- Email: [email protected]
Lifestyle of metabolically healthy obese individuals
p r i m a r y c a r e d i a b e t e s 9 ( 2 0 1 5 ) 179–183
Contents lists available at ScienceDirect
Primary Care Diabetes journal homepage: http://www.elsevier.com/locate/pcd
Original research
Lifestyle of metabolically healthy obese individuals Päivi E. Korhonen a,b,c,∗ , Pirkko Korsoff a , Tero Vahlberg d , Risto Kaaja a,e a
Satakunta Hospital District, 28100 Pori, Finland Central Satakunta Health Federation of Municipalities, 29200 Harjavalta, Finland c Institute of Clinical Medicine, Family Medicine, University of Turku, 20520 Turku, Finland d University of Turku, 20520 Turku, Finland e Institute of Clinical Medicine, Internal Medicine, University of Turku, 20520 Turku, Finland b
a r t i c l e
i n f o
a b s t r a c t
Article history:
Aims: The aim of this study is to find factors associated with metabolic syndrome in obese
Received 30 May 2014
individuals and thus offer guidance to stay metabolically healthy if obese.
Received in revised form
Methods: A cardiovascular screening programme performed in Finland during the years
14 September 2014
2005–2007, identified 901 obese white individuals. Of them, 269 (30%) were metabolically
Accepted 22 September 2014
healthy according to the Harmonization criteria of metabolic syndrome.
Available online 18 October 2014
Results: In multivariate logistic regression analysis, male sex [odds ratio (OR) 1.44 (95% CI 1.01–2.07)], living alone [OR 1.77 (95% CI 1.18–2.65)], physical inactivity [OR 3.73 (95%
Keywords:
CI 1.24–11.24)], and use of betablockers [OR 2.63 (95% CI 1.75–3.95)] were associated with
Metabolic syndrome
metabolic syndrome.
Obesity
Conclusions: Even mild or occasional physical exercise is beneficial to health in obese indi-
Physical activity
viduals. Betablockers may not be the antihypertensive agents of choice when treating obese hypertensive individuals. © 2014 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.
1.
Introduction
A subset of obese individuals without cluster of metabolic disturbances, known as “metabolically healthy obese” (MHO), appears to be protected to the development of diabetes or cardiovascular diseases [1,2]. Prevalence of MHO individuals depends on the definition used and for the time being, there is no standardized definition to identify MHO individuals for research protocols or in clinical practice. In Finland, the prevalence of MHO defined as obesity (body mass index ≥ 30 kg/m2 ) without metabolic syndrome (MetS) is estimated to be 2.0%
among men and 4.5% among women aged 45–74 years [3]. Successful prevention of MetS among these people would save substantial consequences from an individual perspective and costs from a societal perspective related to prevented or postponed comorbidities. It is currently not known why MHO individuals appear to be protected to the development of MetS. The present study characterized MHO individuals in a population-based screening programme and compared their lifestyle to that of “metabolically abnormal obese” (MAO) individuals in order to find factors associated with MetS in obese individuals.
∗
Corresponding author at: Jokikatu 3, 29200 Harjavalta, Finland. Tel.: +358 40 7653257; fax: +358 2 6741180. E-mail addresses: paivi.e.korhonen@fimnet.fi (P.E. Korhonen), pirkko.korsoff@satshp.fi (P. Korsoff), tero.vahlberg@utu.fi (T. Vahlberg), risto.kaaja@utu.fi (R. Kaaja). http://dx.doi.org/10.1016/j.pcd.2014.09.006 1751-9918/© 2014 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.
180
p r i m a r y c a r e d i a b e t e s 9 ( 2 0 1 5 ) 179–183
2.
Methods
2.1.
Subjects and measurements
The study participants were drawn from a population survey, the Harmonica Project, which was carried out in the rural towns of Harjavalta and Kokemäki in southwestern Finland from autumn 2005 to autumn 2007. A cardiovascular risk factor survey, tape for the measurement of waist circumference, and type 2 diabetes risk assessment form (Finnish Diabetes Risk Score, FINDRISC, available from www.diabetes.fi/english), were mailed to all home-dwelling inhabitants aged 45–70 years (n = 6013) [4]. Out of the 4421 (74%) respondents, those having at least one cardiovascular risk factor (n = 3072) were invited for an enrolment examination performed by a public health nurse. Risk factors taken into account were waist circumference ≥80 cm in women and ≥94 cm in men, hypertension, history of gestational diabetes or hypertension, family history of premature cardiovascular disease, and ≥15 points in the FINDRISC (≥12 points in Harjavalta). Participation and all the tests included were free of charge for the subjects. Patients with known cardiovascular disease or previously diagnosed diabetes were excluded since they already had systematic follow-up in the health centres. The public health nurses examined 2752 risk study participants. Height and weight were measured with the subjects in standing position without shoes and outer garments. Height was recorded to the nearest 0.5 cm and weight to the nearest 0.1 kg Digital scales (Seca® 861, Germany) were used, and their calibration was monitored regularly. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m2 ). Waist circumference was measured at the level midway between the lower rib margin and the iliac crest. Blood pressure was measured with a calibrated mercury sphygmomanometer with subjects in a sitting posture, after resting at least 5 min Two readings taken at intervals of at least 2 min were measured, and the mean of these readings was used in the analysis.
2.2.
Laboratory tests
Laboratory tests were determined in blood samples which were obtained after at least 12 h of fasting. Oral glucose tolerance test was performed by measuring fasting plasma glucose and 2-h plasma glucose from capillary blood with HemoCue® Glucose 201+ system (Ängelholm, Sweden) after ingestion of a glucose load of 75 g anhydrous glucose dissolved in water. Glucose disorders were classified according to the World Health Organization 2006 criteria [5]. Total cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides were measured enzymatically (Olympus® AU640, Japan). Low-density lipoprotein (LDL) cholesterol was calculated according to the Friedewald’s formula.
2.3.
Definitions
MetS was diagnosed according to the Harmonization definition [6] (Table 1). MAO and MHO phenotypes were defined
Table 1 – Criteria for the 2009 Harmonization definition of metabolic syndrome [9]. The presence of any 3 of 5 risk factors constitutes a diagnosis of metabolic syndrome. Measure
Cut point
Elevated waist circumference Elevated triglycerides Reduced HDL-cholesterol
Females ≥80 cm, males ≥94 cm
Elevated blood pressure
Systolic ≥130 and/or diastolic ≥85 mmHg, or antihypertensive drug treatment in a patient with a history of hypertension ≥5.6 mmol/l, or drug treatment of elevated glucose
Elevated fasting glucose
≥1.7 mmol/l, or fibrate or nicotinic acid medication Females <1.3 mmol/l, males < 1.0 mmol/l, or fibrate or nicotinic acid medication
Abbreviation: HDL, high-density lipoprotein.
as BMI ≥ 30 kg/m2 with or without MetS, respectively. Selfreported leisure-time physical activity was classified into categories: (1) “no” physical activity; (2) “functional” was defined doing regularly minor physical activity, such as light gardening or walking or cycling to work; (3) “occasional” and (4) “regular” exercise, such as walking, jogging, cycling or swimming.
2.4.
Questionnaires
Subjects completed self-administrated questionnaires at the clinic before the physical examination was performed: sociodemographic factors, occupational status, physical activity level, smoking status, Alcohol Use Disorders Identification Test (AUDIT) [7], Beck’s Depression Inventory (BDI) [8], selfrated physical health, time spent for TV watching or reading per day, duration and quality of sleep. AUDIT-score ≥ 8 indicates harmful alcohol use, as well as possible alcohol dependence [7]. Depressive symptoms were regarded present if the BDI-score was ≥10 [9].
2.5.
Informed consent
The study protocol and consent forms were reviewed and approved by the ethics committee of Satakunta hospital district. All participants provided written informed consent for the project and subsequent medical research.
2.6.
Statistical analysis
Data is presented as the means with standard deviations (SD) or as frequencies with percentages. The comparisons between MHO and MAO groups were done by using two sample ttest or chi-square test. Predictors significantly associated with metabolic syndrome in univariate logistic regression analysis were included in multivariate logistic regression model. Results are expressed using odds ratios (OD) with 95% confidence intervals (CI). P-values lower than 0.05 were considered statistically significant. Statistical analysis was made with SAS
181
p r i m a r y c a r e d i a b e t e s 9 ( 2 0 1 5 ) 179–183
Table 2 – Characteristics of the study participants. MAO n = 632
MHO n = 269
P-value
Demographic Number of female (%)
348 (55)
176 (65)
0.0039
Age, years, mean (SD)
59.4 (6.8)
58.4 (6.9)
0.042
Cohabiting, n (%)
448 (73)
211 (81)
0.015
Self-rated physical health, n (%) Good Moderate Poor
125 (20) 145 (21) 338 (56)
69 (27) 78 (30) 112 (43)
0.0039
Education, n (%) Basic Occupational Higher
345 (57) 230 (38) 33 (5)
124 (48) 115 (44) 21 (8)
0.035
Strenuous work physically, n (%) Strenuous Not strenuous
266 (45) 331 (55)
107 (42) 149 (58)
0.46
Strenuous work mentally, n (%) Strenuous Not strenuous
244 (41) 351 (59)
105 (41) 149 (59)
0.93
142 (23)
42 (16)
Current or former smoker, n (%)
289 (48)
114 (45)
0.41
Physical activity, n (%) No Functional Occasional Regular
35 (6) 187 (31) 229 (38) 150 (25)
5 (2) 62 (24) 111 (43) 78 (30)
0.0074
TV watching, h/24 h, mean (SD)
2.4 (1.4)
2.3 (1.2)
0.14
Reading, h/24 h, mean (SD)
1.3 (0.9)
1.3 (0.8)
0.60
Sleeping, h/24 h, mean (SD)
7.1 (1.2)
7.1 (1.2)
0.64
Quality of sleep, n (%) Very good Quite good Quite poor Very poor
111 (19) 359 (60) 111 (19) 16 (3)
37 (14) 175 (68) 38 (15) 6 (2)
0.16
34.4 (4.3) 145 (19) 87 (11) 5.39 (0.98) 3.24 (0.89) 1.33 (0.36) 1.79 (0.80) 6.16 (1.40) 8.62 (2.76) 161 (26) 216 (35) 155 (25) 121 (20) 44 (7)
33.6 (3.9) 138 (18) 85 (9) 5.35 (0.97) 3.24 (0.86) 1.56 (0.33) 1.23 (0.59) 5.29 (0.69) 6.81 (1.55) 61 (23) 40 (16) 38 (15) 36 (14) 17 (7)
Health behaviours AUDIT-score ≥8, n (%)
Clinical Body mass index, kg/m2 , mean (SD) SBP, mmHg, mean (SD) DBP, mmHg, mean (SD) Total cholesterol, mmol/l, mean (SD) LDL-cholesterol, mmol/l, mean (SD) HDL-cholesterol, mmol/l, mean (SD) Triglycerides, mmol/l, mean (SD) Fasting glucose, mmol/l, mean (SD) 2-h glucose, mmol/l, mean (SD) Depressive symptoms, n (%) Users of betablockers, n (%) Users of diuretics, n (%) Users of statins, n (%) Users of antidepressants, n (%)
0.023
0.001 <0.0001 0.0002 0.56 0.99 <0.0001 <0.0001 <0.0001 <0.0001 0.31 <0.001 0.0016 0.074 0.87
Abbreviations: MAO, metabolically abnormal obese; MHO, metabolically healthy obese; AUDIT, Alcohol Use Disorders Identification Test; SBP, systolic blood pressure; DBP, diastolic blood pressure; LDL, low-density lipoprotein; HDL, high-density lipoprotein.
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p r i m a r y c a r e d i a b e t e s 9 ( 2 0 1 5 ) 179–183
Table 3 – Factors associated with the risk for metabolic syndrome in obese individuals. Variables
Odds ratio (95% CI)
Male sex Age Single AUDIT ≥ 8
1.44 (1.01–2.07) 1.01 (0.98–1.03) 1.77 (1.18–2.65) 1.30 (0.83–2.04)
0.047 0.68 0.005 0.26
Education Basic vs. occupational Higher vs. occupational
1.29 (0.91–1.82) 0.76 (0.40–1.46)
0.16 0.41
Physical activity No vs. regular Functional vs. regular Occasional vs. regular
3.73 (1.24–11.24) 1.50 (0.98–2.32) 1.05 (0.71–1.55)
0.019 0.065 0.81
Self-rated health Moderate vs. good Poor vs. good
0.90 (0.58–1.39) 1.17 (0.77–1.77)
0.62 0.46
Use of betablockers
2.63 (1.75–3.95)
Use of diuretics
1.31 (0.85–2.01)
P-value
<0.0001 0.23
Abbreviations: AUDIT, Alcohol Use Disorders Identification Test; CI, confidence interval.
System for Window, version 9.3 (SAS Institute Inc., Cary, NC, USA).
3.
Results
Nine hundred and one obese white individuals (mean age 59 years, range 45–70 years, 58% females) without previously diagnosed cardiovascular disease or diabetes were examined. Of these, 269 (30%) were metabolically healthy. The characteristics of the study participants are shown in Table 2. MHO individuals were more often women and cohabiting, slightly younger, more educated, had less harmful alcohol use, and exercised more regularly than MAO subjects. Cardiometabolic risk factors were significantly lower in MHO than in MAO individuals, with the exception of total and LDLcholesterol which did not differ between the study groups. MAO individuals used more often betablockers and diuretics than MHO subjects. There was no difference between the groups in the prevalence of depressive symptoms or usage of antidepressive drugs. In multivariate logistic regression analysis, male sex [odds ratio (OR) 1.44 (95% CI 1.01–2.07)], living alone [OR 1.77 (95% CI 1.18–2.65)], physical inactivity [OR 3.73 (95% CI 1.24–11.24)], and use of betablockers [OR 2.63 (95% CI 1.75–3.95)] were significantly associated with MetS. (Table 3).
4.
Discussion
In this cohort of obese individuals without established comorbidities, the prevalence of MHO was 30%. The predominance of females in the MHO group is noteworthy, but in concordance with the previous study among Finnish people [3]. In the present study, the international definitions of obesity and MetS were complied, but this approach allows the inclusion of subjects who are only one criterion short of MetS. However, the
2009 Harmonization definition of MetS used has recently been shown to be a significant predictor of future cardiovascular events and diabetes [10]. According to the results of this study, factors associated with MAO phenotype are male sex, bachelorhood, physical inactivity, and use of betablockers. A potential pathway linking marital status to metabolic health is via behavioural factors. Being married [11,12] and having a family [13] have been associated with health promoting behaviours. From a health-care perspective, the factors taken into account are the choice of antihypertensive medication and individual lifestyle counselling. Obese individuals have been shown to have a 3.5-fold increase in the likelihood of hypertension [14]. Thus, many obese individuals without MetS may need medical therapy for hypertension, and the selection of an antihypertensive drug for them is an important issue in clinical practice. Betablockers and diuretics may increase the likelihood of diabetes and hence the likelihood of prediabetes as well [15,16]. Noteworthy is also that depressive symptoms or usage of antidepressive medication did not differ between the MHO and MAO individuals in our study. Thus, depressive mood does not explain observed differences in physical activity levels. Physical exercise has been widely recommended for the treatment of obesity and for the prevention of type 2 diabetes. In our obese study participants, physical inactivity increased the odds of MetS almost 4-fold compared to individuals who exercised regularly. Functional activity was surprisingly quite beneficial to metabolic health, although this association did not reach statistical significance with comparison to regular physical activity. In the Nurses’ Health Study, obesity and physical inactivity independently contributed to the development of type 2 diabetes, but the association of obesity appeared to be much greater than that of physical inactivity [17]. Moreover, also occasional physical activity may have a positive effect on metabolic risk factors. In type 2 diabetics, significant reductions of glycated haemoglobin and plasma triglyceride levels have been achieved over relatively short periods of time, even in 8 weeks [18]. On the other hand, it has been shown that among physically active men with high BMI – like linemen in the United States National Football League or Sumo wrestlers in Japan – are at risk for rapid onset of MetS upon retirement if the regimen of exercise is not maintained [19,20]. However, BMI alone appears to be poorly associated with metabolic health, and lifestyle counselling generally considered to be metabolically beneficial may not be appropriate standard of care for all subtypes of obese patients [21]. Karelis et al. [22] reported that by the end of a 6-month calorierestricted diet, insulin sensitivity levels in postmenopausal women with MHO had significantly decreased whereas insulin sensitivity levels increased in matched MAO individuals, although both groups lost weight. Thus, not all obesity carries the same cardiometabolic risk and markers such as inflammation, pericardial fat, and genetic/epigenetic variation in genes important for metabolism must be further explored [21]. The strengths of this study are that the data comes from a community-based representative sample of the middleaged Finnish population, and that many aspects of human behaviour were taken into account. Since we used the genderand ethnic-specific WC threshold values of the International
p r i m a r y c a r e d i a b e t e s 9 ( 2 0 1 5 ) 179–183
Diabetes Federation as inclusion criteria, more than 95% of individuals with BMI > 30 kg/m2 in the community were eligible for the study [23]. The major limitation is the crosssectional design, because of which we cannot determine any causal relationships. We did not consider dietary habits of the study participants, nor had we accurate knowledge of forms or intensity of their physical activity. In conclusion, even mild or occasional physical activity seems beneficial to obese individuals. Betablockers may not be the antihypertensive agents of choice when treating obese hypertensive individuals.
[7]
[8]
[9]
[10]
Conflict of interest The authors state that they have no conflict of interest.
Acknowledgement We thank Mr Teemu Kemppainen for performing the statistical analyses.
[11] [12]
[13] [14]
references [15] [1] J.B. Meigs, P.W. Wilson, C.S. Fox, et al., Body mass index, metabolic syndrome, and risk of type-2 diabetes or cardiovascular disease, J. Clin. Endocrinol. Metab. 91 (2906) (2006) 2912. [2] A.C. St-Pierre, B. Cantin, P. Mauriege, et al., Insulin resistance syndrome body mass index and the risk of ischemic heart disease, CMAJ 172 (2005) 1301–1305. [3] P. Pajunen, A. Kotronen, E. Korpi-Hyövälti, et al., Metabolically healthy and unhealthy obesity phenotypes in the general population: the FIN-D2D Survey, BMC Public Health 11 (2011) 754. [4] J. Lindström, J. Tuomilehto, The diabetes risk score: a practical tool to predict type 2 diabetes risk, Diabetes Care 26 (2003) 725–731. [5] World Health Organization, Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia. Report of a WHO/IDF Consultation, World Health Organization, Geneva, 2006. [6] K.G.M.M. Alberti, R.H. Eckel, S.M. Grundy, 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
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International Association for the Study of Obesity, Circulation 120 (2009) 1640–1645. T.F. Babor, J.R. de la Fuente, J. Saunders, M. Grant, AUDIT: The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Health Care, WHO/MNH/DAT 89.4, World Health Organization, Geneva, 1989. A.T. Beck, C.H. Ward, M. Mendelson, et al., An inventory for measuring depression, Arch. Gen. Psychiatry 4 (1961) 561–571. H. Koponen, J. Jokelainen, S. Keinänen-Kiukaanniemi, M. Vanhala, Depressive symptoms and 10 year risk for cardiovascular morbidity and mortality, World J. Biol. Psychiatry 11 (2010) 834–839. P. Pajunen, H. Rissanen, T. Härkänen, et al., The metabolic syndrome as a predictor of incident diabetes and cardiovascular events in the Health 2000 Study, Diabetes Metab. 36 (2010) 395–401. L.J. Waite, Does marriage matter, Demography 32 (1995) 483–508. R.G. Watt, A. Heilmann, W. Sabbah, et al., Social relationships and health related behaviors among older US adults, BMC Public Health 14 (2014) 533–543. J.T. Denney, Family and household formations and suicide in the United States, J. Marriage Fam. 72 (2010) 202–213. T.A. Kotchen, Obesity-related hypertension: epidemiology, pathophysiology, and clinical management, Am. J. Hypertens. 23 (2010) 1170–1178. L.M. Prisant, Preventing type II diabetes mellitus, J. Clin. Pharmacol. 44 (2004) 406–413. W.M. Elliot, P.M. Meyer, Incident diabetes in clinical trials of antihypertensive drugs: a network meta-analysis, Lancet 369 (2007) 201–207. J.S. Rana, T.Y. Li, J.E. Manson, F.B. Hu, Adiposity compared with physical inactivity and risk of type 2 diabetes in women, Diabetes Care 30 (2007) 53–58. D.E. Thomas, E.J. Elliott, G.A. Naughton, Exercise for type 2 diabetes mellitus, CDS Rev. 3 (2006) CD002968. M.A. Miller, L.B. Croft, A.R. Belanger, et al., Prevalence of metabolic syndrome in retired National Football League players, Am. J. Cardiol. 101 (2008) 1281–1284. Y. Matsuzawa, Pathophysiology and molecular mechanisms of visceral fat syndrome: the Japanese experience, Diabetes. Metab. Rev. 13 (1997) 3–13. G.V. Denis, J.A. Hamilton, Healthy obese persons: how can they be identified and do metabolic profiles stratify risk? Curr. Opin. Endocrinol. Diabetes Obes. 20 (369) (2013) 376. A.D. Karelis, V. Messier, M. Brochu, R. Rabasa-Lhoret, Metabolically healthy but obese women: effect of an energy-restricted diet, Diabetologia 51 (2008) 1752–1754. K.G. Alberti, P. Zimmet, J. Shaw, Metabolic syndrome – a new worldwide definition. A consensus statement from the International Diabetes Federation, Diabet. Med. 23 (2006) 469–480.
Contents lists available at ScienceDirect
Primary Care Diabetes journal homepage: http://www.elsevier.com/locate/pcd
Original research
Lifestyle of metabolically healthy obese individuals Päivi E. Korhonen a,b,c,∗ , Pirkko Korsoff a , Tero Vahlberg d , Risto Kaaja a,e a
Satakunta Hospital District, 28100 Pori, Finland Central Satakunta Health Federation of Municipalities, 29200 Harjavalta, Finland c Institute of Clinical Medicine, Family Medicine, University of Turku, 20520 Turku, Finland d University of Turku, 20520 Turku, Finland e Institute of Clinical Medicine, Internal Medicine, University of Turku, 20520 Turku, Finland b
a r t i c l e
i n f o
a b s t r a c t
Article history:
Aims: The aim of this study is to find factors associated with metabolic syndrome in obese
Received 30 May 2014
individuals and thus offer guidance to stay metabolically healthy if obese.
Received in revised form
Methods: A cardiovascular screening programme performed in Finland during the years
14 September 2014
2005–2007, identified 901 obese white individuals. Of them, 269 (30%) were metabolically
Accepted 22 September 2014
healthy according to the Harmonization criteria of metabolic syndrome.
Available online 18 October 2014
Results: In multivariate logistic regression analysis, male sex [odds ratio (OR) 1.44 (95% CI 1.01–2.07)], living alone [OR 1.77 (95% CI 1.18–2.65)], physical inactivity [OR 3.73 (95%
Keywords:
CI 1.24–11.24)], and use of betablockers [OR 2.63 (95% CI 1.75–3.95)] were associated with
Metabolic syndrome
metabolic syndrome.
Obesity
Conclusions: Even mild or occasional physical exercise is beneficial to health in obese indi-
Physical activity
viduals. Betablockers may not be the antihypertensive agents of choice when treating obese hypertensive individuals. © 2014 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.
1.
Introduction
A subset of obese individuals without cluster of metabolic disturbances, known as “metabolically healthy obese” (MHO), appears to be protected to the development of diabetes or cardiovascular diseases [1,2]. Prevalence of MHO individuals depends on the definition used and for the time being, there is no standardized definition to identify MHO individuals for research protocols or in clinical practice. In Finland, the prevalence of MHO defined as obesity (body mass index ≥ 30 kg/m2 ) without metabolic syndrome (MetS) is estimated to be 2.0%
among men and 4.5% among women aged 45–74 years [3]. Successful prevention of MetS among these people would save substantial consequences from an individual perspective and costs from a societal perspective related to prevented or postponed comorbidities. It is currently not known why MHO individuals appear to be protected to the development of MetS. The present study characterized MHO individuals in a population-based screening programme and compared their lifestyle to that of “metabolically abnormal obese” (MAO) individuals in order to find factors associated with MetS in obese individuals.
∗
Corresponding author at: Jokikatu 3, 29200 Harjavalta, Finland. Tel.: +358 40 7653257; fax: +358 2 6741180. E-mail addresses: paivi.e.korhonen@fimnet.fi (P.E. Korhonen), pirkko.korsoff@satshp.fi (P. Korsoff), tero.vahlberg@utu.fi (T. Vahlberg), risto.kaaja@utu.fi (R. Kaaja). http://dx.doi.org/10.1016/j.pcd.2014.09.006 1751-9918/© 2014 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.
180
p r i m a r y c a r e d i a b e t e s 9 ( 2 0 1 5 ) 179–183
2.
Methods
2.1.
Subjects and measurements
The study participants were drawn from a population survey, the Harmonica Project, which was carried out in the rural towns of Harjavalta and Kokemäki in southwestern Finland from autumn 2005 to autumn 2007. A cardiovascular risk factor survey, tape for the measurement of waist circumference, and type 2 diabetes risk assessment form (Finnish Diabetes Risk Score, FINDRISC, available from www.diabetes.fi/english), were mailed to all home-dwelling inhabitants aged 45–70 years (n = 6013) [4]. Out of the 4421 (74%) respondents, those having at least one cardiovascular risk factor (n = 3072) were invited for an enrolment examination performed by a public health nurse. Risk factors taken into account were waist circumference ≥80 cm in women and ≥94 cm in men, hypertension, history of gestational diabetes or hypertension, family history of premature cardiovascular disease, and ≥15 points in the FINDRISC (≥12 points in Harjavalta). Participation and all the tests included were free of charge for the subjects. Patients with known cardiovascular disease or previously diagnosed diabetes were excluded since they already had systematic follow-up in the health centres. The public health nurses examined 2752 risk study participants. Height and weight were measured with the subjects in standing position without shoes and outer garments. Height was recorded to the nearest 0.5 cm and weight to the nearest 0.1 kg Digital scales (Seca® 861, Germany) were used, and their calibration was monitored regularly. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m2 ). Waist circumference was measured at the level midway between the lower rib margin and the iliac crest. Blood pressure was measured with a calibrated mercury sphygmomanometer with subjects in a sitting posture, after resting at least 5 min Two readings taken at intervals of at least 2 min were measured, and the mean of these readings was used in the analysis.
2.2.
Laboratory tests
Laboratory tests were determined in blood samples which were obtained after at least 12 h of fasting. Oral glucose tolerance test was performed by measuring fasting plasma glucose and 2-h plasma glucose from capillary blood with HemoCue® Glucose 201+ system (Ängelholm, Sweden) after ingestion of a glucose load of 75 g anhydrous glucose dissolved in water. Glucose disorders were classified according to the World Health Organization 2006 criteria [5]. Total cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides were measured enzymatically (Olympus® AU640, Japan). Low-density lipoprotein (LDL) cholesterol was calculated according to the Friedewald’s formula.
2.3.
Definitions
MetS was diagnosed according to the Harmonization definition [6] (Table 1). MAO and MHO phenotypes were defined
Table 1 – Criteria for the 2009 Harmonization definition of metabolic syndrome [9]. The presence of any 3 of 5 risk factors constitutes a diagnosis of metabolic syndrome. Measure
Cut point
Elevated waist circumference Elevated triglycerides Reduced HDL-cholesterol
Females ≥80 cm, males ≥94 cm
Elevated blood pressure
Systolic ≥130 and/or diastolic ≥85 mmHg, or antihypertensive drug treatment in a patient with a history of hypertension ≥5.6 mmol/l, or drug treatment of elevated glucose
Elevated fasting glucose
≥1.7 mmol/l, or fibrate or nicotinic acid medication Females <1.3 mmol/l, males < 1.0 mmol/l, or fibrate or nicotinic acid medication
Abbreviation: HDL, high-density lipoprotein.
as BMI ≥ 30 kg/m2 with or without MetS, respectively. Selfreported leisure-time physical activity was classified into categories: (1) “no” physical activity; (2) “functional” was defined doing regularly minor physical activity, such as light gardening or walking or cycling to work; (3) “occasional” and (4) “regular” exercise, such as walking, jogging, cycling or swimming.
2.4.
Questionnaires
Subjects completed self-administrated questionnaires at the clinic before the physical examination was performed: sociodemographic factors, occupational status, physical activity level, smoking status, Alcohol Use Disorders Identification Test (AUDIT) [7], Beck’s Depression Inventory (BDI) [8], selfrated physical health, time spent for TV watching or reading per day, duration and quality of sleep. AUDIT-score ≥ 8 indicates harmful alcohol use, as well as possible alcohol dependence [7]. Depressive symptoms were regarded present if the BDI-score was ≥10 [9].
2.5.
Informed consent
The study protocol and consent forms were reviewed and approved by the ethics committee of Satakunta hospital district. All participants provided written informed consent for the project and subsequent medical research.
2.6.
Statistical analysis
Data is presented as the means with standard deviations (SD) or as frequencies with percentages. The comparisons between MHO and MAO groups were done by using two sample ttest or chi-square test. Predictors significantly associated with metabolic syndrome in univariate logistic regression analysis were included in multivariate logistic regression model. Results are expressed using odds ratios (OD) with 95% confidence intervals (CI). P-values lower than 0.05 were considered statistically significant. Statistical analysis was made with SAS
181
p r i m a r y c a r e d i a b e t e s 9 ( 2 0 1 5 ) 179–183
Table 2 – Characteristics of the study participants. MAO n = 632
MHO n = 269
P-value
Demographic Number of female (%)
348 (55)
176 (65)
0.0039
Age, years, mean (SD)
59.4 (6.8)
58.4 (6.9)
0.042
Cohabiting, n (%)
448 (73)
211 (81)
0.015
Self-rated physical health, n (%) Good Moderate Poor
125 (20) 145 (21) 338 (56)
69 (27) 78 (30) 112 (43)
0.0039
Education, n (%) Basic Occupational Higher
345 (57) 230 (38) 33 (5)
124 (48) 115 (44) 21 (8)
0.035
Strenuous work physically, n (%) Strenuous Not strenuous
266 (45) 331 (55)
107 (42) 149 (58)
0.46
Strenuous work mentally, n (%) Strenuous Not strenuous
244 (41) 351 (59)
105 (41) 149 (59)
0.93
142 (23)
42 (16)
Current or former smoker, n (%)
289 (48)
114 (45)
0.41
Physical activity, n (%) No Functional Occasional Regular
35 (6) 187 (31) 229 (38) 150 (25)
5 (2) 62 (24) 111 (43) 78 (30)
0.0074
TV watching, h/24 h, mean (SD)
2.4 (1.4)
2.3 (1.2)
0.14
Reading, h/24 h, mean (SD)
1.3 (0.9)
1.3 (0.8)
0.60
Sleeping, h/24 h, mean (SD)
7.1 (1.2)
7.1 (1.2)
0.64
Quality of sleep, n (%) Very good Quite good Quite poor Very poor
111 (19) 359 (60) 111 (19) 16 (3)
37 (14) 175 (68) 38 (15) 6 (2)
0.16
34.4 (4.3) 145 (19) 87 (11) 5.39 (0.98) 3.24 (0.89) 1.33 (0.36) 1.79 (0.80) 6.16 (1.40) 8.62 (2.76) 161 (26) 216 (35) 155 (25) 121 (20) 44 (7)
33.6 (3.9) 138 (18) 85 (9) 5.35 (0.97) 3.24 (0.86) 1.56 (0.33) 1.23 (0.59) 5.29 (0.69) 6.81 (1.55) 61 (23) 40 (16) 38 (15) 36 (14) 17 (7)
Health behaviours AUDIT-score ≥8, n (%)
Clinical Body mass index, kg/m2 , mean (SD) SBP, mmHg, mean (SD) DBP, mmHg, mean (SD) Total cholesterol, mmol/l, mean (SD) LDL-cholesterol, mmol/l, mean (SD) HDL-cholesterol, mmol/l, mean (SD) Triglycerides, mmol/l, mean (SD) Fasting glucose, mmol/l, mean (SD) 2-h glucose, mmol/l, mean (SD) Depressive symptoms, n (%) Users of betablockers, n (%) Users of diuretics, n (%) Users of statins, n (%) Users of antidepressants, n (%)
0.023
0.001 <0.0001 0.0002 0.56 0.99 <0.0001 <0.0001 <0.0001 <0.0001 0.31 <0.001 0.0016 0.074 0.87
Abbreviations: MAO, metabolically abnormal obese; MHO, metabolically healthy obese; AUDIT, Alcohol Use Disorders Identification Test; SBP, systolic blood pressure; DBP, diastolic blood pressure; LDL, low-density lipoprotein; HDL, high-density lipoprotein.
182
p r i m a r y c a r e d i a b e t e s 9 ( 2 0 1 5 ) 179–183
Table 3 – Factors associated with the risk for metabolic syndrome in obese individuals. Variables
Odds ratio (95% CI)
Male sex Age Single AUDIT ≥ 8
1.44 (1.01–2.07) 1.01 (0.98–1.03) 1.77 (1.18–2.65) 1.30 (0.83–2.04)
0.047 0.68 0.005 0.26
Education Basic vs. occupational Higher vs. occupational
1.29 (0.91–1.82) 0.76 (0.40–1.46)
0.16 0.41
Physical activity No vs. regular Functional vs. regular Occasional vs. regular
3.73 (1.24–11.24) 1.50 (0.98–2.32) 1.05 (0.71–1.55)
0.019 0.065 0.81
Self-rated health Moderate vs. good Poor vs. good
0.90 (0.58–1.39) 1.17 (0.77–1.77)
0.62 0.46
Use of betablockers
2.63 (1.75–3.95)
Use of diuretics
1.31 (0.85–2.01)
P-value
<0.0001 0.23
Abbreviations: AUDIT, Alcohol Use Disorders Identification Test; CI, confidence interval.
System for Window, version 9.3 (SAS Institute Inc., Cary, NC, USA).
3.
Results
Nine hundred and one obese white individuals (mean age 59 years, range 45–70 years, 58% females) without previously diagnosed cardiovascular disease or diabetes were examined. Of these, 269 (30%) were metabolically healthy. The characteristics of the study participants are shown in Table 2. MHO individuals were more often women and cohabiting, slightly younger, more educated, had less harmful alcohol use, and exercised more regularly than MAO subjects. Cardiometabolic risk factors were significantly lower in MHO than in MAO individuals, with the exception of total and LDLcholesterol which did not differ between the study groups. MAO individuals used more often betablockers and diuretics than MHO subjects. There was no difference between the groups in the prevalence of depressive symptoms or usage of antidepressive drugs. In multivariate logistic regression analysis, male sex [odds ratio (OR) 1.44 (95% CI 1.01–2.07)], living alone [OR 1.77 (95% CI 1.18–2.65)], physical inactivity [OR 3.73 (95% CI 1.24–11.24)], and use of betablockers [OR 2.63 (95% CI 1.75–3.95)] were significantly associated with MetS. (Table 3).
4.
Discussion
In this cohort of obese individuals without established comorbidities, the prevalence of MHO was 30%. The predominance of females in the MHO group is noteworthy, but in concordance with the previous study among Finnish people [3]. In the present study, the international definitions of obesity and MetS were complied, but this approach allows the inclusion of subjects who are only one criterion short of MetS. However, the
2009 Harmonization definition of MetS used has recently been shown to be a significant predictor of future cardiovascular events and diabetes [10]. According to the results of this study, factors associated with MAO phenotype are male sex, bachelorhood, physical inactivity, and use of betablockers. A potential pathway linking marital status to metabolic health is via behavioural factors. Being married [11,12] and having a family [13] have been associated with health promoting behaviours. From a health-care perspective, the factors taken into account are the choice of antihypertensive medication and individual lifestyle counselling. Obese individuals have been shown to have a 3.5-fold increase in the likelihood of hypertension [14]. Thus, many obese individuals without MetS may need medical therapy for hypertension, and the selection of an antihypertensive drug for them is an important issue in clinical practice. Betablockers and diuretics may increase the likelihood of diabetes and hence the likelihood of prediabetes as well [15,16]. Noteworthy is also that depressive symptoms or usage of antidepressive medication did not differ between the MHO and MAO individuals in our study. Thus, depressive mood does not explain observed differences in physical activity levels. Physical exercise has been widely recommended for the treatment of obesity and for the prevention of type 2 diabetes. In our obese study participants, physical inactivity increased the odds of MetS almost 4-fold compared to individuals who exercised regularly. Functional activity was surprisingly quite beneficial to metabolic health, although this association did not reach statistical significance with comparison to regular physical activity. In the Nurses’ Health Study, obesity and physical inactivity independently contributed to the development of type 2 diabetes, but the association of obesity appeared to be much greater than that of physical inactivity [17]. Moreover, also occasional physical activity may have a positive effect on metabolic risk factors. In type 2 diabetics, significant reductions of glycated haemoglobin and plasma triglyceride levels have been achieved over relatively short periods of time, even in 8 weeks [18]. On the other hand, it has been shown that among physically active men with high BMI – like linemen in the United States National Football League or Sumo wrestlers in Japan – are at risk for rapid onset of MetS upon retirement if the regimen of exercise is not maintained [19,20]. However, BMI alone appears to be poorly associated with metabolic health, and lifestyle counselling generally considered to be metabolically beneficial may not be appropriate standard of care for all subtypes of obese patients [21]. Karelis et al. [22] reported that by the end of a 6-month calorierestricted diet, insulin sensitivity levels in postmenopausal women with MHO had significantly decreased whereas insulin sensitivity levels increased in matched MAO individuals, although both groups lost weight. Thus, not all obesity carries the same cardiometabolic risk and markers such as inflammation, pericardial fat, and genetic/epigenetic variation in genes important for metabolism must be further explored [21]. The strengths of this study are that the data comes from a community-based representative sample of the middleaged Finnish population, and that many aspects of human behaviour were taken into account. Since we used the genderand ethnic-specific WC threshold values of the International
p r i m a r y c a r e d i a b e t e s 9 ( 2 0 1 5 ) 179–183
Diabetes Federation as inclusion criteria, more than 95% of individuals with BMI > 30 kg/m2 in the community were eligible for the study [23]. The major limitation is the crosssectional design, because of which we cannot determine any causal relationships. We did not consider dietary habits of the study participants, nor had we accurate knowledge of forms or intensity of their physical activity. In conclusion, even mild or occasional physical activity seems beneficial to obese individuals. Betablockers may not be the antihypertensive agents of choice when treating obese hypertensive individuals.
[7]
[8]
[9]
[10]
Conflict of interest The authors state that they have no conflict of interest.
Acknowledgement We thank Mr Teemu Kemppainen for performing the statistical analyses.
[11] [12]
[13] [14]
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