Regional and demographic variations of Carotid artery Intima and Media Thickness (CIMT): A Systematic review and meta-analysis.

BACKGROUND AND
OBJECTIVE: Carotid artery intima media thickness (CIMT) is a strong predictor of Coronary Heart Disease (CHD) and independent phenotype of early atherosclerosis. The global variation of CIMT and its demographic association is yet unclear. We evaluated regional variations of CIMT based on WHO regions and assessed the differences by age and sex.
METHODS: A systematic search was conducted on studies published between 1980 January up to December 2020. PubMed, Oxford Medicine Online, EBSCO, Taylor & Francis, Oxford University Press and Embase data bases were used for searching. Supplementary searches were conducted on the Web of Science and Google Scholar. Grey literature was searched in "Open Grey" website. The two major criteria used were "adults" and "carotid intima media". The search strategy for PubMed was created first and then adapted for the Oxford Medicine Online, EBSCO, Taylor & Francis, Oxford University Press and Embase databases. Covidence software (Veritas Health Innovation, Melbourne, Australia; http://www.covidence.org) was used to manage the study selection process. Meta-analyses were done using the random-effects model. An I2 ≥ 50% or p< 0:05 were considered to indicate significant heterogeneity.
RESULTS: Of 2847 potential articles, 46 eligible articles were included in the review contributing data for 49 381 individuals (mean age: 55.6 years, male: 55.8%). The pooled mean CIMT for the non-CHD group was 0.65mm (95%CI: 0.62-0.69). There was a significant difference in the mean CIMT between regions (p = 0.04). Countries in the African (0.72mm), American (0.71mm) and European (0.71mm) regions had a higher pooled mean CIMT compared to those in the South East Asian (0.62mm), West Pacific (0.60mm) and Eastern Mediterranean (0.60mm) regions. Males had a higher pooled mean CIMT of 0.06mm than females in the non CHD group (p = 0.001); there were also regional differences. The CHD group had a significantly higher mean CIMT than the non-CHD group (difference = 0.23mm, p = 0.001) with regional variations. Carotid artery segment-specific-CIMT variations are present in this population. Older persons and those having CHD group had significantly thicker CIMTs.
CONCLUSIONS: CIMT varies according to region, age, sex and whether a person having CHD. There are significant regional differences of mean CIMT between CHD and non-CHD groups. Segment specific CIMT variations exist among regions. There is an association between CHD and CIMT values.

Introduction& rationale

The global burden of non-communicable diseases (NCD)varies between developed and developing countries showing regional differences [1-4]. NCDs are the leading cause of death and disability worldwide. In 2005, NCDs caused an estimated 35 million deaths comprising 60% of all deaths globally; 80% of these deaths were in low income and middle-income countries [5, 6]. NCDs are inextricably linked to many modifiable and non-modifiable risk factors [1, 7–9]. Coronary heart disease (CHD) is the leading cause of premature deaths [10-12].

An accurate, non-invasive, convenient and low-cost screening tool to detect CHD is needed for mass screening of at-risk population. The Carotid intima-media thickness (CIMT) is a reliable, non-invasive indicator which predicts the risk of coronary artery disease (CAD) and is widely used in practice as an inexpensive, reliable, non-radiation and reproducible method [13-19].

CIMT is mostly associated with traditional cardiovascular risk factors such as age, sex and race [20-22]. Smoking, alcohol consumption, lack of exercise, high blood pressure, dyslipidemia, poor dietary patterns, risk-lowering drug therapy, glycemia, hyperuricemia, obesity-related anthropometric parameters and obesity-related diseases increase CIMT [23-25]. Traditional risk factors do not explain all of the risk of CHD. It has been reported that more than 60% of CHD cases were not explained by demographic and traditional cardiovascular risk factors [26]. This may probably be due to the effects of novel risk factors such as heredity, presence of certain genotypes, immunological diseases, inflammatory cytokines and hematological parameters [27-30].

Majority of research on CIMT and its association with future risk of cardiovascular disease (CVD) independent of conventional risk factors has been done in Western populations. Only one study has been conducted in Asia in a Japanese population with a limited sample size [31]. Literature suggests that using CIMT cut-off values of western populations for risk prediction of Asians may not be appropriate [32]. CIMT values are strongly affected by age, sex and population [33]. Therefore, CIMT cut-offs are needed for its clinical use as a screening tool to predict future cardiovascular risk [33]. The manner in which CIMT is assessed and the definitions used are still not universally defined [16, 34, 35].

It is not possible to review CIMT values for each country as such values are not available for many countries. Therefore, we reviewed available literature by WHO region, assuming that populations within the region are more homogenous, to derive potential CIMT cut-off values by age and sex that may be used by different countries in the regions.

Method and analysis

We followed guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements, the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines, and methods outlined in the Cochrane Handbook for Systematic Reviews of Interventions [36-38] to conduct this review and meta-analysis.

Eligibility criteria

Study designs

Studies of observational and interventional research were included. The following study designs having adults with a mean age of 40 years and above, with or without CHD were considered: longitudinal, case–control, nested case-control and cross-sectional studies. Case reports, case series, opinion papers, letters to the editor, comments, conference proceedings, review articles, policy papers and meta-analyses were excluded from the analysis. Animal studies, non-English manuscripts and study protocols without baseline data were excluded. The outcome measure was the intima-media thickness of the carotid artery measured by ultrasonography. There was no restriction by time duration of follow-up or observation.

Setting

Data from all countries were considered. There was no restriction by type of setting. The countries were later categorized into WHO regions.

The six WHO regions are 1) African Region (AFR); 2) Eastern Mediterranean Region (EMR); 3) European Region (EUR); 4) Region of the Americas (PAHO); 5) South-East Asia Region (SEAR); and 6) Western Pacific Region (WPR) (40).

Search strategy

Potential articles were systematically searched in the following electronic databases; PubMed, Oxford Medicine Online, EBSCO, Taylor & Francis, Oxford University Press and Embase for publications between January 1980 to December 2020. Supplementary searches were done on Web of Science and Google Scholar. Grey literature was searched in “OpenGrey” website using two criteria “adults” and “carotid intima media”. The search strategy for PubMed was created first and then adapted for the Oxford Medicine Online, EBSCO, Taylor & Francis, Oxford University Press and Embase data bases (S1 File). The references of these selected articles were hand-searched for more relevant articles.

Study selection

After removing duplicates and obviously unrelated articles, the titles and abstracts were screened against pre-specified criteria by two independent reviewers. Pre-determined inclusion criteria were based on the following key words: “carotid intima media thickness”, "coronary heart disease”, “healthy adults”, “adults with coronary heart disease”, and “studies in English language”. Exclusion criteria included “children”, “paediatric”, “any person with a history of stroke or TIA”, “history of malignancy”, “who has undergone carotid end arterectomy”, “history of connective tissue disease”, “history of an ongoing infection”, “studies on cadaver or corpse”, “studies on animals”, “other languages”, “meta–analysis”, “reviews”, and “letters to editor”. Discrepancies were resolved through discussion. If consensus was not reached, arbitration was done with a third reviewer. Full text articles were assessed for eligibility. The systematic reviews software Covidence (Veritas Health Innovation, Melbourne, Australia; http://www.covidence.org) was used to manage the study selection process.

Data extraction

The following data were extracted: name of first author; year of publication; country (according to WHO regions), study design, number of patients, age, proportion of males and females, number of CHD and non-CHD persons, segment measured, measurement protocol, risk factors, mean and maximum values of CIMT. Two authors independent of each other extracted data. Disagreements were resolved by discussion or, if necessary, with the arbitration of a third reviewer. Calibration exercises were conducted before this review stage to enhance consistency between assessors. The study team collated information provided in multiple reports of the same study. For articles on the same population, the more comprehensive one was selected. Apart from inclusion and exclusion criteria, authors selected studies with adjusted CIMT values and study quality assessment statements were considered. When CIMT measurements were available for several time points, the time point closest to the end of the intervention or the follow-up period was selected for data extraction. When essential information was missing from the published reports, the principal investigator contacted the authors of the original studies by email or through “Research gate” to request for missing data. A maximum of two email attempts per study was made.

Study quality

The quality of selected studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) criteria [39], the “STROBE statement” quality assessment tool and “The Newcastle-Ottawa Scale” were used to assess quality and heterogeneity of case control, cross sectional and cohort studies, and risk of bias [40]. Quality appraisal was performed independently by two reviewers. The protocol of ultrasound measurement of CIMT and reliability was assessed based on “A Consensus Statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force” [41].

Data analysis

Data analysis was carried out using STATA version 16 (Stata Corp. 2019. Stata Statistical Software: Release 16. College Station, TX: Stata Corp LLC).

Measures of association

Differences in CIMT by age, sex and selected risk factors in countries between WHO regions.

Descriptive analyses

The characteristics of the study population including details of publication, country, WHO region, age, gender, sample size, measurement site, CIMT assessment, ultrasound protocol and process, identified risk factors, factors adjusted for and adjusted predictors of CIMT in each study are presented in the text and as tables.

Steps of meta-analyses

The mean CIMT were pooled according to WHO regions. Based on the literature we expected to have heterogeneity between the pooled data [16, 41–44]. Therefore, meta-analyses were done using random-effects models with inverse variance-weighted average. Results are presented graphically as forest plots. Meta regression analysis of CIMT values was conducted with and without adjusting for coronary heart disease status, region, mean age and ultrasound technique used.

Assessment of heterogeneity of studies

Heterogeneity was tested using the Cochran’s Q test and quantified using the I2 [38]. An I2 ≥ 50% or p< 0.05 was considered as indicating significant heterogeneity [45]. Sensitivity analyses were carried out by excluding studies with relatively small sample sizes and low-quality studies based on the scores of QUADAS-2 criteria, “STROBE statement “and “The Newcastle-Ottawa Scale”.

Assessment of strength of evidence

Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) criteria, “STROBE statement”and “The Newcastle-Ottawa Scale” were applied to evaluate the quality of the included articles [39]. QUADAS-2 criteria assess the strength of evidence by categorizing studies into low risk, high risk and unclear based on patient selection, index test, and reference standard, flow and timing domains. The “STROBE statement” checklist consists of 22 items that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. Eighteen items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. The primary outcome was the STROBE score, defined as the number of the 22 STROBE items adequately reported divided by the number of applicable items, expressed as a percentage [46, 47]. Publication quality grades of STROBE score are as follows: excellent (more than 85%), good (85 to 70%), fair (70 to 50%) and poor (less than 50%). The Newcastle-Ottawa Scale considers study selection, comparability and outcome categories when assessing the quality of selected studies. The points are considered as follows: 4 points for selection, 2 points for comparability, and 3 points for outcomes. Study quality was categorized according to total points obtained by each study (very good [9], good [7-8], satisfactory [5-6] and unsatisfactory [0-4] [48, 49]).

Results

2847 [(records identified through data bases: n = 2647; published Literature (PL): 2502(94.5%); grey literature (GL): 145(5.5%) and records identified through other sources (n = 200); PL: 192(96%); GL: 8(4%))] relevant articles were obtained; 93 records were duplicates and were removed (Fig 1). The abstract and titles were screened, and 2201 articles were removed due to different populations, disease outcomes and study designs, other methods of CIMT measurement, animal studies and non-English publications. Full texts of the remaining 553 publications were evaluated for eligibility (n = 553, published literature: 549(99.2%); grey literature: 4(0.8%)). From the review of the full texts, an additional 507 articles were removed due to different study designs, study populations, outcomes and settings, insufficient data and paediatric population. Finally, 46 eligible articles were reviewed [PubMed: 11(23.9%), EBSCO: 9(19.6%), Taylor & Francis 9(19.6%), Embase 7(15.2%), Oxford Medicine6 (13.0%), Oxford University Press 4(8.7%)] (Fig 1).

Fig 1

PRISMA flow diagram.

Abbreviation: PL: publish literature, GL: Grey literature.

Two independent reviewers conducted the full text review. The agreement between the two reviewers was 90% with a Cohen’s kappa of 0.733. All the studies were evaluated using QUADAS-2, “STROBE statement” and “The Newcastle-Ottawa Scale” for cross sectional, case control and cohort studies, respectively. QUADAS-2 risk of bias and applicability of the selected studies is shown in Fig 2. The percentages of low-risk studies based on patient selection, index test, reference standard and flow and timing domains were 93.5%, 84.7%, 65.2% and 65.2%, respectively. In the applicability category, it was 64.4% for patient selection, 82.7% for index test and 52.2% for reference standard.

Fig 2

QUADAS-2 risk of bias and applicability of selected studies.

91.3% (42/46) of the studies fulfilled the criteria of the STROBE statement (S1 Table). The Newcastle–Ottawa scale was used to assess the quality of selected studies. Average total quality score for Newcastle–Ottawa scale of cross sectional, case control and cohort studies were7, 7 and 8, respectively (S2–S4 Tables, respectively).

Table 1 provides an overview of the 46 studies included in the systematic review and meta-analyses. The studies were categorized based on the countries they were conducted in according to WHO regions: African Region (AFR) had6(13%) studies; Eastern Mediterranean Region (EMR) had 4(8%); European Region (EUR) had 12(26%); Region of the Americas (PAHO) had8(17%); South-East Asia Region (SEAR) had 7(15%); and the Western Pacific Region (WPR) had 9(20%) studies. There were 24(52%) cross sectional studies, 20(445%) case control studies, 01(2%) prospective study and 01(2%) retrospective cohort study included in the systematic review. There was heterogeneity when measuring the CIMT value among the studies. The commonest segment measured was the far wall of the common carotid artery (CCA) (both sides) (19/46 = 41%), followed by the far wall of CCA, carotid bulb (CB) and internal carotid artery (ICA) (both sides) (6/46 = 13%) and the far and near walls of CCA, CB and ICA (both sides) (4/46 = 9%). The most common IMT definition used was mean CIMT (30/46 = 65%). Definition of plaque was reported in 58% of studies (27/46). ECG gating at acquisition was reported in 28%(13/46) of studies. All studies had used a linear transducer with the frequency varying from 3MHz to 15MHz. Only five studies used Digital Imaging and Communications in Medicine (DICOM) software. Traditional modifiable risk factors were the commonest predictors of CIMT (21/46 = 45.6%) followed by non-modifiable risk factors of age and gender (13/46 = 28%). Three studies reported age as a single predictor of CIMT (3/46 = 7%). One study reported air pollution as a risk factor for CIMT. Three studies reported socio-economic status as a predictor of CIMT. HIV infection, CRP levels and metabolic syndrome were reported as predictors of CIMT in a few studies. Only one study reported that none of the traditional risk factors predicted CIMT.

Table 1

Summary of studies used in systematic review and meta-analysis, reporting demography, IMT measurement protocols and predictors of CIMT.

Publication	Country	WHO regions	Design	Sample size	Mean age (years)	Male N, %	Carotid segments	IMT definition	Definition of plaque	Ultrasound scan specifications	ECG gating at acquisition	Factors adjusted for
Denise et al. 2018 [50]	Nigeria	AFRO	Cross-sectional study	100	58.3	N = 44, 44.0%	CCA, CB, ICA, both sides, far wall	Mean CIMT	Reported	DICOM- Not used, Transducer- Linear:7.5 -10MHz, Edge detection: Not used	Not used	Age, gender, smoking, BMI and hypertension
Ayoola et al. 2015 [51]	Nigeria	AFRO	Case control study	100	54.9	N = 50, 50.0%	CCA, both sides, far wall	Mean CIMT	Reported	DICOM- Not used, Transducer- Linear:7.5-10MHz, Edge detection: Not used	Not used	Hypertension, gender, FBS dyslipidemia
Ofonime et al. 2019 [52]	Nigeria	AFRO	Cross-sectional study	122	52.7	N = 36, 29.5%	CCA, CB, ICA, both sides, far wall	Mean CIMT	Not reported	DICOM- Not used, Transducer- Linear:7.5-10MHz, Edge detection: Not used	Not used	Age, DBP, gender, Family history of heart disease, BMI, Physical activity, Waist circumference and SBP
Okeahialam et al. 2011 [53]	Nigeria	AFRO	Cross-sectional study	71	50	N = 35, 49.3%	CCA, both sides, far wall	Mean CIMT	Not reported	DICOM- Not used, Transducer- Linear:7.5MHz, Edge detection: Not used	Not used	Gender, Diabetes and hypertension
Zaiboonnisa et al. 2009 [54]	South Africa	AFRO	Prospective study	53	47.1	N = 41, 77.3%	CCA, CB, ICA, both sides, far wall	Mean and maximum CIMT	Reported	DICOM- Not used, Transducer- Linear:11MHz, Edge detection: Not used	Not used	Age
Nonterah et al. 2018 [55]	Sub-Saharan Africa	AFRO	Cross-sectional study	8872	49.87	N = 4507, 50.8%	CCA, both sides, far wall	Mean CIMT	Not reported	DICOM- Not used, Transducer- Linear:7.5-10MHz, Edge detection: Not used	Not used	Gender, BMI, TRF, Socio-economic factors and HIV
Kamran et al. 2014 [56]	Iran	EMRO	Case control study	500	60	N = 287, 57.4%	CCA, both sides	Mean and maximum CIMT	Not reported	DICOM- Not used, Transducer- Linear:7.5-15MHz, Edge detection: Not used	Not used	Age, gender, Hypertension, smoking, and Hyperlipidemia
Pourafkari et al. 2006 [57]	Iran	EMRO	Cross-sectional study	113	44	NR	CCA and ICA, both sides	Mean CIMT	Not reported	DICOM- Not used, Transducer- Linear:7.5-15MHz, Edge detection: Not used	Not used	Age and gender
Mirza et al. 2017 [58]	Pakistan	EMRO	Cross-sectional study	257	45	N = 97,38%	CCA and ICA, both sides	Mean CIMT	Not reported	DICOM- Not used, Transducer- Linear: NR, Edge detection: Used	Not used	Age, diabetes, and gender
Mustafa et al. 2013 [59]	Sudan	EMRO	Cross-sectional study	11	41.6	N = 6,54.5%	CCA, both sides, far wall	Mean CIMT	Not reported	DICOM- Not used, Transducer- Linear:6MHz, Edge detection: Not used	Not used	Age, smoking, and gender
Haghi et al. 2005 [60]	Germany	EURO	Case control study	151	61.5	N = 120,79.5%	CCA, both sides, far wall.	Mean CIMT	Reported	DICOM- Not used, Transducer- Linear:7.5MHz, Edge detection: Not used	Not used	Age and gender
Kotsis et al. 2005 [61]	Greece	EURO	Cross-sectional study	390	61.2	N = 345,88.5%	CCA and ICA, both sides, far wall.	Mean CIMT	Not reported	DICOM- Not used, Transducer- Linear:7MHz, Edge detection: Used	Not used	Age, alcoholic, and gender
Mauro Amato et al. 2007 [62]	Italy	EURO	Cross-sectional study	48	61	N = 36,75%	CCA, CB and ICA, both sides, far and near wall.	Mean CIMT	Not reported	DICOM- Not used, Transducer- Linear:6.7MHz, Edge detection: Not used	Not used	NR
Del Sol et al. 2001 [63]	Netherlands	EURO	Case control study	1690	71	N = 686,40.6%	CCA, CB and ICA, both sides, far and near wall.	Mean of Max. CIMT	Reported	DICOM- Not used, Transducer- Linear:7.5MHz, Edge detection: Used	Used	NR
Ziembicka et al. 2005 [64]	Poland	EURO	Cross-sectional study	558	57.5	N = 438,78.5%	CCA, CB and ICA, both sides, far and near wall.	Mean of Max. CIMT	Not reported	DICOM- Not used, Transducer- Linear:5-10MHz, Edge detection: Used	Used	Age, gender, hypertension, smoking, alcoholic, FBS diabetes and Obesity
Lisowska et al. 2009 [65]	Poland	EURO	Case control study	231	49	NR	CCA and CB, both sides, far wall.	Mean CIMT	Reported	DICOM- Not used, Transducer- Linear:3-11MHz, Edge detection: Not used	Not used	Age, gender, diabetes, dyslipidemia, and GFR
Timo´ teo et al. 2013 [66]	Portugal	EURO	Case control study	300	64.5	N = 176, 58.7%	CCA, both sides, far wall.	Mean of Max. CIMT	Reported	DICOM- Not used, Transducer- Linear:7.5MHz, Edge detection: Not used	Not used	Gender
Sait et al. 2003 [67]	Turkey	EURO	Case control study	233	59	N = 131,56.2%	CCA, both sides, far wall.	Mean CIMT	Reported	DICOM- Not used, Transducer- Linear:7.5MHz, Edge detection: Not used	Not used	Age, SBP, smoking, alcoholic, diabetes mellitus and total cholesterol
SelcanKoc et al. 2019 [68]	Turkey	EURO	Retrospective study	644	54.6	N = 314,48.5%	CCA and ICA, both sides, far wall.	Mean of Max. CIMT	Reported	DICOM- Not used, Transducer- Linear:5-12MHz, Edge detection: Used	Not used	Age, gender and SBP, FBS
Mehmet et al. 2006 [69]	Turkey	EURO	Case control study	144	53.2	N = 87, 60.4%	CCA and CB, both sides, far wall.	Mean of Max. CIMT	Reported	DICOM- Not used, Transducer- Linear:NR, Edge detection: Not used	Not used	NR
Geroulakos et al. 1994 [70]	UK	EURO	Case control study	122	58	NR	CCA, both sides, far wall.	Mean CIMT	Not reported	DICOM- Not used, Transducer- Linear:7.5MHz, Edge detection: Not used	Not used	NR
Ebrahim et al. 1999 [71]	UK	EURO	Cross-sectional study	800	66	N = 425,53.1%	CCA and CB, both sides, far wall.	Mean of Max. CIMT	Reported	DICOM- Not used, Transducer- Linear:7MHz, Edge detection: Used	Used	Age, gender, Alcohol, smoking, BMI, hypertension, FBS and social class
Alejandro et al. 2018 [72]	Argentina	PAHO	Cross-sectional study	1012	42	N = 621, 61.36%	CCA, ICA and ECA, both sides, far wall	Mean CIMT	Reported	DICOM- Not used, Transducer- Linear:4-13MHz, Edge detection: Used	Used	Gender, FBS, SBP, MBP, DBP, and PP
Rosa et al. 2003 [73]	Brazil	PAHO	Case control study	58	50.1	N = 32,55.2%	CCA: both sides, far walls	Mean CIMT	Not reported	DICOM- Not used, Transducer- Linear:5MHz, Edge detection: Used	Not used	Alcoholic, Smoking, dyslipidemia
Amer et al. 2016 [74]	Canada	PAHO	Case control study	318	64	N = 128, 40.3%	CCA, CB, ICA, both sides, far and near wall	Mean CIMT	Reported	DICOM- Used, Transducer- Linear:7.5MHz, Edge detection: Used	Used	Age
Catherine et al. 2010 [75]	USA	PAHO	Cross-sectional study	472	52.4	N = 214,45.3%	CCA Both sides, Far wall	Mean CIMT	Reported	DICOM- Not used, Transducer- Linear:NR, Edge detection: Used	Used	Age, gender, FBS, diabetes mellitus, dyslipidemia, and smoking
Polak et al. 2011 [76]	USA	PAHO	Case control study	2965	60.1	N = 1336, 45.1%	CCA, both sides, far wall	Mean CIMT	Reported	DICOM- Not used, Transducer- Linear:12MHz, Edge detection: Used	Used	NR
Cao et al. 2007 [77]	USA	PAHO	Cross-sectional study	5020	72.6	N = 2008,40%	CCA and ICA: near and far walls on both sides.	Mean and Maximum CIMT	Reported	DICOM- Not used, Transducer- Linear:7-10MHz, Edge detection: Not used	Not used	Age, gender, CRP levels
Chambless et al. 1997 [78]	USA	PAHO	Case control study	12841	55.3	N = 5552,43.2%	CCA, CB, ICA, both sides, far wall	Mean CIMT	Not reported	DICOM- Not used, Transducer- Linear:NR, Edge detection: Not used	Not used	Age, race, gender FBS, diabetes, LDL, HDL, hypertension, smoking status
Hensley et al. 2020 [79]	USA	PAHO	Case control study	58	60	N = 39,67.2%	CCA: both sides, far walls	Mean and Maximum CIMT	Reported	DICOM- Not used, Transducer- Linear:7.5MHz, Edge detection: Used	Not used	NR
Gupta et al. 2003 [80]	India	SEAR	Case control study	241	47.2	N = 205, 85.1%	CCA, CB and ICA, both sides, far wall	Mean and Maximum CIMT	Reported	DICOM- Not used, Transducer- Linear:7.5MHz, Edge detection: Not used	Used	Age and gender
Sudhir et al. 2018 [81]	India	SEAR	Case control study	200	43.1	NR	CCA and ICA, both sides	Mean CIMT	Not reported	DICOM- Not used, Transducer- Linear:5-12MHz, Edge detection: Not used	Not used	Age
Agarwal et al. 2008 [82]	India	SEAR	Case control study	111	59.2	N = 66, 59.4%	CCA, both sides, far wall	Mean CIMT	Reported	DICOM- Not used, Transducer- Linear:7.5MHz, Edge detection: Not used	Not used	NR
Kasliwal et al. 2016 [83]	India	SEAR	Cross-sectional study	818	43	N = 438, 53.5%	CCA, both sides, far wall	Mean CIMT	Reported	DICOM- Used, Transducer- Linear:7.5MHz, Edge detection: Used	Used	Age, SBP, FBS, BMI, DBP and serum triglycerides
Paul et al. 2012 [15]	India and Bangladesh	SEAR	Cross-sectional study	96	44.34	N = 53,55.2%	CCA and ICA, both sides, far wall	Mean CIMT	Reported	DICOM- Not used, Transducer- Linear:7.5MHz, Edge detection: Not used	Used	Age and gender
Rinambaan et al. 2016 [84]	Indonesia	SEAR	Cross-sectional study	356	56	N = 236, 66.3%	CCA, both sides, Near and far wall	Mean and Maximum CIMT	Reported	DICOM- Not used, Transducer- Linear:7.5-10MHz, Edge detection: Not used	Not used	Age, triglyceride levels had association. But Weight, BMI, Waist circumference, Glucose, LDL-c, HDL-c.
Barakoti et al. 2016 [85]	Nepal	SEAR	Case control study	104	55.1	N = 59, 56.7%	CCA, both sides, far wall	Mean CIMT	Not reported	DICOM- Not used, Transducer- Linear:10MHz, Edge detection: Not used	Not used	NR
Adams et al. 1995 [86]	Australia	WPR	Cross-sectional study	350	60	N = 249,71%	CCA, both sides, far wall.	Mean and Maximum CIMT	Reported	DICOM- Not used, Transducer- Linear:7MHz, Edge detection: Not used	Used	NR
Bin Liu et al. 2017 [87]	China	WPRO	Cross-sectional study	3789	58.8	N = 1560,41.2%	CCA, Both sides, far and near wall.	Mean CIMT	Not reported	DICOM- Not used, Transducer- Linear:5-12MHz, Edge detection: Used	Not used	Age, gender, low education level, smoking, hypertension, SBP, FBS and LDL-c
Xuefang et al. 2020 [88]	China	WPR	Cross-sectional study	1039	72.3	N = 498,47.9%	CCA, both sides, far and near wall.	Mean CIMT	Reported	DICOM-Used, Transducer- Linear:5-12MHz, Edge detection: Used	Not used	Age, gender and hypertension, FBS
Fujihara et al. 2014 [89]	Japan	WPR	Case control study	116	60.5	N = 78,67.2%	CCA, both sides, far wall.	Mean and Maximum CIMT	Reported	DICOM- Not used, Transducer- Linear:7.5MHz, Edge detection: Used	Not used	NR
Matsushima et al. 2004 [90]	Japan	WPR	Case control study	103	62	N = 71, 68.9%	CCA not mentioned sides and wall	Mean CIMT	Not reported	DICOM- Used, Transducer- Linear:7.5MHz, Edge detection: Used	Not used	Age, BMI, SBP, DBP, HDL-c, LDL-c and HbA1C
Young-Hoon et al. 2014 [91]	Korea	WPR	Cross-sectional study	2595	58.7	N = 713,27.5%	CCA and CB, both sides far wall.	Mean CIMT	Reported	DICOM- Not used, Transducer- Linear:7.5 MHz, Edge detection: Used	Not used	Age, Metabolic syndrome
Young Jin et al. 2011 [92]	Korea	WPR	Cross-sectional study	433	55	N = 107,24.7%	CCA, both sides, far wall	Mean CIMT	Not reported	DICOM- Not used, Transducer- Linear:NR, Edge detection: Not used	Used	Age, gender, BMI, LDL-C level and history of diabetes mellitus.
Chua et al. 2014 [93]	Malaysia	WPR	Cross-sectional study	123	55	N = 74,60.2%	CCA, both sides, far and near wall.	Mean and Maximum CIMT	Not reported	DICOM- Not used, Transducer- Linear:13MHz, Edge detection: Used	Not used	Age, TC and LDL-c
Ta-Chen et al. 2015 [94]	Taiwan	WPR	Cross-sectional study	689	51	N = 497,72.1%	CCA, CB, ICA, both sides, far wall	Mean and Maximum CIMT	Reported	DICOM- Used, Transducer- Linear:3.5-10MHz, Edge detection: Used	Used	Age, gender, diabetes and air pollution

AFR: African Region, EMRO: Eastern Mediterranean Region, EUR: European Region, PAHO: Region of the Americas, SEAR: South-East Asia Region, WPR: Western Pacific Region, CCA: Common carotid artery, CB: Carotid bulb, ICA: internal carotid artery, ECA: External carotid artery, IMT: Intima-media thickness, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, PP: Pulse pressure, FBS: Fasting blood sugar, TC: Total cholesterol, LDL-c: Low-density lipoprotein cholesterol, HDL-c: High-density lipoprotein cholesterol, HbA1C:, CRP: C-reactive protein, GFR: Glomerular filtration rate, TRF: Traditional risk factors, BMI: Body mass index, HIV: human immunodeficiency virus, DICOM: Digital Imaging and Communications in Medicine, NR: Not reported.

Table 2 shows the mean CIMT values of different carotid segments by WHO region in the non-CHD and CHD groups. The mean CIMT values of CCA vary from 0.58±0.09mm to 0.74±0.11mm. The mean CIMT of CB ranges from 0.65±0.08mm to 0.81±0.09mm. The range for the mean CIMT of ICA was 0.65±0.10mm to 0.69±0.06. In each region, the highest mean CIMT value was in the CB followed by the CCA and the ICA. The highest mean CIMT value of CCA of 0.74±0.11mm was in EUR countries. The mean CIMT values of CCA in SEAR and WPR countries were significantly different from those of countries from AFR, EMR, EUR and PAHO regions (P<0.01). There were significant differences in the mean CIMT values of CB between the regions (P<0.01). The mean CIMT value of ICA was significantly higher in countries in AFR in comparison to countries EUR and PAHO (P<0.01).

Table 2

The mean CIMT values of different carotid segments by WHO region and CHD group.

Segment	CCA				CB				ICA
Group	Non-CHD		CHD		Non-CHD		CHD		Non-CHD		CHD
WHO region	N	Mean±SD (mm)	N	Mean±SD (mm)	N	Mean±SD (mm)	N	Mean±SD (mm)	N	Mean±SD (mm)	N	Mean±SD (mm)
AFR	9244	0.70±0.08a	4380	0.92±0.13*	8994	0.75±0.06a		NR	8994	0.69±0.06a		NR
EMR	870	0.58±0.09b	261	0.86±0.26**	500	0.71±0.12b		NR		NR		NR
EUR	4668	0.74±0.11c	698	0.92±0.20*	877	0.77±0.11c	145	0.93±0.19*	464	0.66±0.11b	255	0.86±0.16
PAHO	22628	0.71±0.07d	839	0.89±0.15***	8457	0.81±0.09d	1798	0.93±0.16*	1802	0.65±0.10b	720	0.87±0.17
SEAR	1004	0.62±0.10e	251	0.87±0.21**		NR		NR		NR		NR
WPR	8215	0.61±0.06e	1391	0.87±0.16**	239	0.65±0.08e	239	0.89±0.19**		NR		NR

AFR: African Region, EMR: Eastern Mediterranean Region, EUR: European Region, PAHO: Region of the Americas, SEAR: South-East Asia Region, WPR: Western Pacific Region, CCA: Common carotid artery, CB: Carotid bulb, ICA: internal carotid artery, NR: Not reported. N: Number of participants.

Note: The pooled mean was calculated weighting the studies on sample size.

a,b,c,d,eMeans having a superscript with the same letter are similar (Non-CHD group).

*, **, *** Means having a superscript with the same letter are similar (CHD group).

The mean CIMT values of CCA vary from 0.86±0.26mm to 0.92±0.20mm. The mean CIMT of CB ranges from 0.89±0.19mm to 0.93±0.19mm. The range for the mean CIMT of ICA was 0.86±0.16mm to 0.87±0.17. In each region, the highest mean CIMT value was in the CB. The highest CIMT values of CCA were reported in AFR and EUR countries. The mean CIMT values of CCA in EMR, SEAR and WPR countries were significantly different from those of countries from AFR, EUR and PAHO regions (P<0.01). There were significant differences in the mean CIMT values of CB in WPR countries in comparison to EUR and PAHO countries (P<0.01). The mean CIMT value of ICA was not significantly different in EUR and PAHO (t-test: 0.819; df: 973; p = 0.793) (Table 2).

Meta-analysis

The pooled mean CIMT value for healthy persons in all regions was 0.65mm (95%CI–0.62–0.69; I2 = 13.79%) (Fig 3). There was a significant difference in the mean CIMT values between the regions (Test of group difference, Q(40) = 11.51, P = 0.04). Subgroup analyses show no significant difference of mean CIMT values within the regions. Countries in AFR, (0.72mm), PAHO (0.71mm) and EUR (0.71mm) had a higher pooled mean CIMT compared to countries in SEAR (0.62mm), WPR (0.60mm) and EMR (0.60mm) (Fig 4). The pooled mean CIMT values were significantly different between different age groups (Q(3) = 19.32, P<0.001) (Fig 4).

Fig 3

Forest plot of the mean CIMT for healthy persons by WHO regions.

Fig 4

Summary of mean CIMT values by age and WHO region in healthy persons.

The pooled mean CIMT difference between healthy males and females was 0.06mm (95%CI: 0.04–0.07). There were differences in the mean CIMT between males and females within regions (AFR: 0.04mm, p = 0.04; PAHO: 0.05mm, p<0.001: and WPR: 0.04mmp<0.001) (Fig 5).

Fig 5

Forest plot of mean difference of CIMT between healthy males and females.

There was a significant mean difference of the pooled CIMT values between CHD and non-CHD groups (0.23mm, p = 0.001) (Fig 6). PAHO (I2 = 97.18%, Q(3) = 56.63, p<0.001), SEAR (I2 = 99.22%, Q(3) = 376.54, p<0.001) and EUR (I2 = 78.98%%, Q(4) = 18.13, p<0.001) countries had significant differences in the mean CIMT difference between the CHD and non-CHD groups within the respective region (Fig 6).

Fig 6

Forest plot of mean difference of CIMT between CHD and non-CHD groups.

Table 3 shows the summary of the Meta regression analysis of CIMT values. In the adjusted model, CHD group, WHO region and age were significantly associated with CIMT. The mean CIMT value in the CHD group was 0.214 mm greater than that of the non-CHD group after adjusting for the other variables. The mean CIMT was significantly less among populations in SEAR and WPR as compared to populations from PAHO after adjustment. With age there was a significant increase in the mean CIMT values.

Table 3

Summary of meta regression analysis of CIMT values.

Variable	Unadjusted Regression coefficient	95%CI	Adjusted regression coefficient	95%CI
Age (years)	0.008 *	0.004 to 0.013	0.006 *	0.001 to 0.011
AFR	0.001	-0.121 to 0.122	0.026	-0.112 to 0.175
EMR	-0.141 *	-0.256 to -0.027	-0.064	-0.217 to 0.087
EUR	0.006	-0.096 to 0.109	-0.013	-0.141 to 0.112
SEAR	-0.173 *	-0.279 to -0.067	-0.149 *	-0.287 to -0.012
WPR	-0.107 *	-0.207 to -0.006	-0.117 *	-0.217 to -0.165
Region of the Americas	Reference category
Automatically	-0.050	-0.144 to 0.044	0.016	-0.067 to 0.099
Automatically with ECG gating	-0.023	-0.142 to 0.094	0.018	-0.097 to 0.133
Manual ultrasound technique	Reference category
CHD group	0.228 *	0.153 to 0.304	0.214 *	0.139 to 0.289
Non CHD group	Reference category
Constant			0.578

AFR: African Region, EMR: Eastern Mediterranean Region, EUR: European Region, PAHO: Region of the Americas, SEAR: South-East Asia Region, WPR: Western Pacific Region.

*significant variables.

Discussion

Coronary heart disease (CHD) is the most important cause of morbidity, mortality and premature deaths of NCDs. We included 46 eligible articles comprising data of 49 381 individuals. The highest number of studies was from the European region while the lowest was from the Eastern Mediterranean region.

Modifiable risk factors

45.6% of the studies reviewed showed that modifiable risk factors were predictors of CIMT. There was a significant difference in CIMT values among the non-CHD group between regions. Higher CIMT values were observed in countries in the African, American and European regions. The mean difference in CIMT values between CHD and non-CHD groups were significantly different between and within regions. Differences in the CIMT values between regions may be due to socio-economic status [95, 96], environmental conditions, smoking habits, harmful consumption of alcohol, physical activity, dietary patterns, sedentary behaviors and body mass indices [23, 24, 97–100], and prevalence of co-morbidities such as diabetes, hypertension, dyslipidemia, cancer and chronic kidney disease [101, 102].

Age-adjusted cardiovascular death rates have declined in several developed countries in the past decades. In contrast, the death rates of cardiovascular disease have risen greatly in lower middle income countries [103-105]. Several publications underscore the high burden of disease associated with non-communicable diseases and its economic impact on lower middle income countries [4, 104, 106]. Due to this reason, non-communicable diseases in lower middle income countries have received increasingly more global attention by scientists, public health advocates and policy makers. A recent study has identified that NCDs and CHD risk factors such as demographic transition, environmental pollution, metabolic risk factors, lack of education, unhealthy food habits and unhealthy lifestyles have similar effects in both developed and developing countries [109].

Some studies reported that non-traditional risk factors such as HIV infection, metabolic syndrome, infections and inflammation as predictors of CIMT. Some studies have highlighted that during chronic infections and inflammation, elevated levels of the pro-inflammatory cytokines interleukin (IL)-6 and C-reactive protein (CRP) are associated with subclinical atherosclerosis [107, 108]. Intima-medial thickening is a complex process. Modifiable risk factors contribute in different stages in different proportions. Factors that vary stress and blood pressure, which may cause a local delay in lumen transportation, may lead to the accumulation of potentially atherogenic particles in the arterial wall and stimulate CIM thickening and plaque formation [109]. Risk factors which cause endothelial destruction and functional abnormalities are associated with higher carotid IMT and were associated with a higher risk of atherosclerotic disease [110].

Non- modifiable risk factors

28.5% of the studies we reviewed reported that non-modifiable risk factors such as age and gender are associated with CIMT. The CIMT values of males are significantly higher than that of females (pooled difference of 0.06 mm) across regions. In our meta-analysis there was a significant difference in the pooled mean CIMT values between the age groups with older age groups having higher CIMT values.

Heredity and certain genotypes [27, 28], immunological diseases [111, 112], inflammatory cytokines, hematological parameters [30,112-114] and vitamin D [115] have been reported to be potential risk factors for increased CIMTs. In our review, we did not find these to be risk factors probably due to the specific study designs, study populations and outcomes considered by us.

The Meta regression analysis demonstrated that CIMT values were influenced by WHO region, age and CHD group. Even though there is a clear association between CIMT and CHD its usability as a risk predictor for CHD needs to be further investigated. Approaches to prevention as well as screening of at-risk populations for CHD may need to consider regional variations of CIMT.

Most studies included in this review had not documented the ethnic composition of their samples. Therefore, we were unable to evaluate CIMT variations among different ethnicities. It is reported that healthy UK black African-Caribbean children have higher CIMT levels, not explained by conventional cardiovascular risk markers, as compared to other ethnicities [116]. Ethnicity significantly modifies the associations between risk factors, CIMT values and cardiovascular events [122]; the association between CIMT and age, HDL cholesterol, total cholesterol and smoking was weaker among Blacks and Hispanics [117]. Systolic blood pressure was associated more strongly with CIMT in Asians [117]. These differences could be due to varying interactions between different risk factors and ethnicities. These differences provide insight into the etiology of cardiovascular disease among ethnic groups and aid the ethnic-specific implementation of primary prevention.

Segmental variation of CIMT

We have summarized variations in the mean CIMT values of CCA, CB and ICA within and between regions. These differences may be due to different influences of risk factors on the different segments. A Korean study reported associations between cardiovascular risk factors and different segments of the carotid artery: in men, alcohol use (CIMT at the bifurcation); physical activity (CIMT at the common and internal carotid segments); BMI (CIMT of all segments); diabetes (CIMT at the bifurcation and internal carotid segment); hypertension (CIMT at the internal carotid segment); and HDL-cholesterol (CIMT at the bifurcation and the common carotid segment): in women, smoking (CIMT at the bifurcation), hypertension (CIMT at the common carotid segment), total and LDL cholesterol (CIMT at the bifurcation and internal carotid segment), and hs-CRP (CIMT at the common and internal carotid segments) [118]. Furthermore, the Malmö Diet and Cancer Study (MDCS) reported that HDL was associated with IMT progression in the CCA but not at the bifurcation. The same showed that diabetes was associated with IMT progression at the bifurcation, but not in the CCA [119, 120].

This study summarized that CIMT values of non-CHD population vary among regions. Age and gender have a significant effect on CIMT differences. Furthermore, there were marked differences of mean CIMT values between non-CHD and CHD groups. It was different from region to region as well as within regions.

Ultrasound protocol for CIMT measurement

There were variations in the ultrasound assessment of CIMT. The transducer frequency ranged from 3MHz to 15MHz; five studies used DICOM software. Variations in the ultrasonography protocol are likely to affect CIMT values. There are different arguments with regard to various ultrasound protocols during CIMT measurement [44]. Mannheim Carotid Intima-Media Thickness consensus (2004–2006) is a useful guideline to achieve homogeneity of CIMT measurement among studies [121]. A common protocol will ensure reproducibility and comparison of findings of different studies.

There was no uniformity in the selection of the site for measurement or the reporting of the CIMT measurement. The far wall of CCA (both sides) was the commonest site (41%) selected. The mean CIMT value was reported in 65% of studies. Plaque formation was reported in 59% of studies. It has been reported that this is unlikely to alter the results by much in populations with a low prevalence of plaque [44]. Some studies imaged only one side of the neck, whereas others imaged both sides [122]. Some included imaging of a single segment while multiple segments were imaged in others [77, 123, 124]. Some studies imaged the far wall of multiple segments, whereas others imaged both the near and far walls [125, 126]. Studies also differed in the type of IMT measurements made and the use of different arbitrary cut-off points of CIMT to predict risk. Our review also shows that ECG gating at acquisition was reported only by 28% of studies. The phase of the cardiac cycle (end-systole vs. end-diastole) when CIMT is measured also differs among studies. Because of systolic lumen diameter expansion that leads to thinning of CIMT during systole, CIMT values obtained from end-systole are lower than those obtained in end-diastole [16]. In our meta-analysis we categorized ultrasound technique of measuring CIMT into three categories; manually, automatically and automatically with ECG gating. Literature shows that CIMT measured by General Electric (GE) semi-automated edge-detection software and Artery Measurement semi-automated software (AMS) have significant differences when measuring mean CIMT [127]. Hence, results obtained from different CIMT software systems should be compared with caution. CIMT variations using similar software may be explained by the position/angle of ultrasound transducer, and the specific combinations of segments and walls examined [128]. These factors are associated with differences in reproducibility, magnitude, and precision of progression of CIMT over time. To avoid these discrepancies, it is recommended to measure CIMT in multiple segments with different angles [128]. In our review, we found that most of the studies have obtained an average CIMT value by multiple measurements. This may be a reason that significant differences were not found when multiple segments were examined.

Conclusion

CIMT among the non-CHD group varies between and within regions, and by age and sex. The mean CIMT values between non-CHD and CHD groups were significantly different within and between WHO regions possibly due to varying influences of modifiable and non-modifiable risk factors. CHD group had a significantly thicker mean CIMT after adjusting for age, WHO region and ultrasound machine used. Segment specific CIMT variations exist among regions.

Limitation of study

Our review consisted of few studies with small sample sizes. But Egger’s test showed no significant small study effect in our review. Some studies had large sample sizes which contributed most to our analyses. We were unable to capture some new risk factors such as genetic composition, immune disorders and cytokine’s effect on CIMT due to the selection criteria we used. However, these studies had small sample sizes which may not have been generalizable. It is unlikely that exclusion of these risk factors would have influenced our findings.

The way we grouped countries by WHO regions may not be the most appropriate grouping to consider as WHO regions have been established taking into consideration political considerations as well. For example, the Republic of Korea (South Korea) is in the WPR whereas the Democratic People’s Republic of Korea (North Korea) is in the SEAR. Similarly, Pakistan and Afghanistan, both South Asian countries, are in the EMR, though all other South Asian countries (Bangladesh, Bhutan, India, Maldives, Nepal and Sri Lanka) are in the SEAR together with Myanmar, Thailand, Indonesia and Timor Leste. Therefore, there is a likelihood of ethnic and cultural diversity influencing CIMT values among countries within regions.

Studies included in our review had not specified the ethnic composition of the study samples. Consequently, we were unable to examine CIMT variations by ethnicity. Further studies to explore this variability in future are warranted.

Search strategy for PubMed.

(DOCX)

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Summary of STROBE statement.

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Newcastle–Ottawa Scale for cross sectional study.

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Newcastle–Ottawa Scale for case—Control study.

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Newcastle–Ottawa Scale for Cohort studies.

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PRISMA checklist.

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10 Feb 2022

Regional and demographic variations of Carotid artery Intima and Media Thickness (CIMT): A systemic review and meta-analysis.

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Reviewer #1: In this manuscript Abeysuriya et al. present a meta-analysis on systematic search regional and demographic variations of carotid artery intima and media thickness. In a first step, the authors screened PubMed, Oxford Medicine Online, EBSCO, Taylor & Francis, Oxford University Press and Embase databases (with a supplementary search in Web of Science and Google Scholar) for eligible on carotrid artery intima and media thickness published between January 1980 January up to December 2020. Subsequently, meta-analyses were done using random-effects models.

Of 2847 potential articles, 46 eligible articles were included in the review contributing data for 49 381 individuals. The authors report a significant difference in the mean CIMT between regions, with countries in the African, American and European regions had a higher pooled mean CIMT compared to those in the Southeast Asian, Western Pacific and Eastern Mediterranean regions. Males appeared to have a higher pooled mean CIMT than females in the non-CHD group. The CHD group had a significantly higher mean CIMT than the non-CHD group. Age and region were significant predictors of CIMT among the non-CHD group.

This manuscript presents interesting data, however, several questions remain:

Major comments:

1. Section “Method and analysis”, paragraph “Study selection“: According to this paragraph, titles and abstracts of the search results were screened against pre-specified criteria by two independent reviewers for study selection. Please add a description of the pre-specified criteria for study selection to the text.

2. Section “Method and analysis”, paragraph “Study quality”: The quality of selected studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) criteria. It is important to assess the quality of reports in a consistent manner with other studies. The CONSORT statement and the STROBE statement represent 2 of the most widely accepted and used guidelines for accurate reporting and transparency. Therefore, it is recommended to additionally assess the quality of reports in reference to the CONSORT statement and the STROBE statement prior to including them in the analysis.

3. Section “Method and analysis”, paragraph “Steps of Meta-analyses”: The meta-analyses were performed using random-effects models. Which model was used? Was the model based on inverse variance-weighted average method or weighted sum of z-scores?

4. Section “Method and analysis”, paragraph “Steps of Meta-analyses”: Due to the comparison of highly heterogeneous populations in different WHO regions with very divers individual risk profiles, there is a high chance of confounding. With regard to the extracted CIMT estimates, how did the model of the authors account for the risk of residual confounding? Were the estimates extracted from the primary studies confounder-adjusted or unadjusted data? The extraction process should be clarified. In addition, comparisons of adjusted and crude estimates allow insights into the importance of confounding. To reliably detect independent regional differences as well as potential influencing variables, the random-effects model should be adjusted for all known risk factors for cardiovascular disease, if possible from the data set.

5. Section “Method and analysis”, paragraph “Assessment of heterogeneity of studies”: In addition to the QUADAS-2 criteria, the Newcastle-Ottawa Scale should be applied to minimize risk of bias.

6. Section “Results”: What was the percentage of included subjects from grey literature and from published literature? What proportion of the total number of included patients is derived from each of the databases as a source? These numbers should be added to the results section as well as to the flow diagram in “Figure 1”.

7. Section “Discussion”: The discussion is mainly descriptive with an extended presentation of the results of the meta-analysis and its underlying primary studies. A reflection of possible underlying factors and differential population characteristics of the respective WHO regions would contribute to strengthen the discussion. Furthermore, general text flow and readability of the discussion should be significantly improved.

Minor comments:

1. Section “Abstract”: The search strategy is not clearly described in the abstract. Therefore, adding a summarized description of the search strategy is recommended.

2. Section “Method and analysis”, paragraph “Search strategy”: The search strategy for PubMed should be specified in the text section of the search strategy paragraph. The separate box displaying the paragraph should be removed from the main manuscript.

3. Section “Method and analysis”, paragraph “:Data extraction”: For articles based on the same population, the authors state that the ‘more comprehensive one’ was selected. Please add a precised description of the data extraction criteria.

4. Section “Results”, “Table 3” and “Table 4”: “Table 3” and “Table 4” show mean CIMT values of different carotid segments by WHO region among patients with and without CHD. To provide a more focused illustration of the results, the content of “Table 3” and “Table 4” should be summarized in a single table.

5. Section “Results”: The text flow in the sections results is partially very tough. Therefore, it should be revised to ensure a more fluid presentation of the results.

Reviewer #2: Comments to the authors:

The authors present a systematic review and meta-analysis of differences of carotid intima media thickness (CIMT) in various regions around the globe, based on the WHO definition of regions. Analysis and pooling data of 49 381 patients showed a difference in CIMT between African, American and European population versus Southeast Asian, Western Pacific and Eastern Mediterranean. The authors found significant regional differences of mean CIMT between CHD and non-CHD groups. The authors conclude that CIMT varies according to region, age and sex among the non-CHD group and that there are significant regional differences of mean CIMT between CHD and non-CHD groups. The authors also state that there is a need to develop country-specific CIMT cutoff values to screen at-risk populations for CHD. The following points arose to the reviewers eyes when reading the manuscript:

Major comments:

- Regarding the conclusion, that there is a need to establish country specific CIMT cutoff values, I find it difficult to state this in light of the presented data. For my understanding in this study, differences between countries have not been investigated (since the results are based on WHO regions). As the authors also state in the discussion, there are many factors that affect CIMT, and there are known regional differences in the prevalence of those risk factors (e.g. BMI, hypertension, diabetes). Since reference values are based on studies on a healthy population, it would be interesting to look at geographical differences in comparing healthy cohorts. Is there a possibility to address this? Otherwise I would recommend to rewrite this part of the conclusion.

- Ethnical aspects: It is known that in regards to cardio- and cerebrovascular disease the ethnical background plays a detrimental role. The presented study seems to calculate the values of patients from countries, but irrespective of their ethnicity. Is there also a possibility to analyze the impact of the ethnical background?

- Geographical aspects: There is also a different burden of cardiovascular and cerebrovascular diseases within one WHO region, e.g. Northern Europe versus southern Europe. Some regions also seem to be underrepresented. Authors tried to apply statistical methods relativize this fact and they also comment this in the study limitations section, that WHO classification is also partly based on political aspects as well. Would it not be clinically more reasonable to compare trials of comparable quality using a classification that is only based on geographical aspects (even taking into account not to cover the whole globe)?

- Time span: Authors have reviewed and compared data on CIMT in the time span from 1980 to 2020. Within 40 years, the spatial resolution of ultrasound systems has revolutionized and is still becoming more precise, and therefore the measurements of vessels and their segments are not entirely comparable. Furthermore, there are significant differences in measuring the CIMT manually and automatically. Further factors that have changed over the decades are methodical quality of trials, the quality of trial performance, and the presence of trial audits are not respected in the study and are very likely to influence the outcome. Would it be possible to analyze only studies with a similar technological standard? This would improve the value of this study, since it would minimize bias due to technical and methodical differences.

- The statistical methods seem to be sufficient.

- Search strategy seems to be representative according to selected keywords, but the heterogeneity of population and patients with co-morbidity is not reflected.

Minor Comments:

Line 59: word „diseases“ is missing.

Line 64: The sentence starting with “The current COVID-19 pandemic…” is not relevant to the presented review and I would suggest to delete it.

**********

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Reviewer #1: No

Reviewer #2: No

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19 Mar 2022

1. Academic editor : Many thanks for your valuable comments.We hereby sincerely address the specific academic editor comments and queries. (Please refer to "Response to reviewer" attachment and the revised manuscript: marked-up copy)

2. Reviewer 1: We would like to thank the reviewer for the comments given in the Review Form of our manuscript.We hereby sincerely address the specific reviewer comments and queries.(Please refer to "Response to reviewer" attachment and the revised manuscript: marked-up copy)

3. Reviewer 2: We would like to thank the reviewer for the comments given in the Review Form of our manuscript.We hereby sincerely address the specific reviewer comments and queries.(Please refer to "Response to reviewer" attachment and the revised manuscript: marked-up copy)

Submitted filename: Responce to Reviewers.docx

Click here for additional data file.

18 Apr 2022

28 Apr 2022

Responses to the raised review points by the reviewers

Reviewer 1:

Comment

Major comments:

1. Section “Discussion”: How do the authors interpret the value of these findings? Because of the descriptive background of this analysis and the different adjustment for different risk factors regarding the included studies, it is indeed difficult to demonstrate an incremental value of the participant’s geographical region/country. Therefore, as already indicated by reviewer 2, the topic should be considered very cautiously in the discussion. Statements on causal relationships and incremental value of the variable region for risk prediction should therefore be avoided. Adapt more defensive wording regarding this relationship.

Response

We understand the concerns of the reviewer. We have made the following changes:

Abstract – we reworded and included the following sentences

Older persons and those having CHD group had significantly thicker CIMTs – results section (Line 54)

There is an association between CHD and CIMT values – conclusion (Line 57)

In the results section of the body of the manuscript the sentence was reworded as

“In the adjusted model, CHD group, WHO region and age were significantly associated with CIMT.” (Line 331)

In the discussion, the following sentence was added.

“Even though there is a clear association between CIMT and CHD its usability as a risk predictor for CHD needs to be further investigated. Approaches to prevention as well as screening of at-risk populations for CHD may need to consider regional variations of CIMT.” (Line 389 to 391)

The following is the conclusion of the manuscript

“CIMT among the non-CHD group varies between and within regions, and by age and sex. The mean CIMT values between non-CHD and CHD groups were significantly different within and between WHO regions possibly due to varying influences of modifiable and non-modifiable risk factors. CHD group had a significantly thicker mean CIMT after adjusting for age, WHO region and ultrasound machine used. Segment specific CIMT variations exist among regions.”(Line 455 to 459)

Minor Comment

1. Section “Results”, Table 1: “Summary of studies used in systematic review and meta-analysis, reporting demography: As requested, the authors added further information on the adjustment of the studies included in the analyses. However, the mention of "factors adjusted for" and "adjusted predictors of CIMT" seems repetitive. Therefore, the column "Adjusted predictors of CIMT" should be removed since all relevant information is already listed in the column "Factors adjusted for."

Response

We removed the column.

Minor Comment

2. Section “Method and analysis”: Grammar and spelling of the newly added text parts should be revised.

Response

Grammar and spelling has been revised.

Minor Comment

3. Section “Results”: Since the included studies and corresponding details are already listed in Table 1, there is no need to cite the respective studies again separately in the results section.

Response

As suggested, correction has been done in the result section by omitting the references.

Reviewer 2:

Minor comments:

1. The conclusion, that region or country specific CIMT values are important when developing risk assessment tools to screen at-risk population of CHD is not supported by the data presented, since in this study only differences in CIMT between WHO regions were examined and not their impact to a certain CV risk and significant confounding is possible. CIMT in fact may be important in the risk assessment, but the presented data do not fully validate the role of CIMT differences between WHO regions regarding CV risk. Furthermore, in my opinion the conclusion that CHD is a predictor for CIMT is clinically not sound. As for my understanding, the presented data show a clear association between CHD and CIMT and not necessarily that the presence of CHD predicts CIMT values.

Therefore, I would recommend to precise the conclusion according to the presented data which may help to improve the quality of the manuscript.

Response

As suggested by the reviewer, we have deleted the sentence that country-specific CIMT values are important for risk prediction of CHD.

We have reworded the relevant sentences that stated that CHD is a predictor of CIMT. Please see response to major comment by reviewer 1 above which gives all details of the corrections made.

Submitted filename: Responce to Reviewers .docx

Click here for additional data file.

6 May 2022

Regional and demographic variations of Carotid artery Intima and Media Thickness (CIMT): A systemic review and meta-analysis.

PONE-D-21-23252R2

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Additional Editor Comments (optional):

Reviewers' comments:

4 Jul 2022

PONE-D-21-23252R2

Regional and demographic variations of Carotid artery Intima and Media Thickness (CIMT): a systemic review and meta-analysis.

Dear Dr. Abeysuriya:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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