical exercise, diet, social class, and smoking on the prevalence of diabesity. Although there are other publications that evaluate the influence of these variables on obesity and diabetes mellitus, we are not aware of any study to date that evaluates both together (diabesity).

A fundamental limitation of the study is the fact that, since it deals with the working population, it does not include people who are unemployed, retired, or either under 18 or over 67 years of age, so the results cannot be extrapolated to the general population as they are not representative of it.

Conclusions

The factors that produce a greater increase in the risk of diabesity are advanced age, male sex, and a sedentary lifestyle. High adherence to the Mediterranean diet has an inverse influence on diabesity, although the results obtained are not always significant, whereas tobacco consumption and belonging to the least favored social classes do not seem to have an influence.

Conflict of interest

None declared.

REFERENCES

  1. López-González AA. Globesity: the modern epidemic that is fast becoming the biggest danger to world health. EC endocrinology and metabolic research 1. 2. 2017; 1(2): 56–57.
  2. Aranceta J, Foz M, Moreno B, et al. Documento de consenso: obesidad y riesgo cardiovascular. Clínica e Investigación en Arteriosclerosis. 2003; 15(5): 196232, doi: 10.1016/s0214-9168(03)78933-5.
  3. Calle EE, Rodriguez C, Walker-Thurmond K, et al. Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults. N Engl J Med. 2003; 348(17): 16251638, doi: 10.1056/NEJMoa021423, indexed in Pubmed: 12711737.
  4. Küper MA, Königsrainer I, Schmidt D, et al. Morbid obesity and subsequent pancreatic cancer: pylorus-preserving pancreatoduodenectomy after laparoscopic sleeve gastrectomy. Obes Surg. 2009; 19(3): 385388, doi: 10.1007/s11695-008-9679-4, indexed in Pubmed: 18815848.
  5. Marchesini G, Natale S, Tiraferri F, et al. The burden of obesity on everyday life: a role for osteoarticular and respiratory diseases. Diabetes Nutr Metab. 2003; 16(5-6): 284290, indexed in Pubmed: 15000439.
  6. Forga L, Petrina E, Barbería JJ. Complicaciones de la obesidad. An Sist Sanit Navar. 2002; 25(Suppl 1): 117126.
  7. Barbany M, Foz M. Obesidad: concepto, clasificación y diagnóstico. An Sist Sanit Navar. 2002; 25(Suppl 1): 716, doi: https://doi.org/10.23938/ASSN.0810.
  8. Faeh D, William J, Tappy L, et al. Prevalence, awareness and control of diabetes in the Seychelles and relationship with excess body weight. BMC Public Health. 2007; 7: 163, doi: 10.1186/1471-2458-7-163, indexed in Pubmed: 17640380.
  9. Rodriguez A, Catalán V, Gómez-Ambrosi J, et al. Visceral and subcutaneous adiposity: are both potential therapeutic targets for tackling the metabolic syndrome? Current Pharmaceutical Design. 2007; 13(21): 21692175, doi: 10.2174/138161207781039599.
  10. Ohnishi H, Saitoh S, Takagi S, et al. Incidence of insulin resistance in obese subjects in a rural Japanese population: the Tanno and Sobetsu study. Diabetes Obes Metab. 2005; 7(1): 8387, doi: 10.1111/j.1463-1326.2004.00381.x, indexed in Pubmed: 15642079.
  11. Toplak H, Leitner DR, Harreiter J, et al. “Diabesity”-Obesity and type 2 diabetes (Update 2019). Wien Klin Wochenschr. 2019; 131(Suppl 1): 7176, doi: 10.1007/s00508-018-1418-9, indexed in Pubmed: 30980154.
  12. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2011; 34(Supplement_1): S62S69, doi: 10.2337/dc11-s062.
  13. Gómez-Ambrosi J, Silva C, Catalán V, et al. Clinical usefulness of a new equation for estimating body fat. Diabetes Care. 2012; 35(2): 383388, doi: 10.2337/dc11-1334, indexed in Pubmed: 22179957.
  14. Molina-Luque R, Romero-Saldaña M, Álvarez-Fernández C, et al. Equation córdoba: a simplified method for estimation of body fat (ECORE-BF). Int J Environ Res Public Health. 2019; 16(22): 4529, doi: 10.3390/ijerph16224529, indexed in Pubmed: 31731813.
  15. Mill-Ferreyra E, Cameno-Carrillo V, Saúl-Gordo H, et al. Estimation of the percentage of body fat based on the body mass index and the abdominal circumference: Palafolls Formula. Semergen. 2019; 45(2): 101108, doi: 10.1016/j.semerg.2018.04.007, indexed in Pubmed: 30268360.
  16. Deurenberg P, Weststrate JA, Seidell JC. Body mass index as a measure of body fatness: age- and sex-specific prediction formulas. Br J Nutr. 1991; 65(2): 105114, doi: 10.1079/bjn19910073, indexed in Pubmed: 2043597.
  17. Woolcott OO, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentage — A cross-sectional study in American adult individuals. Sci Rep. 2018; 8(1): 10980, doi: 10.1038/s41598-018-29362-1, indexed in Pubmed: 30030479.
  18. Gallagher D, Heymsfield SB, Heo M, et al. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr. 2000; 72(3): 694701, doi: 10.1093/ajcn/72.3.694, indexed in Pubmed: 10966886.
  19. Domingo-Salvany A, Bacigalupe A, Carrasco JM, et al. Propuestas de clase social neoweberiana y neomarxista a partir de la Clasificación Nacional de Ocupaciones 2011. Gaceta Sanitaria. 2013; 27(3): 263272, doi: 10.1016/j.gaceta.2012.12.009.
  20. Miró Ò, Martín-Sánchez FJ, Jacob J, et al. Valoración del grado de adherencia a la dieta mediterránea en pacientes con insuficiencia cardiaca: Estudio DIME-EAHFE. Anales del Sistema Sanitario de Navarra. 2016; 39(2): 261268.
  21. Serón P, Muñoz S, Lanas F. Nivel de actividad física medida a través del cuestionario internacional de actividad física en población Chilena. Revista médica de Chile. 2010; 138(10): 12321239, doi: 10.4067/s0034-98872010001100004.
  22. Basterra-Gortari FJ, Bes-Rastrollo M, Ruiz-Canela M, et al. Prevalence of obesity and diabetes in Spanish adults 1987-2012. Med Clin (Barc). 2017; 148(6): 250256, doi: 10.1016/j.medcli.2016.11.022, indexed in Pubmed: 28081903.
  23. Aranceta-Bartrina J, Pérez-Rodrigo C, Alberdi-Aresti G, et al. Prevalencia de obesidad general y obesidad abdominal en la población adulta española (2564 años) 20142015: estudio ENPE. Revista Española de Cardiología. 2016; 69(6): 579587, doi: 10.1016/j.recesp.2016.02.010.
  24. Bhupathiraju SN, Hu FB. Epidemiology of obesity and diabetes and their cardiovascular complications. Circ Res. 2016; 118(11): 17231735, doi: 10.1161/CIRCRESAHA.115.306825, indexed in Pubmed: 27230638.
  25. Wells JCK. The diabesity epidemic in the light of evolution: insights from the capacity-load model. Diabetologia. 2019; 62(10): 17401750, doi: 10.1007/s00125-019-4944-8, indexed in Pubmed: 31451870.
  26. Sánchez M, Sánchez E, Hernández M, et al. ILERVAS project collaborators. Dissimilar impact of a mediterranean diet and physical activity on anthropometric indices: a cross-sectional study from the ILERVAS project. Nutrients. 2019; 11(6): 1359, doi: 10.3390/nu11061359, indexed in Pubmed: 31212934.
  27. Rosique-Esteban N, Díaz-López A, Martínez-González MA, et al. PREDIMED-PLUS investigators. Leisure-time physical activity, sedentary behaviors, sleep, and cardiometabolic risk factors at baseline in the PREDIMED-PLUS intervention trial: A cross-sectional analysis. PLoS One. 2017; 12(3): e0172253, doi: 10.1371/journal.pone.0172253, indexed in Pubmed: 28273154.
  28. Wolff-Hughes DL, Fitzhugh EC, Bassett DR, et al. Total activity counts and bouted minutes of moderate-to-vigorous physical activity: relationships with cardiometabolic biomarkers using 2003-2006 NHANES. J Phys Act Health. 2015; 12(5): 694700, doi: 10.1123/jpah.2013-0463, indexed in Pubmed: 25109602.
  29. Ulian MD, Aburad L, da Silva Oliveira MS, et al. Effects of health at every size® interventions on health-related outcomes of people with overweight and obesity: a systematic review. Obes Rev. 2018; 19(12): 16591666, doi: 10.1111/obr.12749, indexed in Pubmed: 30261553.
  30. Anton SD, Hida A, Heekin K, et al. Effects of popular diets without specific calorie targets on weight loss outcomes: systematic review of findings from clinical trials. Nutrients. 2017; 9(8), doi: 10.3390/nu9080822, indexed in Pubmed: 28758964.