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BMC Endocrine Disorders

, 20:8 | Cite as

Global prevalence of cardiometabolic risk factors in the military population: a systematic review and meta-analysis

  • Fereshteh Baygi
  • Kimmo Herttua
  • Olaf Chresten Jensen
  • Shirin Djalalinia
  • Armita Mahdavi Ghorabi
  • Hamid Asayesh
  • Mostafa QorbaniEmail author
Open Access
Research article
Part of the following topical collections:
  1. Diabetes and Metabolism

Abstract

Background

Although there are numerous studies on the global prevalence of cardiometabolic risk factors (CMRFs) in military personnel, the pooled prevalence of CMRFs in this population remains unclear. We aimed to systematically review the literature on the estimation of the global prevalence of CMRFs in the military population.

Methods

We simultaneously searched PubMed and NLM Gateway (for MEDLINE), Institute of Scientific Information (ISI), and SCOPUS with using standard keywords. All papers published up to March 2018 were reviewed. Two independent reviewers assessed papers and extracted the data. Chi-square-based Q test was used to assess the heterogeneity of reported prevalence among studies. The overall prevalence of all CMRFs, including overweight, obesity, high low-density lipoprotein (LDL), high total cholesterol (TC), high triglyceride (TG), low high-density lipoprotein (HDL), hypertension (HTN) and high fasting blood sugar (FBS) was estimated by using the random effects meta-analysis. A total of 37 studies met the eligibility criteria and were included in the meta-analysis.

Results

According the random effect meta-analysis, the global pooled prevalence (95% confidence interval) of MetS, high LDL, high TC, high TG, low HDL and high FBS were 21% (17–25), 32% (27–36), 34% (10–57), 24% (16–31), 28% (17–38) and 9% (5–12), respectively. Moreover, global pooled prevalence of overweight, generalized obesity, abdominal obesity and HTN were estimated to be 35% (31–39), 14% (13–16), 29% (20–39) and 26 (19–34), respectively.

Conclusions

The overall prevalence of some cardio-metabolic risk factors was estimated to be higher in military personnel. Therefore, the necessary actions should be taken to reduce risk of developing cardiovascular diseases.

Systematic review registration number in PROSPERO

CRD42018103345

Keywords

Metabolic syndrome Obesity Military personnel Systematic review 

Abbreviations

ATPIII

National Cholesterol Education Program- Adult Treatment Panel III.

CI

Confidence Intervals

CMRFs

Cardiometabolic Risk Factors

FBS

Fasting Blood Sugar

HDL

High-Density Lipoprotein

HTN

Hypertension

IDF

International Diabetes Federation

ISI

Institute of Scientific Information

LDL

Low-Density Lipoprotein

MetS

Metabolic Syndrome

TC

Total Cholesterol

TG

Triglyceride

WHO

World Health Organization

Key messages

  • The global prevalence of metabolic syndrome in the military population was estimated to be 21%.

  • The overall prevalence of obesity in the military population was estimated to be 14%.

  • There was considerable variation in the overall prevalence of cardio-metabolic risk factors was considerable among military personnel.

  • The findings suggest that implementing interventions for the control of cardio-metabolic risk factors among military personnel seems necessary.

Background

The global prevalence of cardiovascular diseases and Metabolic syndrome (MetS) has increased over the last 20 years. The prevalence of Mets in men and women varies from 8% in India to 24% in USA, and from 7% in France to 43% in Iran, respectively [1]. Studies conducted on subjects over the past 20 years revealed that overweight, obesity, hypertension and hypercholesterolemia are the four leading causes of risk factors with the highest share of cardiovascular diseases [2, 3]. Mets is defined as a group of metabolic disorders that can lead to developing cardiovascular diseases, including central obesity, dyslipidemia, type II diabetes mellitus, certain cancers and all-cause mortality [1].

Sociodemographic factors (e.g. age, race and ethnicity), health behaviors (e.g. smoking, physical activity) and neuropsychiatric outcomes (depression, post-traumatic disorders) play a decisive role in the development of Mets [4, 5, 6]. Some of these factors are independently associated with military service [7, 8]. Military service personnel work in a unique environment characterized by high risk conditions and high levels of occupational stress [9]. It has been reported that military personnel with their heavy responsibilities are more likely to expose a greater risk of developing cardiovascular risk factors [10, 11].

Obesity and MetS have become the main health threat factors in military health system and their alarming incidence is a serious challenge for authorized organizations [12]. A study conducted on a population of military personnel in Iran reported that the prevalence of Mets, overweight and abdominal obesity in this group was estimated to be 11, 48 and 45%, respectively [13]. The prevalence of MetS in Chinese general population (16.5%) was much lower than that in the military population (35%) [14]. Obesity has been called as a serious national security threat by military institute in the United States [12]. A study on military personnel in Saudi Arabia revealed that the prevalence rates of overweight, obesity and current smoking were 41, 29 and 35% respectively [15].

There are numerous studies on the global prevalence of cardio metabolic risk factors (CMRFs) among military personnel. It is thus important to obtain an overall estimation on the prevalence of above-mentioned risk factors by synthesizing available studies. To date, the current study is the first meta-analysis conducted on this topic globally. Therefore, this study aimed to systematically review the literature on the estimation of the global pooled prevalence of CMRFs, including overweight, obesity, high low-density lipoprotein (LDL), high total cholesterol (TC), high triglyceride (TG), low high-density lipoprotein (HDL), hypertension (HTN) and high fasting blood sugar (FBS) in the military population.

Methods

Identification of relevant studies

This is a comprehensive systematic review of all available evidences on the prevalence of CMRFs in the military personnel. We developed a systematic review adhering to the PRISMA-P guidelines [16]. All the documents are based on the details of the study protocol. Registration number of current study in PROSPERO is CRD42018103345.

The main root of developing the search strategies is based on the two main components of “cardio metabolic risk factors” and “metabolic syndrome” in military personals. To assess the optimal sensitivity of search for documents, we simultaneously searched PubMed and NLM Gateway (for MEDLINE), Institute of Scientific Information (ISI), and SCOPUS as the main international electronic data sources (Additional file 1).

Inclusion and exclusion criteria

All available observational studies conducted up to March 2018 c on relevant subjects were included. There was no limitation for the target groups in terms of age and gender and language of published studies. In situation of more than one paper from the one study, the most complete data were considered. We also excluded papers with duplicate citation. Non-peer reviewed articles, conference proceedings and book chapters were considered for more access to relevant data.

Quality assessment and data extraction

After completing all three steps of data assessment for titles, abstracts and full texts, the full texts of each article selected were retrieved for more detailed analysis. The quality assessment and data extraction were followed a check list recorded citation, publication year, study year, place of study, type of study, population characteristics and methodological criteria (sample size, mean age, type of measure, results of measures and other information).

The whole process of searching for the data extraction and quality assessment was followed independently by two research experts. The kappa statistic for agreement of quality assessment was 0.94. Probable discrepancies between experts were resolved by discussion. Any disagreements were resolved by consensus by a third person. The quality assessment was performed using a validated quality assessment checklist for prevalence studies [17]. This tool comprises 10 items which covers methodological quality of prevalence studies, including sampling method (2 questions), data collection (5 questions) and data analysis (3 questions). Each item can be answered either Yes/No or Unclear/ Not applicable. The overall score for 10 studies was the total score ≥ 6, considered as acceptable in terms of quality.

Statistical analysis

The prevalence and 95% confidence intervals (CI) were used for presenting the results. Chi-square based on Q test and I square statistics were used to assess the heterogeneity of reported prevalence among the studies. P < 0.05 was regarded as statistically significant at. Due to severe heterogeneity among studies regarding reported prevalence, the pooled prevalence was estimated using a random-effect meta-analysis proposed by Der-Simonian and Laird. We undertook a meta-regression analysis to assess the effect of study covariates, including the mean age of participants, quality score, type of personnel, and years of publication of reported prevalence. Meta-analysis was performed for risk factors reported in more than four studies. If a study was reported separately the prevalence of CMRFs over a time period, the weighted prevalence for the entire period would calculate and then this value could be considered as an overall prevalence in the meta-analysis. The prevalence of MetS was extracted according to International Diabetes Federation (IDF), World Health Organization (WHO) and National Cholesterol Education Program- Adult Treatment Panel III (ATPIII) criteria. Since most studies had reported MetS by ATP-III criteria, only these studies were included in meta-analysis. To assess the effect of each study on overall prevalence, we performed sensitivity analyses by sequentially removing each study and rerunning the analysis. Statistical analysis was performed using STATA software, V.11.1 (StataCorp LP, College Station, Texas, USA).

Results

Study selection process

Figure 1 shows the flowchart of selection of studies for inclusion in the meta-analysis. In total, 2395 papers were identified after initial database search. Of these, 51 full-text papers were assessed for eligibility. In the next phase, 14 full text papers were excluded and finally 37 studies were eligible for inclusion in this meta-analysis: [9, 13, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51].
Fig. 1

PRISMA 2009 flow diagram. From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(7): e1000097. doi: https://doi.org/10.1371/journal. pmed1000097. For more information, visit www.prisma-statement.org.

Study characteristics

The selected articles were published between 2001 and 2017. Out of 37 studies, 8 contained the prevalence information for navy, 16 for military personnel, 5 for army, 5 for soldier’s /warship personnel and 3 for air force staff. Six studies had reported trends in the prevalence of CMRFs over a time period [22, 24, 26, 28, 30, 40], so that their weighted prevalence was considered as an overall prevalence. Among all publications, 15 studies were conducted in the American countries [9, 19, 20, 24, 25, 26, 27, 29, 30, 31, 32, 36, 38, 41, 51], 13 in Europe [22, 28, 33, 34, 35, 37, 39, 40, 44, 45, 48, 49, 50] and 9 in Asia [13, 15, 18, 21, 23, 42, 43, 46, 47].

Qualitative synthesis

Table 1 shows the general characteristics of the selected studies for the prevalence of MetS. According to ATPIII criteria, the highest and lowest prevalence rates of MetS were 39 and 9% in US mariners [31] and French military staffs [49], respectively. The prevalence range of MetS was 3.8–39% according to the different definition criteria.
Table 1

Characteristic of the selected studies on the prevalence of Mets

Author, year

Country

Study type

Study year

Study population

Sampling

Sample size

Mean age/ Range

Outcome

Definition/Criteria

Prevalence%(95% CI)

Payab, 2017 [13],

Iran

C/S

2015

Military

Convenience

2200

37.73

Mets

ATPIII

11.1

(9.8–12.5)

ATPIII with waist> 90 cm

26.6

(24.7–28.5)

ATPIII> 95 cm

19.6

(17.9–21.3)

Sharma, 2016 [18],

India

C/S

Not provided

Military

aircrew

Convenience

210

20–50

Mets

MS-4

33.0

(26.6–39.7)

ATPIII

11.9

(7.6–16.7)

IDF

7.1

(4.0–11.7)

WHO

3.8

(1.8–7.6)

Gasier, 2016 [20],

US

C

Not provided

Navy

(Submariners)

Convenience

53

29

Mets

ATP-III

30.0

(18.7–44.5)

Baygi, 2016 [21],

Iran

C/S

2015

Seafarers

Convenience

234

36

Mets

IDF

14.9

(10.8–20.3)

Rhee, 2015 [23],

Korea

C/S

2014

Military aviators

Convenience

911

24–49

Mets

WHO

9.8

(7.9–11.9)

Herzog, 2015 [27],

US

C/S

2012

Military

Convenience

79,139

18–65

Mets

ATPIII

16.7

(15.7–16.2)

Filho, 2014 [9],

Brazil

C/S

2012

Military

Convenience

452

45.8

Mets

ATPIII

38.5

(34.0–43.2)

Scovill, 2012 [31],

US

C/S

Not provided

Mariner

Convenience

388

44

Mets

ATPIII

39.0

(34.1–43.9)

Hagnas, 2012 [33],

Finland

Prospectiv

Not provided

Military

Convenience

1046

19.2

Mets

IDF

6.1

(4.8–7.8)

Costa, 2011 [36],

Brazil

C/S

2008

Navy

Convenience

1383

30.7

Mets

IDF

17.6

(15.6–19.7)

Khazale, 2007 [43],

Jordan

C

2006

Air force

Convenience

111

32.5

Mets

ATPIII

18

(11.6–26.7)

Al-Qahtani, 2005 [47],

Saudi Arabia

C/S

2004

Soldiers

Convenience

1079

20–60

Mets

ATPIII

20.8

(18.4–23.3)

Athyros, 2005 [48],

Greece

C/S

2003

Military

Convenience

300

37.0

Mets

ATPIII

9.4

(6.4–13.3)

Bauduceau, 2005 [49],

France

C/S

2003

Military

Convenience

2045

38.6

Mets

ATPIII

WHO

9.0

(7.8–10.3)

14.0

(12.5–15.6)

C/S: Cross-sectional; C: Cohort; Mets: Metabolic Syndrome; ATPIII: Adult Treatment Panel III; IDF: International Diabetes Federation; WHO: World Health Organization

Characteristics of the selected studies for the prevalence of overweight, generalized obesity and abdominal obesity are shown in Table 2. The highest prevalence of overweight (66%) and obesity (62%) was reported in Danish seafarers and the US submariners, respectively.
Table 2

Characteristic of the included studies on the prevalence of overweight, obesity and abdominal obesity

Author, year

Country

Study type

Study year

Study population

Sampling

Sample size

Mean age/ Range

Outcome

Definition/Criteria

Prevalence%

(95% CI)

Payab, 2017 [13],

Iran

C/S

2015

Military

Convenience

2200

37.73

Overweight

Obesity

Abdominal Obesity

25.9 ≤ BMI < 29.9 kg/m2

BMI ≥ 30 kg/m2

WC > 90 cm

47.59

(45.4–49.7)

15.05

(13.6–16.6)

45.4

(43.3–47.5)

Rush, 2016 [19],

US

C/S

2001

Military

Randomly

77,047

42

Overweight

Obesity

25 ≤ BMI < 29.9 kg/m2

BMI ≥ 30 kg/m2

51.0

(50.6–51.3)

23.0

(22.7–23.3)

Gasier, 2016 [20],

US

C

Not provided

Navy

(Submariners)

Convenience

53

29

BF%

Overweight

Obesity

BF ≥ 25%

27.0

(15.7–40.6)

25 ≤ BMI < 29.9 kg/m2

6.0

(1.5–16.6)

BMI ≥ 30 kg/m2

62.0

(47.8–74.9)

Baygi, 2016 [21],

Iran

C/S

2015

Sefarers

Convenience

234

36

Abdominal obesity

Excess weight

WC > 95 cm

38.5

(32.3–45.0)

BMI > 25 kg/m2

51.1

(44.7–57.8)

Fajfrova,2016 [22],

Czech Republic

C/S

 

Armed Forces

Convenience

69,962

40

Overweight

Obesity

51.5

(51.0–52.0)

14.0

(13.7–14.2)

Rhee, 2015 [23],

Korea

C/S

2014

Military aviators

Convenience

911

24–49

Abdominal obesity

WC > 90 cm

25.3

(22.5–28.2)

Reyes-Guzman, 2015 [24],

US

C/S

2008

Military

Randomly

90,905

25–46

Overweight

Obesity

25 ≤ BMI < 29.9 kg/m2

47.8

(47.4–48.3)

BMI ≥ 30 kg/m2

9.6

(9.4–9.7)

Lennon, 2015 [25],

US

C/S

2012

Sailor

Convenience

313,513

17–50

Obesity

BMI > 30 kg/m2

13.6

(13.4–13.7)

Hruby, 2015 [26],

US

C/S

2012

Army

Convenience

1,703,150

20–40

Overweight

Obesity

25 ≤ BMI < 30 kg/m2

BMI ≥ 30 kg/m2

33.6

(33.5–33.6)

8.2

(8.1–8.2)

BinHoraib, 2013 [15],

Saudi Arabia

C/S

2009

Military

Multi-stage stratified random

10,229

34.1

Overweight

Obesity

Abdominal obesity

25 ≤ BMI < 30 kg/m2

40.9

(39.9–40.7)

BMI ≥ 30 kg/m2

29.0

(28.1–29.9)

WC > 90 cm

42.4

(41.4–43.3)

Binkowska-Bury, 2013 [28],

Poland

C/S

2010

Military

Convenience

37,916

19

Overweight

Obesity

25 ≤ BMI < 29.9 kg/m2

12.6

(12.2–12.9)

BMI ≥ 30 kg/m2

3.0

(2.8–3.1)

Marion,2012 [29],

US

C/S

2008

Navy

Convenience

26,341

26.5

Obesity

BMI ≥ 30 kg/m2

15.9

(15.4–16.3)

Smith, 2012 [30],

US

Not provided

2005

Military

Convenience

28,602

17–40

Excess weight

BMI ≥ 25 kg/m2

58.9

(58.3–59.4)

Scovill, 2012 [31],

US

C/S

Not provided

Mariner

Convenience

388

44

Obesity

BMI ≥ 30 kg/m2

61.0

(56.0–65.9)

Pasiakos, 2012 [32],

US

L

Not provided

Army

Convenience

209

21

Obesity

BMI ≥ 30 kg/m2

14.0

(9.6–19.5)

Sundin, 2011 [34],

UK

Not provided

2006

Armed Forces

Stratified Random Sampling

T:2470

M:2148

F:311

28.3

Overweight

T

M

F

Obesity

T

M

F

25 ≤ BMI < 30 kg/m2

29.6

(27.7–31.4)

BMI ≥ 30 kg/m2

30.5%

(28.6–32.5)

27.1%

(22.2–32.3)

13.5

(12.2–14.9)

13.5%

(12.1–15.0)

13.5%

(10.0–17.9)

Hansen, 2011 [35],

Denmark

Not provided

2010

Seafarers

Convenience

2101

18–64

Overweight

25 ≤ BMI < 30 kg/m2

66.0

(36.9–67.9)

Costa, 2011 [36],

Brazil

C/S

2008

Navy

Convenience

1383

30.7

Abdominal obesity

WC ≥ 90 cm

35.0

(32.5–37.6)

Mullie, 2010 [37],

Belgium

C/S

2007

Army

Random

974

44.0

Obesity

BMI ≥ 30 kg/m2

15.2

(13.3–17.9)

Wenzel, 2009 [38],

Brazil

C/S

2000

Military

Air force

Convenience

380

19–49

Overweight

Obesity

25 ≤ BMI < 30 kg/m2

36.0

(31.3–41.1)

BMI ≥ 30 kg/m2

8.0

(5.5–11.2)

Saely, 2009 [39],

Switzerland

C

2004

Army

Convenience

56,784

19.7

Overweight

Obesity

25 ≤ BMI < 30 kg/m2

16.8

(16.5–17.1)

BMI ≥ 30 kg/m2

4.1

(3.9–4.2)

Mullie, 2008 [40],

Belgium

C/S

1992–2005

Army

Convenience

43,343

20–59

Overweight

Obesity

25 ≤ BMI < 30 kg/m2

BMI ≥ 30 kg/m2

34.9

(34.4–35.3)

3.5

(3.3–3.6)

Napradit, 2007 [42],

Thailand

C/S

2005

Army

Convenience

4276

41.5

Overweight

Obesity

25 ≤ BMI < 30 kg/m2

BMI ≥ 30 kg/m2

27.1

(25.7–28.4)

4.9

(4.3–5.6)

Khazale, 2007 [43],

Jordan

C

2006

Air force

Convenience

111

32.5

Abdominal obesity

WC > 102 cm

9.3

(4.6–16.3)

Hoeyer, 2005 [45],

Denmark

Not provided

Not provided

Seafarers

Convenience

1257

16–66

Overweight

Obesity

25 ≤ BMI < 30 kg/m2

17.1

(15.1–19.2)

BMI ≥ 30 kg/m2

5.8

(4.6–7.3)

Al-Qahtani, 2005 [46],

Saudi Arabia

C/S

2004

Soldiers

Convenience

1049

36.1

Overweight

Obesity

25 ≤ BMI < 30 kg/m2

37.5

(34.5–40.4)

BMI ≥ 30 kg/

31.6

(28.7–34.4)

Al-Qahtani, 2005 [47],

Saudi Arabia

C/S

2004

Soldiers

Convenience

1079

20–60

Abdominal Obesity

WC > 102 cm

33.1

(30.3–36.0)

Athyros, 2005 [48],

Greece

C/S

2003

Military

Convenience

300

37.0

Abdominal Obesity

WC > 102 cm

13.7

(10.1–18.2)

Bauduceau, 2005 [49],

France

C/S

2003

Military

Convenience

2045

38.6

Abdominal obesity

WC > 102 cm

17.0

(15.4–18.7)

Mazokopakis, 2004 [50],

Greece

C/S

1998

Warship personnel

Convenience

274

24.4

Overweight

Obesity

25 ≤ BMI < 29.9 kg/m2

26.5

(21.2–31.9)

BMI ≥ 30 kg/m2

4.7

(2.6–8.1)

Lindquist, 2001 [51],

US

C/S

1995–1998

Military

Convenience

33,457

20–35

Overweight

BMI ≥ 25 kg/m2

52.0

(51.4–52.5)

C/S: Cross-sectional; L: Longitudinal; BF: Body Fat; BMI: Body Mass Index; ATPIII: Adult Treatment Panel III; IDF: International Diabetes Federation; WC: Waist circumferences; F: Female; M: Male; T: Total

Table 3 shows the characteristics of the selected studies for the prevalence of abnormal lipid profile and other CMRFs. A study carried out by Smoley et al. [41] in the US found the highest prevalence (63%) of Pre-HTN. The highest and lowest prevalence rates of HTN were observed in the Brazilian military (55.8%) and the Iranian military (2.6%), respectively. The highest and lowest prevalence rates of high TG were 50.9% [9] and 5.0% [32] for American military personnel.
Table 3

Characteristic of the included studies on the prevalence of high level lipid profile, high glycemic indices and hypertension

Author, year

Country

Study type

Study year

Study population

Sampling

Sample size

Mean age/ Range

Outcome

Definition/Criteria

Prevalence%

(95% CI)

Payab, 2017 [13],

Iran

C/S

2015

Military

Convenience

2200

37.73

HTN

SBP ≥130 mmHg or

DBP ≥85 mmHg

2.6

(1.98–3.37)

Gasier, 2016 [20],

US

C

Not provided

Obese Navy

(Submariners)

Convenience

53

29

Insulin resistant

HOMA> 2.73

30.0

(18.7–44.5)

Baygi, 2016 [21],

Iran

C/S

2015

Sefarers

Convenience

234

36

High TG

TG ≥150 mg/dl

25.2

(20.3–31.8)

26.5

(21.1–32.7)

26.5

(21.1–32.7)

28.2

(22.6–34.5)

19.2

(14.5–25.0)

23.1

(17.9–29.11)

Low HDL

HDL < 40 mg/dl

High LDL

LDL.130 mg/dl

High TC

TC ≥ 200 mg/dl

HTN

SBP ≥130 mmHg or DBP ≥85 mmHg

High FBS

FBS > 100 mg/dl

Rhee, 2015 [23],

Korea

C/S

2014

Military aviators

Convenience

911

24–49

High BP

Impaired glucose

High TG

Low HDL

SBP ≥130 mmHg or

DBP ≥85 mmHg

FBS ≥ 100 mg/dl

TG ≥150 mg/dl

HDL < 40 mg/dl

31.7

(28.7–34.9)

19.0

(16.5–21.7)

16.6

(14.2–19.1)

7.9

(6.3–9.9)

Filho, 2014 [9],

Brazil

C/S

2012

Military

Convenience

452

45.8

HTN

SBP ≥130 mmHg or

55.8

(51.0–60.4)

50.9

(46.2–55.6)

30.5

(26.4–35.0)

30.5

(26.4–35.0)

High TG

DBP ≥85 mmHgTG

Low HDL

≥150 mg/dl

High FBS

HDL < 40 mg/dl FBS > 100 mg/dl

Scovill, 2012 [31],

US

C/S

Not provided

Mariner

Convenience

388

44

HTN

SBP ≥130 mmHg or

42.0

(37.1–47.1)

42.0

(37.1–47.1)

47.0

(41.8–52.0)

22.0

(17.9–26.4)

High TG

DBP ≥85 mmHg

Low HDL

TG ≥150 mg/dl

High FBS

HDL < 40 mg/dl

LDL > 130 mg/dlFBS ≥ 100 mg/dl

Pasiakos, 2012 [32],

US

L

Not provided

Army

Convenience

209

21

High TC

High TG

Low HDL

High LDL

High FBS

TC ≥ 200 mg/dl

TG ≥150 mg/dl

HDL < 40 mg/dl

LDL > 130 mg/dl

FBS > 100 mg/dl

8.0

(4.9–12.9)

5.0

(2.4–8.9)

33.0

(26.8–39.9)

39.0

(32.2–45.7)

8.0

(4.9–12.9)

Costa, 2011 [36],

Brazil

C/S

2008

Navy

Convenience

1383

30.7

Low HDL

HTN

High TG

High FBS

HDL < 40 mg/dl

SBP ≥130 mmHg or

DBP ≥85 mmHg

TG ≥150 mg/dl

FBS ≥ 100 mg/dl

43.0

(40.4–45.7)

26.3

(24.0–28.7)

19.3

(17.3–21.5)

6.6

(5.4–8.0)

Mullie, 2010 [37],

Belgium

C/S

2007

Army

Random

974

44.0

High TC

TC ≥ 190 mg/dl

65.0

(61.7–67.9)

Wenzel, 2009 [38],

Brazil

C/S

2000

Military

Air force

Convenience

380

19–49

HTN

SBP ≥140 mmHg or

DBP ≥90 mmHg

22.0

(18.1–26.7)

Saely, 2009 [39],

Switzerland

C

2004

Army

Convenience

56,784

19.7

Pre-HTN

HTN

High TC

120 ≤ SBP < 139 mmHg

SBP ≥140 mmHg or

DBP ≥90 mmHg

TC ≥ 190 mg/dl

61.4

(61.0–61.8)

26.8

(26.4–27.2)

7.8

(7.6–8.0)

Smoley, 2008 [41],

US

C/S

2004

Service members

Convenience

15,391

27.8

Pre HTN

HTN

120 ≤ SBP < 139 mmHg or

80 ≤ DBP < 89 mmHg

SBP ≥140 mmHg or

DBP ≥90 mmHg

63.0

(62.2–63.7)

11.0

(105–11.5)

Napradit, 2007 [42],

Thailand

C/S

2005

Army

Convenience

4276

41.5

HTN

SBP ≥140 mmHg or

DBP ≥90 mmHg

34.5

(33.1–35.9)

Khazale, 2007 [43],

Jordan

C

2006

Air force

Convenience

111

32.5

High SBP

High DBP

High TC

Low HDL

High FBS

SBP > 130 mmHg

DBP > 85 mmHg

TC ≥ 150 mg/dl

HDL < 40 mg/dl

FBS > 100 mg/dl

9.6

(4.6–16.3)

23.1

(13.8–29.6)

52.2

(42.6–61.7)

38.7

(29.7–48.5)

9.6

(4.6–16.3)

Vaicaitiene, 2006 [44],

Lithuania

C/S

Not provided

Military

Random

200

25–54

High TC

TC ≥ 240 mg/dl

43.4

(36.5–50.6)

Al-Qahtani, 2005 [47],

Saudi Arabia

C/S

2004

Soldiers

Convenience

1079

20–60

High TG

High BP

TG ≥150 mg/dl

SBP > 130 mmHg

DBP > 85 mmHg

32.2

(29.4–35.5)

29.5

(26.8–32.3)

Athyros, 2005 [48],

Greece

C/S

2003

Military

Convenience

300

37.0

High FBS

High TG

Low HDL

Impaired Glucose

FBS > 100 mg/dl

TG ≥150 mg/dl

HDL < 40 mg/dl

FBS > 100 mg/dl

4.0

(2.2–7.1)

25.0

(20.3–30.4)

9.4

(6.4–13.3)

3.0

(1.5–5.8)

1.0

(0.3–3.1)

Bauduceau, 2005 [49],

France

C/S

2003

Military

Convenience

2045

38.6

HTN

High TG

Low HDL

High FBS

SBP > 130 mmHg

or DBP > 85 mmHg

TG ≥150 mg/dl

HDL < 40 mg/dl

FBS > 100 mg/dl

51.0

(48.7–53.1)

17.0

(15.4–18.7)

9.6

(8.4–10.9)

5.0

(4.1–6.0)

C/S: Cross-sectional; C: Cohort; L: Longitudinal; ATPIII: Adult Treatment Panel III; IDF: International Diabetes Federation; WHO: World Health Organization; FBS, fasting blood sugar; TC, total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; BP, blood pressure; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; HTN: Hypertension; HOMA: Homeostasis model assessment

Meta-analysis

The results of meta-analysis are shown in Table 4. The total sample size of the studies included in meta-analysis was n = 12,153,936. The study population consisted of men and women aged 16–66 years. The eligible studies for estimation of the prevalence of MetS, overweight, obesity, high LDL, high TC and HTN were 10, 19, 22, 29, 6 and 13, respectively.
Table 4

The pooled prevalence of cardiometabolic risk factors in Military Population at global level using random effect meta-analysis method

Variables

No. of studies

Sample Size

Prevalence (CI 95%)

Model

I2(%)

*P-value

MetS

10

4,912,369

21 (17–25)

Random

97

< 0.001

Overweight

19

2,867,867

35 (31–39)

Random

99

< 0.001

Obesity

22

3,211,654

14 (13–16)

Random

99

< 0.001

Abdominal obesity

8

17,581

29 (20–39)

Random

99

< 0.001

HTN

13

816,414

26 (19–34)

Random

99

< 0.001

High TG

9

7001

24 (16–31)

Random

98

< 0.001

Low HDL

9

6033

28 (17–38)

Random

99

< 0.001

High LDL

29

157,730

32 (27–36)

Random

99

< 0.001

High TC

6

58,512

34 (10–57)

Random

99

< 0.001

High FBS

6

4436

9 (5–12)

Random

92

< 0.001

*According to Q test (Chi-square test)

According to random effect meta-analysis, the rates of the global pooled prevalence (95% confidence interval) of MetS, high LDL, high TC, high TG, low HDL and high FBS were 21% (17–25), 32% (27–36), 34% (10–57), 24% (16–31), 28% (17–38) and 9% (5–12), respectively. Moreover, the rates of the global estimated pooled prevalence of overweight, generalized obesity, abdominal obesity and HTN were 35% (31–39), 14% (13–16), 29% (20–39) and 26% (19–34), respectively. Figure 2 shows a forest plot of eligible articles for the estimation of MetS prevalence.
Fig. 2

Forest plot of MetS global prevalence using random-effect model

Quality assessment

The quality assessment of the included studies was performed by using a critical appraisal tool for use in systematic reviews addressing questions of prevalence. Accordingly, all studies had an acceptable quality score (Table 5).
Table 5

Quality assessment of the included studies

Study

Total score

Item 1

Item 2

Item 3

Item 4

Item 5

Item 6

Item 7

Item 8

Item 9

Item 10

Payab, 2017

7

N

Y

Y

Y

N

Y

Y

N

Y

Y

Sharma, 2016

5

N

Y

Y

N

Y

N

N

Y

Y

Rush, 2016

6

N

Y

Y

Y

N

Y

N

N

Y

Y

Gasier, 2016

3

N

N

N

N

N

Y

UC

N

Y

Y

Baygi, 2016

7

N

Y

Y

Y

NA

Y

Y

N

Y

Y

Fajfrova,2016

4

N

Y

Y

Y

NA

N

Y

N

N

N

Rhee, 2015

8

N

Y

Y

Y

NA

Y

Y

Y

Y

Y

Reyes-Guzman, 2015

7

N

Y

Y

Y

N

Y

N

Y

Y

Y

Lennon, 2015

6

N

Y

Y

Y

NA

Y

N

N

Y

Y

Hruby, 2015

7

N

Y

Y

Y

NA

Y

UC

Y

Y

Y

Herzog, 2015

7

N

Y

Y

Y

NA

Y

UC

Y

Y

Y

Filho, 2014

5

N

N

Y

Y

N

Y

UC

N

Y

Y

BinHoraib, 2013

8

N

Y

Y

Y

N

Y

Y

Y

Y

Y

Binkowska-Bury, 2013

4

N

Y

Y

N

NA

Y

UC

Y

N

N

Marion,2012

7

N

Y

Y

Y

NA

Y

UC

Y

Y

Y

Smith, 2012

7

N

Y

Y

Y

NA

Y

UC

Y

Y

Y

Scovill, 2012

3

N

Y

Y

N

N

Y

UC

N

N

N

Pasiakos, 2012

5

N

N

Y

Y

N

Y

UC

Y

N

Y

Hagnas, 2012

3

N

Y

Y

N

N

N

Y

N

N

N

Sundin, 2011

7

N

Y

Y

Y

N

Y

N

Y

Y

Y

Hansen, 2011

7

N

Y

Y

Y

NA

Y

Y

N

Y

Y

Costa, 2011

6

N

N

Y

Y

N

Y

N

Y

Y

Y

Mullie, 2010

6

N

N

Y

Y

Y

Y

UC

N

Y

Y

Wenzel, 2009

7

N

N

Y

Y

N

Y

Y

Y

Y

Y

Saely, 2009

5

N

Y

Y

N

NA

Y

UC

N

Y

Y

Mullie, 2008

7

N

Y

Y

Y

N

Y

N

Y

Y

Y

Smoley, 2008

8

N

Y

Y

Y

NA

Y

Y

Y

Y

Y

Napradit, 2007

7

N

Y

Y

Y

N

Y

N

Y

Y

Y

Khazale, 2007

5

N

Y

N

Y

N

Y

N

N

Y

Y

Vaicaitiene, 2006

7

N

Y

Y

Y

N

Y

Y

N

Y

Y

Hoeyer, 2005

5

N

N

Y

Y

N

Y

N

N

Y

Y

Al-Qahtani, 2005

6

N

N

Y

N

Y

Y

N

Y

Y

Y

Al-Qahtani, 2005

6

N

N

Y

N

Y

Y

N

Y

Y

Y

Athyros, 2005

6

N

Y

Y

Y

N

Y

N

N

Y

Y

Bauduceau, 2005

5

N

Y

Y

Y

N

Y

Y

N

N

N

Mazokopakis, 2004

3

N

N

Y

Y

N

Y

N

N

N

N

Lindquist, 2001

6

N

Y

Y

Y

Y

Y

N

Y

N

N

Item 1: Was the sample representative of the target population?

Item 2: Were study participants recruited an appropriate way?

Item 3: Was the sample size adequate?

Item 4: Where the study subjects and setting described in detail?

Item 5: Was the data analysis conducted with sufficient coverage of the identified sample?

Item 6: Were objective, standard criteria used for measurement of the condition?

Item 7: Was the condition measured reliably?

Item 8: Was there appropriate statistical analysis?

Item 9: Are all important confounding factors/subgroups/different identified and accounted for?

Item 10: Were subpopulations identified using objective criteria?

Y: Yes, N: No, UC: Unclear, NA: Not applicable

Meta-regression

Results of meta-regression analysis demonstrated that effect of study characteristics, including the mean age of participant, quality score, type of personnel, and years of publication on reported prevalence was not statistically significant (p > 0.05).

Sensitivity analysis

Sensitivity analyses were performed to assess effect of each individual study on pooled prevalence rates. The results showed that no significant changes in in the pooled prevalence was found in the included studies (p > 0.05).

Discussion

To the best of our knowledge, this is the first meta-analysis to estimate the global pooled prevalence of CMRFs in the military population. In the current study, the overall prevalence of MetS was estimated to be 21% according to ATP-III criteria. The prevalence of Mets was among Iranian male military personnel 11% [13]. Corresponding prevalence was 35% in Chinese military population, while it was 17% in the Chinese general population [14]. The prevalence of Mets was 39% among Brazilian soldiers [9], whereas it was 15% among Royal Jordanian Air Force pilots [4]. In a study conducted by Baygi et al. on Iranian seafarers demonstrated that the prevalence of Mets was 15% which was lower than that (33%) for urban dwellers of Tehran [21]. The wide variation in these prevalence rates may be due to differences in study samples, age and gender.

In the present study, the estimated prevalence rates of overweight, obesity and abdominal obesity were 35, 14 and 29%, respectively. Bin Horaib et al. in their study of 5 military regions of Kingdom of Saudi Arabia among 10,500 active military personnel reported that the proportions of overweight, obesity and abdominal obesity were 41, 29 and 42%, respectively [15]. The prevalence rate of overweight was 52% in the U.S. navy [51], whereas it was 66% among Danish seafarers [35]. Using the dissimilar cutoff points and including females in some of the studies may explain differences between the prevalence figures. Because of the nature of their job, military individuals are generally assumed to be healthier. However, our findings showed an alarming trend in the global prevalence rates of overweight and obesity, which might be due to unhealthy diet practice among military personnel [13].

In the present study, the reported prevalence rates of Pre-HTN and HTN were 62 and 26%, respectively. A study conducted on male subjects in Saudi Arabia showed that the prevalence rate of HTN was 33%, indicating a progressive increase in body fat with age [52]. The results of a National survey conducted in the U.S. demonstrated that the estimated age-adjusted prevalence of HTN was 27% in men and 30% in women [53]. The corresponding estimate in general population of Korea was 33%, increased progressively with age from 14% among 14–24-year-olds to 71% among subjects aged 75 years or older [54]. The prevalence rate of HTN in people with regular and intensive physical activity was 13% lower than that in their non-active peers [55]. Our results showed that the prevalence rate of HTN in military personnel was 26% that was lower than that in the general population. This is likely explained by a reverse association between intensive physical activity and HTN.

Based on our findings, the estimated prevalence rates of high TG, low HDL, high LDL and high TC were 24, 28, 32 and 34%, respectively. The results of a study conducted among 911 Korean military aviators demonstrated that the prevalence rates of elevated TG and reduced HDL were 16.6 and 7.9%, respectively [23]. The prevalence rates of mentioned figures in the general Korean population were significantly lower than those of their peers in Air Force [56]. A meta-analysis conducted by Tabatabaei et al. in Iranian general population showed that these figures for high TG, low HDL, high LDL and high TC were 41.6, 46, 35.5 and 43.9%, respectively [57]. The significant differences between general population and military personnel with respect to lipid profile could be explained by their strict standards for physical activity on a regular basis as which might have positive effects on their overall health status.

In the current study, the overall prevalence rates of high FBS and diabetes were 9 and 5%, respectively. The global prevalence rare of diabetes for all age groups has been estimated to be 2.8% in 2000 and 4.4% in 2030 [58]. The results of a study performed in Greece showed that the prevalence rate of diabetes was 10.6% in general population and 3.0% among military staff [48]. This is likely due to higher physical activity levels in the military personnel compared to their peers in the general population. Additionally, nutrition and physical activity of military individuals are strictly controlled for maintaining their healthy body weight which has a positive effect on managing FBS level and preventing Diabetes and other non-communicable diseases and their risk factors.

The limitations of this study are as follows, in most of the included studies, convenience sampling was used to estimate the prevalence which might be decreased generalizibiability of reported prevalence. Moreover, definition of some cardio- metabolic risk factors in the included primary studies was heterogeneous which the pooled prevalence might be limited by the different definitions.

Conclusions

The overall estimated prevalence of some cardio-metabolic risk factors was estimated to be higher in military personnel. Therefore, this study provides strong evidence to the military healthcare providers’ and policy makers for devising and implementing feasible interventions in order to control risk factors in this occupation. Moreover, further studies are needed to identify associated risk factors and reveal best predictors of high-risk subpopulation.

Notes

Acknowledgments

Not applicable.

Authors’ contribution

M.Q., F.B., and OCJ conceived and designed the review. F.B., SH.J., and AMG participated in literature review and data extraction. F.B., AMG and H.A., participated in data extraction, interpretation of the results and drafting the manuscript. M. Q participated in data analysis and interpretation of the results. K. H revised the manuscript. All the authors approved the final version of the manuscript submitted for publication.

Funding

This study was funded by Alborz University of Medical Sciences.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interest.

Supplementary material

12902_2020_489_MOESM1_ESM.docx (14 kb)
Additional file 1. Search strategy.

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© The Author(s). 2020

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Fereshteh Baygi
    • 1
  • Kimmo Herttua
    • 1
  • Olaf Chresten Jensen
    • 1
  • Shirin Djalalinia
    • 2
    • 3
  • Armita Mahdavi Ghorabi
    • 2
  • Hamid Asayesh
    • 4
  • Mostafa Qorbani
    • 5
    • 6
    Email author
  1. 1.Center of Maritime Health and Society, Department of Public HealthUniversity of Southern DenmarkEsbjergDenmark
  2. 2.Non-communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences InstituteTehran University of Medical SciencesTehranIran
  3. 3.Deputy of Research and Technology, Ministry of Health and Medical EducationTehranIran
  4. 4.Department of Medical emergencyQom University of Medical SciencesQomIran
  5. 5.Non-communicable Diseases Research CenterAlborz University of Medical SciencesKarajIran
  6. 6.Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences InstituteTehran University of Medical SciencesTehranIran

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