Abstract
Introduction
Metabolic syndrome (MetS) is an important predictor of cardiovascular mortality. Identification of occurrence and regression trends of MetS could permit elaboration of preventive strategies with new targets. The objective of this study was to analyze the occurrence and regression rates of MetS and its associated factors in the representative cohort of Spain of the di@bet.es study.
Research design and methods
The di@bet.es study is a prospective cohort where 5072 people representative of the Spanish population over 18 years of age were randomly selected between 2009 and 2010. Follow-up was a median of 7.5 (IQR 7.2–7.9) years, with 2408 (47%) participating subjects. A total of 1881 (78%) subjects had all the pertinent data available and were included in this study.
Results
Of the 1146 subjects without baseline criteria for MetS, 294 (25.7%) developed MetS during follow-up, while of the 735 patients with prior MetS, 148 (20.1%) presented regression. Adjusted MetS incidence per 1000 person-years was 38 (95% CI 32 to 44), while regression incidence was 36 (95% CI 31 to 41). Regression rate was independently higher than incidence rate in the following: women, subjects aged 18–45, university-degree holders, patients without central obesity, without hypertension, as well as those with body mass index of <25 kg/m2. Lower progression and higher regression rates were observed with an adapted 14-point Mediterranean Diet adherence screener questionnaire score of >11 in both groups and with >500 and>2000 MET-min/week of physical activity, respectively.
Conclusions
This study provides MetS incidence and regression rates, and identifies the target population for intervention strategies in Spain and possibly in other countries.
Keywords: metabolic syndrome, lifestyle, epidemiology
Significance of this study.
What is already known about this subject?
Metabolic syndrome (MetS) is one of the main predictors of cardiovascular mortality.
Knowing the trends of occurrence and regression of the MetS and its associated factors can allow defining preventive strategies and new targets to intervene.
What are the new findings?
Adjusted MetS incidence per 1000 person-years was 38 (95% CI 32 to 44), while regression incidence was 36 (95% CI 31 to 41).
Regression rate was independently higher than incidence rate in the following: women, subjects aged 18–45, university-degree holders, patients without central obesity, without hypertension, as well as those with a body mass index of <25 kg/m2.
To reduce the occurrence of MetS it is necessary to achieve a higher degree of adherence to a Mediterranean diet.
How might these results change the focus of research or clinical practice?
This study provides MetS incidence and regression rates and identifies the target population for intervention strategies in Spain and possibly in other countries. Lower progression and higher regression rates were observed with an adapted 14-point Mediterranean Diet adherence screener questionnaire score of >11 in both groups and with >500 and >2000 MET-min/week of physical activity, respectively.
Researchers should further investigate how lifestyle can be optimized for health promotion and prevention of MetS.
Introduction
Metabolic syndrome (MetS) is the combination of a range of metabolic disorders that include central obesity, abnormal glucose regulation, atherogenic dyslipidemia and arterial hypertension coexisting in the same individual, predisposing to cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM). The definition of MetS was agreed by different scientific societies during the last decade,1 recommending the use of specific waist circumference measurements adapted to each population. Its fundamental component is represented by visceral obesity. The relevance of the MetS lies on doubling the risk of CVD between 5 and 10 years following the diagnosis, and the occurrence of T2DM by 5 years. The prevalence in Spain reached just over 42% of men and 32% of women in 2010.2 Since then, national strategies for the prevention of T2DM and obesity have been reinforced.3 4
The Diabetes Prevention Program (DPP) study demonstrated the regression from pre-diabetes to normal glucose regulation achieved by the reduction of body weight5 6 and was also associated with a reduction in a wide range of microvascular complications.7 These findings explain why so much emphasis has been placed on obesity programs/interventions. Regression from pre-diabetes and T2DM has been consistently found after surgical treatment of obesity in several series8–11 Considering that the clinical presentation of the MetS can evolve in two directions, occurrence and regression, both reducing its occurrence and facilitating its regression are equally important. In order to assess the efficacy of any intervention, the same population should be assessed simultaneously during the same period of time. So far, only the DPP has simultaneously analyzed the reduction in the risk for progression to type 2 diabetes (8%) and the regression to normal glucose regulation (35%) through an intensive lifestyle intervention or metformin treatment in overweight or obese subjects with impaired glucose regulation at low risk of developing T2DM.6 Other components of the MetS have not been evaluated prospectively, and to our knowledge, the determinants associated with the regression or progression of the MetS are unknown.
The di@bet.es cohort was selected between 2009 and 2010 from the five basic healthcare units and was built from a representative sample of subjects of the non-institutionalized Spanish population. Its main objective was to evaluate the prevalence of T2DM based on the oral glucose tolerance test (OGTT), in order to define preventive strategies developed at the population level and plan improvements.3 4 Our group has published previously the prevalence of T2DM12 and MetS2 from the data collected during the di@bet.es study. The cohort of subjects was followed up prospectively until 2016–2017, when a cross-sectional study was undertaken. The main aim of this study was to address the rate of progression of the subjects who were not diagnosed with MetS at baseline and the rate of regression of the subjects who did, and to analyze the determinants associated with both situations.
Research design and methods
Population
A total of 5072 subjects over 18 years of age were randomly selected from the National Health System registry of users (covering over 99% of the Spanish population). This sample is representative of the non-institutionalized Spanish population. A broader description of the study design was previously published.12 The cohort was re-evaluated during 2016–2017. A total of 2408 subjects participated in the follow-up analysis. The characteristics of non-responders and responders have been recently reported.13
A total of 1881 (78%) subjects had all the necessary information available for each of the MetS components at baseline and at follow-up. The subjects were divided into two groups: group A, without MetS at baseline (n=1146), and group B, with MetS at baseline (n=735). The characteristics of the patients are shown in table 1.
Table 1.
Characteristics of the study population stratified by absence (group A) or presence (group B) of metabolic syndrome at baseline
| All sample | Group A | Group B | P value | |
| Number | 1881 | 1146 | 735 | |
| Men | 802 (42.6) | 419 (36.6) | 383 (52.1) | <0.001 |
| Women | 1079 (57.4) | 727 (63.4) | 352 (47.9) | |
| Age (years) | 50.22±14.47 | 46.06±13.90 | 56.69±12.89 | <0.001 |
| Body weight (kg) | 75.02±14.91 | 70.03±12.75 | 82.83±14.71 | <0.001 |
| BMI (kg/m2) | 28.18±4.97 | 26.23±3.98 | 31.23±4.84 | <0.001 |
| WC (cm) | ||||
| Men | 98.93±11.07 | 93.41±9.83 | 104.96±9.02 | <0.001 |
| Women | 90.82±14.27 | 85.32±11.69 | 102.18±12.28 | <0.001 |
| SBP (mm Hg) | 130.89±18.98 | 123.93±16.73 | 141.76±17.08 | <0.001 |
| DBP (mm Hg) | 77.00±10.29 | 73.95±9.57 | 81.77±9.53 | <0.001 |
| FG (mmol/L) | 5.52±1.52 | 5.05±0.81 | 6.26±2.01 | <0.001 |
| Total-chol (mmol/L) | 5.12±1.01 | 5.06±0.97 | 5.22±1.07 | 0.005 |
| TG (mmol/l) | 1.37±1.05 | 1.06±0.47 | 1.87±1.44 | <0.001 |
| HDL-chol (mmol/L) | 1.35±0.33 | 1.45±0.32 | 1.21±0.29 | <0.001 |
| LDL-chol (mmol/L) | 2.75±0.76 | 2.69±0.73 | 2.85±0.79 | <0.001 |
| Obesity (≥30 kg/m2) | 571 (30.4) | 182 (15.9) | 389 (52.9) | <0.001 |
| Uric acid (μmol/L) | 307.09±88.66 | 283.52±80.60 | 343.82±88.21 | <0.001 |
| Educational level | <0.001 | |||
| No studies | 197 (10.5) | 63 (5.5) | 134 (18.3) | |
| Elementary | 702 (37.3) | 393 (34.3) | 309 (42.0) | |
| Secondary education | 671 (35.7) | 450 (39.3) | 221 (31.1) | |
| University degree | 148 (7.9) | 240 (20.9) | 71 (9.6) | |
| Socioeconomic status | <0.001 | |||
| Salaried worker | 937 (49.8) | 640 (55.8) | 297 (40.4) | |
| Retired | 333 (17.7) | 141 (12.3) | 192 (26.1) | |
| Unemployed | 154 (8.2) | 110 (9.6) | 44 (6.0) | |
| Student | 46 (2.4) | 43 (3.8) | 3 (0.4) | |
| Domestic work | 366 (19.5) | 190 (16.6) | 176 (23.9) | |
| Others | 45 | 22 | 23 | |
Data are mean±SD or n (%).
BMI, body mass index; DBP, diastolic blood pressure; FG, fasting serum glucose; HDL-chol, high-density lipoprotein cholesterol; LDL-chol, low-density lipoprotein cholesterol; SBP, systolic blood pressure; TG, triglyceride; Total-chol, total cholesterol; WC, waist circumference.
MetS definition
MetS was defined according to the Harmonized definition1 using the specific waist circumference (WC) measurement of the Spanish population (94.5 cm in men and 89.5 cm for women) based on a Spanish population study14 and confirmed in the baseline study2 as the cut-off points with the highest sensitivity and specificity. The cut-off points for the rest of the components were −blood pressure (BP)>130/85 mm Hg, −triglyceride (TG)>150 mg/dL (1.7 mmol/L), −glucose>100 mg/dL (5.6 mmol/L) and −HDL-chol less than 40 mg/dL (1 mmol /L) for men and 50 mg/dL (1.3 mmol/L) for women. Subjects who received appropriate pharmacological treatment for hypertension, dyslipidemia or impaired glucose tolerance/diabetes were considered as having the respective risk factors. In order to establish the diagnosis of the MetS at least three abnormal components were required. Subjects who withdraw pharmacological treatment and were under the cut-off points were considered as having a regression for the respective component of the MetS.
Outcomes
In group A, the rate of the MetS occurrence was evaluated, in the subjects who fulfilled <3 criteria at baseline and who fulfilled ≥3 criteria during follow-up, and in group B, the regression rate of the MetS was evaluated in the subjects fulfilling ≥3 criteria at baseline and <3 at the follow-up visit, respectively.
Variables and procedures
The subjects were received in the laboratory of their healthcare unit between 08:00 and 09:00, after at least 10 hours of fasting. After signing the informed consent, a blood sample was obtained to analyze the levels of glucose (hexokinase enzymatic method), total cholesterol (cholesterol enzymatic method), HDL-chol (direct method), low-density lipoprotein cholesterol (Friedewald formula) and TGs (glycerol phosphate oxidase enzymatic method). Capillary blood glucose (One Touch System, LifeScan, Johnson & Johnson, S.A., Madrid) was determined. If the capillary glucose was less than 126 mg/dL (7.8 mmol/L), a sample was obtained 2 hours after 75 g OGTT. A face-to-face interview was then conducted with a trained nurse, structured questionnaires on health status and lifestyle were applied, and a physical examination was performed.
Physical exam: weight without heavy clothing and height, in order to calculate the body mass index [BMI=weight (kg)/height2 (m)], waist circumference (cm), BP with an appropriately sized cuff in a sitting position after 5 min of rest, taking three measurements 2 min apart with the last one being considered.
Sociodemographic variables: the following variables obtained in both phases were considered: age, educational level (categorized as no studies, elementary, secondary education and university degree), socioeconomic status (salaried worker, retired, unemployed, student, domestic work and others), family status (married or cohabiting, single, widowed or separated). Family history for first-degree relatives with T2DM was also collected.
Questionnaires: The questionnaires were applied in a face-to-face interview conducted by a trained nurse. The health questionnaire included the assessment of previous diseases, the family medical history and the pharmacological treatments that subjects were taking in order to determine the presence of clinical diabetes, hypertension or dyslipidemia. The level of physical activity was evaluated by applying the International Physical Activity Questionnaire (IPAQ)15 16 to transform into metabolic equivalent of task (MET) the duration and frequency of low, moderate and vigorous activities and all walking to the activities declared during the previous week. They were classified into one of these three categories: low, moderate or high expenditure, according to a previous study,17 and the total energy expenditure of all physical activities was also calculated as MET-min/week of physical activity. The adapted 14-point MedDiet adherence screener (MEDAS) questionnaire18 was applied to estimate adherence to the Mediterranean diet, after applying a qualitative food frequency questionnaire of 50 foods items. The cut-off point of 9 was considered to reflect high or low adherence to the Mediterranean diet.
Statistical study
Qualitative variables were summarized by their frequency distribution as well as quantitative variables by their mean±SD). The continuous non-normally distributed variables were summarized by the median and IQR: (P25–P75). Differences in variables according to the presence or absence of MetS were determined by the t-test for independent samples or the Mann-Whitney test when appropriate for quantitative variables and χ2 test for qualitative variables. The sample incidence rates (IRs) for MetS or regression were calculated as number of events/person-time at risk, assuming a constant incidence over time. The IR was calculated adjusted for sex and age by direct method using as reference the Spanish population (https://www.ine.es/, accessed June 2009).
Univariate analysis for the incidence or regression of MetS was performed using logistical regression. For IRs and ORs obtained in logistic regression, 95% CIs were computed. A multivariate logistic regression model was fitted to identify the factors that were independently related to the incidence or regression of MetS. The variables for the adjustment were those which, in the univariate analyses, showed a level of statistical significance of p<0.05 and/or were considered clinically relevant. A first model (model 1) was adjusted by introducing the following variables: age (categorized as 18–45, 46–60, 61–75 and ≥76), gender, educational level (categorized as university degree or no university degree), marital status (categorized as married/cohabiting or others) and number of components of MetS. A second model was adjusted by adding the basic healthcare units to the variables of the first model (model 2). All analyses were performed using SPSS V.21.0 and STATA V.15.0 software. Statistical significance was assumed p<0.05.
Results
Over a median follow-up of 7.5 (IQR 7.2–7.9) years, MetS developed in 294 (25.7%) subjects in group A. With 8646 person-years, the adjusted incidence (95% CI) per 1000 person-years was 38 (32-44), affecting more in men, increasing with age, in those with the lowest educational level and with the presence of 1 or 2 components of the MetS at baseline. The OR increases in overweight people 4.48 (95% CI 3.12 to 6.43), obesity 9.53 (95% CI 6.25 to 14.55) and with central fat distribution 4.16 (95% CI 3.12 to 5.44). The mean differences between the non-progressors compared with the progressors in the adapted MEDAS (9.2±1.4 vs 9.2±1.4, –0.0 (–0.2 to 0.2, p=0.519) and the median (IQR) MET-min/week of physical activity (1074 (396–3021) vs 1048 (394–3360), p=0.875) were not statistically significantly different. The probability of progression per 1000 person-years was observed by an adapted MEDAS score of >11 (31 (16–55) vs 34 (30–38)) in those with ≤11, but not by 9. Similarly, the probability decreases slightly as physical activity increases, and this trend is particularly observed in those who spent more than 500 MET-min/week (31 (25–39) vs 36 (29–43)) in those with ≤500 MET-min/week, respectively. These results are presented in table 2.
Table 2.
Incidence of MetS in group A according to characteristics at baseline
| At risk (n) | Developing MetS (n) | P value | OR (95% CI) | Person-year | Incidence per 1000 person-years (95% CI) | |
| All samples | 1146 | 294 (25.7) | 8646 | 38 (32 to 44) | ||
| Gender | ||||||
| Men | 419 | 153 (36.5) | 2.39 (1.82 to 3.13) | 3166 | 48 (41 to 57) | |
| Women | 727 | 141 (19.4) | 0.000 | 1 | 5480 | 26 (22 to 30) |
| Age (years) | ||||||
| 18–45 | 592 | 85 (14.4) | 1 | 4452 | 19 (15 to 24) | |
| 46–60 | 361 | 114 (31.6) | 2.75 (2.00 to 3.79) | 2730 | 42 (35 to 50) | |
| 61–75 | 172 | 83 (48.3) | 5.56 (3.82 to 8.11) | 1305 | 64 (51 to 79) | |
| ≥76 | 21 | 12 (57.1) | 0.000 | 7.95 (3.25 to 19.45) | 160 | 75 (39 to 131) |
| Marital status | ||||||
| Single | 218 | 39 (17.9) | 1 | 1650 | 24 (17 to 32) | |
| Married/cohabiting | 836 | 235 (28.1) | 1.80 (1.23 to 2.62) | 6292 | 37 (33 to 42) | |
| Widower | 43 | 10 (23.3) | 1.39 (0.63 to 3.06) | 327 | 31 (15 to 56) | |
| Divorced | 49 | 10 (20.4) | 0.016 | 1.18 (0.54 to 2.56) | 378 | 27 (13 to 49) |
| Educational level | ||||||
| No studies | 62 | 34 (38.7) | 3.70 (1.98 to 6.91) | 471 | 51 (33 to 76) | |
| Elementary | 527 | 160 (30.4) | 2.55 (1.71 to 3.82) | 3973 | 40 (34 to 47) | |
| Secondary education | 316 | 75 (23.7) | 1.82 (1.17 to 2.84) | 2380 | 32 (25 to 40) | |
| University degree | 240 | 35 (14.8) | 0.000 | 1 | 1814 | 19 (13 to 27) |
| Region of the country | ||||||
| North | 135 | 26 (19.3) | 1 | 1040 | 25 (16 to 37) | |
| South | 447 | 122 (27.3) | 1.57 (0.98 to 2.53) | 3341 | 37 (30 to 44) | |
| Centre | 291 | 85 (29.2) | 1.73 (1.05 to 2.84) | 2221 | 38 (31 to 47) | |
| Northeast | 117 | 26 (22.2) | 1.20 (0.65 to 2.21) | 916 | 28 (19 to 42) | |
| East coast | 156 | 35 (22.4) | 0.135 | 1.21 (0.69 to 2.14) | 1127 | 31 (22 to 43) |
| Number of components of MetS | ||||||
| 0 | 294 | 16 (5.4) | 1 | 2219 | 7 (4 to 11) | |
| 1 | 401 | 72 (18.0) | 3.80 (2.16 to 6.69) | 3023 | 24 (19 to 30) | |
| 2 | 451 | 206 (45.7) | 0.000 | 14.61 (8.54 to 24.99) | 3404 | 61 (53 to 69) |
| Glucose regulation | ||||||
| Normal | 885 | 214 (24.2) | 1 | 6675 | 32 (28 to 37) | |
| >99 | 261 | 80 (30.7) | 0.023 | 1.39 (1.02 to 1.88) | 1971 | 41 (32 to 51) |
| HDL-chol low | ||||||
| No | 968 | 245 (25.3) | 1 | 7303 | 34 (30 to 38) | |
| Yes | 178 | 49 (27.5) | 0.296 | 1.21 (0.78 to 1.61) | 1343 | 37 (27 to 48) |
| Central obesity | ||||||
| No | 750 | 120 (16.0) | 1 | 5684 | 21 (18 to 25) | |
| Yes | 396 | 174 (43.9) | 0.000 | 4.16 (3.12 to 5.44) | 2962 | 59 (50 to 68) |
| Hypertension | ||||||
| No | 736 | 141 (19.2) | 1 | 5531 | 26 (22 to 30) | |
| Yes | 410 | 153 (37.3) | 0.000 | 2.51 (1.92 to 3.30) | 3115 | 49 (42 to 58) |
| Hyper-TG | ||||||
| No | 1088 | 266 (24.4) | 1 | 8206 | 32 (29 to 37) | |
| Yes | 58 | 28 (48.3) | 0.000 | 2.89 (1.69 to 4.92) | 440 | 64 (42 to 92) |
| BMI (kg/m2) | ||||||
| 18–<25 | 474 | 45 (9.5) | 0.000 | 1 | 3582 | 13 (9 to 17) |
| 25–<30 | 488 | 156 (32.0) | 4.48 (3.12 to 6.43) | 3683 | 42 (36 to 50) | |
| >30 | 182 | 91 (50.0) | 9.53 (6.25 to 14.55) | 1366 | 67 (54 to 82) | |
| IPAQ | ||||||
| High | 278 | 69 (24.8) | 0.933 | 1 | 2103 | 33 (26 to 42) |
| Moderate | 387 | 99 (25.6) | 1.04 (0.73 to 1.49) | 2921 | 34 (28 to 41) | |
| Low | 480 | 125 (26.0) | 1.07 (0.76 to 1.50) | 3614 | 35 (29 to 41) | |
| MET-min/week | ||||||
| >500 | 777 | 194 (25.0) | 0.241 | 1 | 3320 | 31 (25 to 39) |
| ≤500 | 369 | 100 (27.1) | 1.12 (0.84 to 1.48) | 2773 | 36 (29 to 43) | |
| MEDAS | ||||||
| <9 | 345 | 85 (24.6) | 0.330 | 1 | 2587 | 33 (26 to 41) |
| >9 | 801 | 209 (26.1) | 1.08 (0.81 to 1.45) | 6059 | 35 (30 to 40) | |
| ≤11 | 1095 | 282 (25.8) | 0.434 | 1.28 (0.58 to 2.18) | 8266 | 34 (30 to 38) |
| >11 | 51 | 12 (23.5) | 1 | 379 | 31 (16 to 55) | |
| Family history of T2DM | ||||||
| No | 528 | 134 (25.4) | 0.425 | 1 | 3944 | 34 (28 to 40) |
| Yes | 595 | 155 (26.1) | 1.04 (0.79 to 1.35) | 4476 | 35 (29 to 41) | |
Data are n (%).
BMI, body mass index; HDL-chol, high-density lipoprotein cholesterol; IPAQ, International Physical Activity Questionnaire; MEDAS, Mediterranean Diet adherence screener; MET, metabolic equivalent of task; MetS, metabolic syndrome; T2DM, type 2 diabetes mellitus; TG, triglyceride.
Regression occurred in 148 (20.1%) patients in group B, who fulfilled the criteria for MetS at baseline. With a follow-up of 5556 person-years, the adjusted regression rate per 1000 person-years was 36 (95% CI 31 to 41). The regression was more frequently observed in women, and especially in subjects fulfilling only three criteria of the MetS at baseline with an OR of 5.98 (2.36–15.15) compared with those with five criteria at baseline (reference group). The ORs in subjects without hypertension for MetS regression was 2.84 (1.75–4.61) and 1.77 (1.22–2.57) for those without hypertriglyceridemia, respectively. The likelihood of MetS regression decreases as body weight increases, with an ORs of 0.23 (0.11–0.49) in overweight subjects at baseline and 0.12 (0.06–0.26) in obese.
There were no differences in the adapted MEDAS score and in the physical activity obtained by the MET-min/week in the group of regressors compared with the non-repressors of MetS. The comparison of the mean score in the adapted MEDAS questionnaire in the regressors compared with the non-regressors was 8.9±1.3 vs 9.2±1.4, −0.3 (−0.6 to 0.1), p=0.543. Similarly, the comparison of the median between regressors and non-regressors in MET-min/week expenditure of physical activity was 876 (IQR 231–2772) vs 693 (IQR 231–2544), p=0.387. The regression rate per 1000 person-years of the MetS was greater in people who achieved an adapted MEDAS score of >11 (34 (11–78) vs (26 (22–31)) in those with ≤11 but not with 9. Similarly, a greater probability of MetS regression was observed when the subjects reached a MET-min/week expenditure of physical activity of >2000 MET-min/week (30 (23–40) vs 23 (17–33)) in those with <2000 MET-min/week, respectively. The data on the MetS regression (group B) are displayed in table 3.
Table 3.
Regression of MetS in group B according to characteristics at baseline
| At risk (n) | Regression to no MetS (n) | P value | OR (95% CI) | Person-year | Incidence per 1000 person-years (95% CI) | |
| All samples | 735 | 148 (20.1) | 5556 | 36 (31 to 41) | ||
| Gender | ||||||
| Men | 383 | 59 (15.4) | 1 | 2911 | 20 (15 to 26) | |
| Women | 352 | 89 (25.3) | 0.001 | 1.86 (1.29 to 2.68) | 2645 | 34 (27 to 41) |
| Age (years) | ||||||
| 18–45 | 146 | 42 (28.8) | 1.77 (0.76 to 4.12) | 1092 | 39 (28 to 52) | |
| 46–60 | 265 | 51 (19.2) | 1.04 (0.46 to 2.38) | 1998 | 26 (19 to 34) | |
| 61–75 | 281 | 47 (16.7) | 0.88 (0.38 to 2.01) | 2135 | 22 (16 to 29) | |
| >76 | 43 | 8 (18.6) | 0.029 | 1 | 330 | 24 (11 to 48) |
| Marital status | ||||||
| Single | 61 | 17 (27.9) | 1 | 465 | 37 (21 to 59) | |
| Married/cohabiting | 605 | 119 (19.7) | 0.63 (0.35 to 1.15) | 4579 | 26 (22 to 31) | |
| Widower | 46 | 7 (15.2) | 0.47 (0.17 to 1.24) | 342 | 21 (8 to 42) | |
| Divorced | 23 | 5 (21.7) | 0.380 | 0.72 (0.23 to 2.24) | 169 | 30 (10 to 69) |
| Educational level | ||||||
| No studies | 134 | 23 (17.2) | 1 | 1013 | 23 (14 to 34) | |
| Elementary | 387 | 76 (19.6) | 1.18 (0.71 to 1.97) | 2931 | 26 (20 to 32) | |
| Secondary education | 143 | 33 (23.1) | 1.45 (0.80 to 2.62) | 1072 | 31 (21 to 43) | |
| University degree | 71 | 16 (22.5) | 0.611 | 1.40 (0.69 to 2.87) | 540 | 30 (17 to 48) |
| Region of the country | ||||||
| North | 68 | 19 (27.9) | 1 | 531 | 36 (22 to 56) | |
| South | 285 | 57 (20.0) | 0.65 (0.35 to 1.18) | 2100 | 27 (21 to 35) | |
| Center | 205 | 32 (15.6) | 0.48 (0.25 to 0.91) | 1589 | 20 (14 to 28) | |
| Northeast | 83 | 19 (22.9) | 0.77 (0.37 to 1.60) | 653 | 29 (18 to 45) | |
| East coast | 94 | 21 (22.3) | 0.209 | 0.74 (0.36 to 1.52) | 684 | 31 (19 to 47) |
| Number of components of MetS | ||||||
| 3 | 408 | 111 (27.2) | 5.98 (2.36 to 15.15) | 3086 | 36 (30 to 43) | |
| 4 | 242 | 32 (13.2) | 2.44 (0.92 to 6.48) | 1835 | 17 (12 to 25) | |
| 5 | 85 | 5 (5.9) | 0.000 | 1 | 635 | 8 (3 to 18) |
| Glucose regulation | ||||||
| Normal | 145 | 37 (25.5) | 1.48 (0.97 to 2.27) | 1095 | 34 (24 to 47) | |
| >99 | 580 | 111 (18.8) | 0.048 | 1 | 4461 | 25 (21 to 30) |
| HDL-chol, low | ||||||
| No | 386 | 74 (19.2) | 0.88 (0.62 to 1.26) | 2924 | 25 (20 to 32) | |
| Yes | 349 | 74 (21.2) | 0.276 | 1 | 2632 | 28 (22 to 35) |
| Central obesity | ||||||
| No | 58 | 17 (29.3) | 1.73 (0.95 to 3.14) | 436 | 39 (23 to 62) | |
| Yes | 677 | 131 (19.4) | 0.054 | 1 | 5120 | 26 (21 to 30) |
| Hypertension | ||||||
| No | 84 | 32 (38.1) | 2.84 (1.75 to 4.61) | 627 | 51 (35 to 72) | |
| Yes | 651 | 116 (17.8) | 0.000 | 1 | 4929 | 24 (20 to 28) |
| Hyper-TG | ||||||
| No | 385 | 94 (24.4) | 1.77 (1.22 to 2.57) | 2926 | 32 (26 to 39) | |
| Yes | 350 | 54 (15.4) | 0.002 | 1 | 2630 | 21 (15 to 27) |
| BMI (kg/m2) | ||||||
| 18–<25 | 33 | 19 (57.6) | 0.000 | 1 | 251 | 76 (46 to 118) |
| 25–<30 | 309 | 74 (23.9) | 0.23 (0.11 to 0.49) | 2342 | 32 (25 to 40) | |
| ≥30 | 389 | 55 (14.1) | 0.12 (0.06 to 0.26) | 2934 | 19 (14 to 24) | |
| IPAQ | ||||||
| High | 118 | 28 (23.7) | 0.534 | 1 | 893 | 31 (21 to 45) |
| Moderate | 248 | 49 (19.8) | 0.79 (0.47 to 1.34) | 1887 | 26 (20 to 34) | |
| Low | 368 | 70 (19.0) | 0.76 (0.46 to 1.24) | 2768 | 25 (20 to 32) | |
| MET-min/week | ||||||
| >2000 | 222 | 51 (23.0) | 0.113 | 1.29 (0.88 to 1.89) | 1680 | 30 (23 to 40) |
| <2000 | 512 | 96 (18.8) | 1 | 3868 | 24 (20 to 30) | |
| MEDAS | ||||||
| <9 | 235 | 59 (25.1) | 0.015 | 1 | 1777 | 33 (25 to 43) |
| ≥9 | 500 | 89 (17.8) | 0.65 (0.45 to 0.94) | 3779 | 24 (19 to 29) | |
| >11 | 20 | 5 (25) | 0.376 | 1.33 (0.48 to 3.73) | 149 | 34 (11 to 78) |
| ≤11 | 715 | 143 (20) | 1 | 5406 | 26 (22 to 31) | |
| Family history of T2DM | ||||||
| No | 302 | 64 (21.2) | 0.251 | 1 | 2303 | 28 (21 to 36) |
| Yes | 408 | 77 (18.9) | 0.87 (0.60 to 1.25) | 3061 | 25 (20 to 31) | |
Data are n (%).
BMI, body mass index; HDL-chol, high-density lipoprotein cholesterol; IPAQ, International Physical Activity Questionnaire; MEDAS, MedDiet adherence screener; MET, metabolic equivalent of task; MetS, metabolic syndrome; T2DM, type 2 diabetes mellitus; TG, triglyceride.
Multivariate analysis adjusted by significant variables in univariate analysis (model 1) and adding basic healthcare units (model 2) showed no effect in the association factors with the progression/regression of MetS (online supplemental table 1).
bmjdrc-2020-001715supp001.pdf (93.5KB, pdf)
The MetS regression rate was significantly higher than the MetS incidence in women, university-level subjects, subjects with no central fat distribution, overweight or obesity, and without hypertension (all p<0.05).
Discussion
According to the data obtained in our study, the incidence of MetS in a representative sample of the Spanish population is 38 cases per 1000 person-years but, on the contrary, regresses in 36 cases per 1000 person-years. This means that the MetS rate increases 2 cases per 1000 person-years in excess in relation to the regression rate, which for a population of 47 million people represents an increase in approximately of 94 000 cases per year, with about 257 more patients diagnosed with MetS daily.
This study has provided important information to identify the characteristics of progressors to MetS. The appearance of the MetS is significantly higher in men, in the age group over 45 years and in those who do not reach a university level, as reported in the baseline study. Those with at least one component of the MetS are four times more likely to develop it and if they have two components/criteria at baseline, about 15 times higher compared with those fulfilling no criteria at baseline. These results are in agreement to those reported in other populations,19–22 probably indicating a slow progression in the occurrence of MetS. Although the presence of any component increases the probability of presenting MetS, the most determining factors are waist circumference, which multiplies the risk by 4, while being overweight or obese multiplies by more than nine times the probability of developing the MetS. The implications of these findings are that programs aimed to reduce body weight are essential to reduce the incidence of the MetS in Spain. Unexpectedly, a correct adherence to the Mediterranean diet evaluated by MEDAS is not associated with a reduction in the occurrence of MetS, if we define 9 as the cut-off point referred to identify adherents to Mediterranean diet (Med Diet), as identified in the PREDIMED study in the prevention of T2DM.18 23 However, PREDIMED study also found no association with the occurrence or regression of the MetS.24
Using other questionnaires as tools to assess the adherence to the Med Diet and in a Spanish population with a high educational level, such as alumni from the University of Navarra in the SUN (Seguimiento Universidad de Navarra) study, a significant reduction in MetS occurrence was found associated with a higher level of adherence to Med Diet.21 In our study, using other cut points, such as 11, we found that there is a trend to a decreased rate of occurrence, indicating that to reduce the occurrence of MetS, it is likely to be necessary to achieve a higher degree of adherence to Med Diet. On the other hand, we must consider that the MEDAS median in our cohort is nine and that the score greater than 7 is exceeded by 98% of the cohort included in the study. Thus, we can consider that in a population with a high degree of adherence to Med Diet, it may be necessary to achieve a higher adherence to Med Diet to observe benefits.25 26 A larger sample size may also be necessary to observe a more favorable effect.
Regarding physical activity, there is a slight linear reduction in the appearance of MetS with moderate and high intensity; it begins to be favorable from being moderately active, that is, from 500 MET-min/week of physical activity. Physical activity has substantial beneficial effects on MetS.27 There is agreement that being active, even at a low level, can be sufficient for the prevention of MetS, even if more benefits are obtained at a higher level of attained activity.27–30 The population included in the di@bet.es study cohort at baseline was not very active, and the subgroup with lower activity was associated with an increase in the rate of some components of the MetS.17 Our study is only observational, based on the two national strategies3 4 and whose main recommendation is to be moderately active, such as walking daily and climbing stairs, which has been associated with an improvement in insulin sensitivity.
Our study identifies the characteristics that the population has at baseline with a greater probability of MetS regression. Those are being women, presenting fewer numbers of MetS criteria at baseline, higher educational level and young age, while the absence of hypertension, hypertriglyceridemia and overweight/obesity identifies the population that is most susceptible to population interventions and in whom MetS is more likely to regress.
An important finding of our study is that it is necessary to achieve a higher degree of adherence to Med Diet, estimated at 11 points, in the same way as to reduce the occurrence of MetS. In relation to physical activity, it is also necessary to reach a minimum level of ≥2000 MET-min/week of physical activity to observe a tendency to regression. This is in agreement with results obtained in other studies and are important treatment objectives in patients with MetS.31–34
Limitations
The participants in the di@bet.es study cohort were randomly selected from the population covered by National Health System, divided in five different basic healthcare units, which included 100 clusters (primary healthcare centers) throughout the national territory, and the interventions carried out in each center have not specifically been evaluated. However, the monitoring of this cohort represents in real time the modifications that have arisen with population strategies and the response of the public health system. Differences due to the area of origin of the surveyed subjects have been analyzed; the studied population is representative of the Spanish population, but it is not enough to achieve representativeness of each specific area, of the five in which Spain was divided. This means that the results by zones must be taken with caution. There could also be an underdiagnosis in the declared interpretation of the subjects in relation to the pharmacological treatment of some components of the MetS, and this cannot be ruled out. The assessment of diet and physical activity has been carried out by the IPAQ and MEDAS questionnaires, which may have some difficulty in their application. However, they were obtained both at baseline and during follow-up by the same nurses trained to carry out the surveys, so data collection is subject to less variability. The response rate during follow-up can also affect our results, but it has been more than 78% of those eligible, an important rate in this type of study, although the influence of some confounding factors cannot be ruled out.
Conclusions
Our data show that despite the designed population strategies, the rate of MetS occurrence in Spain exceeds that of regression, increasing the existence of MetS by 257 people each day. The high prevalence of MetS makes it mandatory to implement national policies for its prevention. Preventive strategies to reduce the occurrence of MetS should focus on the population older than 45 years and with one or two components of MetS, especially in overweight/obese people and those with central body fat distribution. The strategies to achieve a MetS regression are more likely to be successful in the population that has fewer MetS components, and in particular those who do not have hypertension, hypertriglyceridemia or a normal weight with peripheral fat distribution. It is necessary to achieve a greater adherence to Med Diet (>11 points) and at least 2000 MET-min/week of physical activity to observe a trend to the MetS regression, while >500 MET-min/week of physical activity may be sufficient to observe a reduction on its occurrence. These data must be considered by the public health system in Spain and may be of interest to apply them in other countries. It will also be useful for comparing data obtained from patients with other chronic diseases and across countries. Researcher should further investigate how the lifestyle can be optimized for health promotion and prevention of cardiometabolic conditions like MetS.
Acknowledgments
Our most sincere thanks to Isabel Ramis (Spanish Biomedical Research Network in Diabetes and Associated Metabolic Disorders (CIBERDEM), Barcelona. Spain) for her help at all times in the organization of the study. Our profound appreciation goes to the primary care managers and personnel of the participating health centers, and very especially to L Forga (Complejo Hospitalario de Navarra, Pamplona Spain), F Casanueva (Complejo Hospitalario Universitario de Santiago, Santiago de Compostela, Spain), JJ Alemán (Dirección General de Salud Pública del Servicio Canario de la Salud, Palmas de Gran Canaria, Spain), J Marzo (Dirección General de Asistencia Sanitaria, Zaragoza, Spain), JM Millaruelo (Centro de Salud Torrero-La Paz, Zaragoza, Spain), J García-Rotllán (Dirección General de Asistencia Sanitaria y Resultados en Salud, Sevilla, Spain), MA Ayuso (Ayuntamiento de Villamanrrique de Tajo, Madrid, Spain), MB Ballesteros (Hospital General Universitario de León, Spain), L Jolín (Dirección de Atención Primaria, Palencia), M Moreiras (Hospital Universitario de Palencia, Spain), J Parra (Hospital de Mérida, Spain), C Jiménez (Dirección de Atención Primaria, Cáceres, Spain), and MJ Gamero (Dirección de Atención Primaria, Badajoz, Spain). To all the fieldworkers, nurses, and technicians, without whose work the study would not have been possible. We are indebted to the technical staff of the UGC of Laboratories of the Hospital Regional Universitario of Málaga, for their invaluable technical assistance. We are very grateful to Patricia González (Biomedical Research Institute of Malaga, Spain), for help with the databases, and to all the people who voluntarily participated in the study. We are indebted to the Biobank of the Hospital Regional Universitario of Malaga-IBIMA and CIBERDEM Biorepository (IDIBAPS Biobank), integrated in the Spanish National Biobank Network, for the samples management.
Footnotes
MC and MF contributed equally.
Contributors: Conception and design: GR-M, SV, FS, JG, SG, JF-N, EDA, FJC, LC, AG and ALC-P. Acquisition of data: JJV, IMU, EO, EMo, EMe, AL-S, RG, AG, EB, MF, MC, CC, RB-G, AB and NGdlT. Analysis and interpretation of data: MF, MC, GR-M, SV, FS and ALC-P. Drafting the article: MC, MF, MR and ALC-P. Review for important intellectual content: JJV, IMU, EO, EMo, EMe, AL-S, RG, AG, MR, AB, EB, NGdlT, CC, RB-G, JG, SG, JF-N, EDA, FJC, LC and ALC-P. These authors jointly supervised this work: GR-M, SV, JG, JF-N, EDA, FJC, LC, MR and ALC-P. ALC-P is the guarantor of this work. All authors have reviewed and approved the final version of the manuscript.
Funding: This study was supported by CIBERDEM (Ministerio de Economía, Industria y Competitividad-ISCIII), Ministerio de Sanidad, Servicios Sociales e Igualdad-ISCIII, Instituto de Salud Carlos III. (PI14/00710, PI14/01104, PI14/00970, PI14/00874, PIE14/00031). LifeScan España (Madrid, Spain) kindly donated the glucometers and test strips for capillary glucose measurements.
Competing interests: EMo has been on advisory boards, has received consulting fees, speaker honoraria or research support from Astra Zeneca, Menarini Laboratories, Merck Sharp & Dohme, Novartis, NovoNordisk and Sanofi.
Patient consent for publication: Not required.
Ethics approval: The study was approved by the Ethical Committee of the Hospital Regional Universitario de Malaga (27/11/2014) y la Comisión Central de Investigación de Atención Primaria (acta 03/17) and was conducted according to the recommendations of the Declaration of Helsinki. All participants were informed of the characteristics of the study and signed a written informed consent.
Provenance and peer review: Not commissioned; externally peer reviewed.
Data availability statement: Data are available upon reasonable request. All data relevant to the study are included in the article or uploaded as supplemental information. The datasets analyzed during the current study are available from the corresponding author on reasonable request.
References
- 1.Alberti KGMM, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International diabetes Federation Task force on epidemiology and prevention; National heart, lung, and blood Institute; American heart association; world heart Federation; international atherosclerosis Society; and international association for the study of obesity. Circulation 2009;120:1640–5. 10.1161/CIRCULATIONAHA.109.192644 [DOI] [PubMed] [Google Scholar]
- 2.Marcuello C, Calle-Pascual AL, Fuentes M, et al. Prevalence of the metabolic syndrome in Spain using regional cutoff points for waist circumference: the di@bet.es study. Acta Diabetol 2013;50:615–23. 10.1007/s00592-013-0468-8 [DOI] [PubMed] [Google Scholar]
- 3.Ministerio de Sanidad, Consumo Y Bienestar social Estrategia en diabetes del Sistema Nacional de Salud, 2020. Available: www.mscbs.gob.es
- 4.Ministerio de Sanidad, Consumo Y Bienestar social Estrategia NAOS, 2020. Available: http://www.aecosan.msssi.gob.es/AECOSAN/docs/documentos/nutricion/estrategianaos
- 5.Perreault L, Kahn SE, Christophi CA, et al. Regression from pre-diabetes to normal glucose regulation in the diabetes prevention program. Diabetes Care 2009;32:1583–8. 10.2337/dc09-0523 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Herman WH, Pan Q, Edelstein SL, et al. Impact of lifestyle and metformin interventions on the risk of progression to diabetes and regression to normal glucose regulation in overweight or obese people with impaired glucose regulation. Diabetes Care 2017;40:1668–77. 10.2337/dc17-1116 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Perreault L, Pan Q, Schroeder EB, et al. Regression from prediabetes to normal glucose regulation and prevalence of microvascular disease in the diabetes prevention program outcomes study (DPPOS). Diabetes Care 2019;42:1809–15. 10.2337/dc19-0244 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.American Diabetes Association 8. Obesity Management for the Treatment of Type 2 Diabetes: Standards of Medical Care in Diabetes-2020. Diabetes Care 2020;43:S89–97. 10.2337/dc20-S008 [DOI] [PubMed] [Google Scholar]
- 9.Sheng B, Truong K, Spitler H, et al. The long-term effects of bariatric surgery on type 2 diabetes remission, microvascular and macrovascular complications, and mortality: a systematic review and meta-analysis. Obes Surg 2017;27:2724–32. 10.1007/s11695-017-2866-4 [DOI] [PubMed] [Google Scholar]
- 10.Fisher DP, Johnson E, Haneuse S, et al. Association between bariatric surgery and macrovascular disease outcomes in patients with type 2 diabetes and severe obesity. JAMA 2018;320:1570–82. 10.1001/jama.2018.14619 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ramos-Levi AM, Sanchez-Pernaute A, Cabrerizo L, et al. Remission of type 2 diabetes mellitus should not be the foremost goal after bariatric surgery. Obes Surg 2013;23:2020–5. 10.1007/s11695-013-1032-x [DOI] [PubMed] [Google Scholar]
- 12.Soriguer F, Goday A, Bosch-Comas A, et al. Prevalence of diabetes mellitus and impaired glucose regulation in Spain: the Di@bet.es Study. Diabetologia 2012;55:88–93. 10.1007/s00125-011-2336-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Rojo-Martínez G, Valdés S, Soriguer F, et al. Incidence of diabetes mellitus in Spain as results of the nation-wide cohort di@bet.es study. Sci Rep 2020;10:2765. 10.1038/s41598-020-59643-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Martínez-Larrad MT, Fernández-Pérez C, Corbatón-Anchuelo A, et al. Revised waist circumference cut-off points for the criteria of abdominal obesity in the Spanish population: multicenter nationwide Spanish population based study. Avances en Diabetología 2011;27:168–74. 10.1016/j.avdiab.2011.09.003 [DOI] [Google Scholar]
- 15.Craig CL, Marshall AL, Sjöström M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003;35:1381–95. 10.1249/01.MSS.0000078924.61453.FB [DOI] [PubMed] [Google Scholar]
- 16.International physical activity questionnaire (IPAQ). Available: https://sites.google.com/site/theipaq/ [Accessed 8 Mar 2020].
- 17.Brugnara L, Murillo S, Novials A, et al. Low physical activity and its association with diabetes and other cardiovascular risk factors: a nationwide, population-based study. PLoS One 2016;11:e0160959. 10.1371/journal.pone.0160959 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Schröder H, Fitó M, Estruch R, et al. A short screener is valid for assessing Mediterranean diet adherence among older Spanish men and women. J Nutr 2011;141:1140–5. 10.3945/jn.110.135566 [DOI] [PubMed] [Google Scholar]
- 19.Panagiotakos DB, Pitsavos C, Arvaniti F, et al. Adherence to the Mediterranean food pattern predicts the prevalence of hypertension, hypercholesterolemia, diabetes and obesity, among healthy adults; the accuracy of the MedDietScore. Prev Med 2007;44:335–40. 10.1016/j.ypmed.2006.12.009 [DOI] [PubMed] [Google Scholar]
- 20.Kastorini C-M, Milionis HJ, Esposito K, et al. The effect of Mediterranean diet on metabolic syndrome and its components: a meta-analysis of 50 studies and 534,906 individuals. J Am Coll Cardiol 2011;57:1299–313. 10.1016/j.jacc.2010.09.073 [DOI] [PubMed] [Google Scholar]
- 21.Tortosa A, Bes-Rastrollo M, Sanchez-Villegas A, et al. Mediterranean diet inversely associated with the incidence of metabolic syndrome: the sun prospective cohort. Diabetes Care 2007;30:2957–9. 10.2337/dc07-1231 [DOI] [PubMed] [Google Scholar]
- 22.Saklayen MG. The global epidemic of the metabolic syndrome. Curr Hypertens Rep 2018;20:12. 10.1007/s11906-018-0812-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Salas-Salvadó J, Bulló M, Babio N, et al. Reduction in the incidence of type 2 diabetes with the Mediterranean diet: results of the PREDIMED-Reus nutrition intervention randomized trial. Diabetes Care 2011;34:14–19. 10.2337/dc10-1288 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Babio N, Toledo E, Estruch R, et al. Mediterranean diets and metabolic syndrome status in the PREDIMED randomized trial. CMAJ 2014;186:E649–57. 10.1503/cmaj.140764 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Kouvari M, Panagiotakos DB, Naumovski N, et al. Dietary anti-inflammatory index, metabolic syndrome and transition in metabolic status; a gender-specific analysis of Attica prospective study. Diabetes Res Clin Pract 2020;161:108031. 10.1016/j.diabres.2020.108031 [DOI] [PubMed] [Google Scholar]
- 26.Kouvari M, Panagiotakos DB, Yannakoulia M, et al. Transition from metabolically benign to metabolically unhealthy obesity and 10-year cardiovascular disease incidence: the Attica cohort study. Metabolism 2019;93:18–24. 10.1016/j.metabol.2019.01.003 [DOI] [PubMed] [Google Scholar]
- 27.Pérez-Martínez P, Mikhailidis DP, Athyros VG, et al. Lifestyle recommendations for the prevention and management of metabolic syndrome: an international panel recommendation. Nutr Rev 2017;75:307–26. 10.1093/nutrit/nux014 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Misra A, Alappan NK, Vikram NK, et al. Effect of supervised progressive resistance-exercise training protocol on insulin sensitivity, glycemia, lipids, and body composition in Asian Indians with type 2 diabetes. Diabetes Care 2008;31:1282–7. 10.2337/dc07-2316 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Ilanne-Parikka P, Laaksonen DE, Eriksson JG, et al. Leisure-Time physical activity and the metabolic syndrome in the Finnish diabetes prevention study. Diabetes Care 2010;33:1610–7. 10.2337/dc09-2155 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Cuesta Hernández M, Calle Pascual AL. [Benefits of exercise in healthy population and impact on disease occurrence]. Endocrinol Nutr 2013;60:283–6. 10.1016/j.endoen.2013.03.002 [DOI] [PubMed] [Google Scholar]
- 31.He D, Xi B, Xue J, et al. Association between leisure time physical activity and metabolic syndrome: a meta-analysis of prospective cohort studies. Endocrine 2014;46:231–40. 10.1007/s12020-013-0110-0 [DOI] [PubMed] [Google Scholar]
- 32.Church T. Exercise in obesity, metabolic syndrome, and diabetes. Prog Cardiovasc Dis 2011;53:412–8. 10.1016/j.pcad.2011.03.013 [DOI] [PubMed] [Google Scholar]
- 33.Ekelund U, Ward HA, Norat T, et al. Physical activity and all-cause mortality across levels of overall and abdominal adiposity in European men and women: the European prospective investigation into cancer and nutrition study (EPIC). Am J Clin Nutr 2015;101:613–21. 10.3945/ajcn.114.100065 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Bassi N, Karagodin I, Wang S, et al. Lifestyle modification for metabolic syndrome: a systematic review. Am J Med 2014;127:1242.e1–1242.e10. 10.1016/j.amjmed.2014.06.035 [DOI] [PubMed] [Google Scholar]
Associated Data
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Supplementary Materials
bmjdrc-2020-001715supp001.pdf (93.5KB, pdf)