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The Colombo Twin and Singleton Follow-up Study: a population based twin study of psychiatric disorders and metabolic syndrome in Sri Lanka

  • Kaushalya Jayaweera1,
  • Lisa Aschan2, 3Email authorView ORCID ID profile,
  • Gayani Pannala1,
  • Anushka Adikari1,
  • Nicholas Glozier4,
  • Khalida Ismail2, 3,
  • Carmine M. Pariante5,
  • Fruhling Rijsdijk6,
  • Sisira Siribaddana7,
  • Helena M. S. Zavos8,
  • Patricia A. Zunszain5,
  • Athula Sumathipala9, 1 and
  • Matthew Hotopf2, 3
BMC Public HealthBMC series – open, inclusive and trusted201818:145

https://doi.org/10.1186/s12889-017-4992-2

Received: 18 April 2017

Accepted: 12 December 2017

Published: 17 January 2018

Abstract

Background

The disease burden related to mental disorders and metabolic syndrome is growing in low-and middle-income countries (LMIC). The Colombo Twin and Singleton Study (COTASS) is a population-based sample of twins and singletons in Colombo, Sri Lanka. Here we present prevalence estimates for metabolic syndrome (metS) and mental disorders from a follow-up (COTASS-2) of the original study (COTASS-1), which was a mental health survey.

Methods

In COTASS-2, participants completed structured interviews, anthropometric measures and provided fasting blood and urine samples. Depressive disorder, depressive symptoms, anxiety symptoms, post-traumatic stress disorder (PTSD) and hazardous alcohol use were ascertained with structured psychiatric screens (Composite International Diagnostic Interview (CIDI), Beck Depression Inventory (BDI-II), Generalised Anxiety Disorder Questionnaire (GAD-7), PTSD Checklist – Civilian Version (PCL-C), and Alcohol Use Disorders Identification Test (AUDIT)). We defined metS according to the International Diabetes Federation (IDF) criteria and the revised National Cholesterol Education Programme Adult Treatment Panel (NCEP ATP III) criteria. We estimated the prevalence of psychiatric disorders and metS and metS components, and associations with gender, education and age.

Results

Two thousand nine hundred thirty-four twins and 1035 singletons were followed up from COTASS-1 (83.4 and 61.8% participation rate, respectively). Prevalence estimates for depressive disorder (CIDI), depressive symptoms (BDI ≥ 16), anxiety symptoms (GAD-7 ≥ 10) and PTSD (PCL-C DSM criteria) were 3.8, 5.9, 3.6, and 4.5% respectively for twins and 3.9, 9.8, 5.1 and 5.4% for singletons. 28.1 and 30.9% of male twins and singletons respectively reported hazardous alcohol use. Approximately one third met the metS criteria (IDF: 27.4% twins, 44.6% singletons; NCEP ATP III: 30.6% twins, 48.6% singletons). The most prevalent components were central obesity (59.2% twins, 71.2% singletons) and raised fasting blood glucose or diabetes (38.2% twins, 56.7% singletons).

Conclusion

MetS was highly prevalent in twins, and especially high in singletons, whereas the prevalence of mental disorders was low, but consistent with local estimates. The high levels of raised fasting plasma glucose and central obesity were particularly concerning, and warrant national diabetes prevention programmes.

Background

Low and middle income countries (LMIC) globally have undergone rapid urbanisation, and changes in demography and health behaviours [1, 2]. Whilst life expectancy at birth for many LMIC has improved, disease burden from non-communicable diseases, and years lived with disability have risen [3, 4]. In 2012, 82% of all global premature deaths attributable to non-communicable diseases occurred in LMICs [2]. The global burden of mental disorders, particularly depression, is also forecast to rise and depression is strongly associated with many non-communicable diseases [5]. In Sri Lanka, the setting for the present study, coronary heart disease is the leading cause of mortality, one in ten in the population has diabetes, and there has been an exponential increase in hospitalisations due to these diseases [68]. High prevalence estimates of their risk factors, including hypertension (18–20%), dysglycaemia (14–20%) and obesity (9–36%) have been reported [7, 911].

MetS represents a cluster of metabolic abnormalities which indicate an elevated risk of future development of type II diabetes and cardiovascular disease (CVD) [1217]. Its status as a syndrome, and its predictive validity as a specific risk factor for type 2 diabetes and CVD (over and above its constituent parts), are debated and some have called into question the usefulness of metS as a specific entity [18]. The confidence in its nosological status could be improved if it could be demonstrated that the clustered phenotypes are driven by the same underlying genes. This may be tested using multivariable behavioural genetics, which permits a better understanding of the genetic architecture of the syndrome would strengthen the biological plausibility of metS. For this reason, we conducted a study of cardiometabolic risk using a sample of twins and singletons in Colombo, Sri Lanka; a study design which allows for these types of analyses.

Studying cardiometabolic risk in a South Asian population in a genetically sensitive design is of particular interest given suggestions that the elevated incidence of cardiovascular disease and diabetes in South Asian populations may be partially explained by mechanisms involving gene-environment interactions [19]. Such heritable adaptations may include those which improve resilience to prolonged periods of food shortage (the so-called “thrifty genotype”), but at times of excess lead to greater susceptibility of developing obesity and other metS phenotypes [20, 21]. This may mean that metS in South Asian populations is different in terms both of phenotypic associations and genetic architecture compared with Western populations.

This paper describes the Colombo Twin and Singleton Follow-up Study (COTASS-2) – a population based study of twins and singletons from the Colombo District, Sri Lanka. COTASS-2 explores mental health and cardiovascular risk factors, and the role of genetic and environmental influences on their variance and covariance. The specific aims of COTASS-2 were to: (1) estimate the (genetic) stability of depression in a south Asian population; (2) describe the prevalence of the component phenotypes which make up “metS” in Sri Lanka; (3) explore the genetic architecture of metS phenotypes, and estimate the extent to which phenotypic associations are explained by shared genetic and environmental effects and (4) investigate the aetiological overlap between depression and the component phenotypes of metS.

In the present paper we describe the methods of COTASS-2 and present prevalence estimates of metS, its components, and mental disorders, separately for twins and singletons.

Methods

Setting

Colombo District has a population of 2.32 m, composed of multiple ethnic groups, including Sinhalese (76.5%), Tamils (11.2%) and Moors (10.7%). It is mainly classified as urban (77.6%) and includes the capital city of Sri Lanka, Colombo, but the wider district also includes rural areas [22]. Typical of many urban regions, the district attracts a high level of immigration, of which a substantial minority have been displaced as a result of conflict [23]. The population is characterised by great socio-economic diversity in terms of education, employment, and occupational social class. Since data collection ended for the original COTASS study in 2007, the three decade-long civil conflict in Sri Lanka came to an end.

Description of COTASS-2

COTASS-2 took place between 2012 and 2015, and is a follow-up study of the Colombo Twin and Singleton Study (COTASS-1), conducted in 2005–2007 [24]. COTASS-1 focussed on mental health, and achieved a 91% participation rate for a carefully ascertained population of twins residing in Colombo District, and an 87% participation rate for singletons recruited from the same area. COTASS-2 consisted of three components: an interview component, collection of anthropometric data, and biosample (blood and urine) collection for clinical investigations and biobanking. Two sub-studies examining the autonomic nervous system using heart rate variability measures, and sleep and physical activity using actigraphy were nested within COTASS-2. The study received ethical approval from Psychiatry, Nursing & Midwifery Research Ethics Subcommittee, King’s College London, UK (reference number: PNM/10/11-124), and the Faculty of Medical Sciences University of Sri Jayewardenepura Ethical Review Committee (USJP ERC) (reference number: 596/11).

Participant tracing and data collection

Participant tracing and recruitment

The recruitment process of the original COTASS-1 sample is described in Siribaddana et al. [24]. In recruiting for COTASS-2, COTASS-1 participants were sent invitation letters, and trained field research assistants (FRAs) traced participants by telephone and home visits. Written informed consent was obtained for each study component that they opted to partake in. Participants unable to understand the consent process or the questionnaires due to language barriers or apparent cognitive impairments were excluded. Participants successfully completing one or more study components were offered 750 LKR (approximately £3.50 GBP) to compensate for time and inconvenience.

Interview data collection

Fifteen FRAs were involved in recruitment and questionnaire data collection. All FRAs had at minimum high school qualifications. They received training on basic research methodology and research ethics; collecting informed consent, and conducting the paper and pen based questionnaire interviews. Interviews lasted 1–2 h and were typically conducted in participants’ homes. Quality checks were performed throughout data collection. Study coordinators performed random unannounced spot checks while FRAs conducted interviews. The study coordinators and the data entry team checked questionnaires thoroughly for errors and inconsistencies before accepting completed questionnaire booklets. A sub-sample of randomly selected participants were phoned to confirm that the collected questionnaire data were accurate. An experienced data entry team entered data into a database using SPSS version 13 [25].

Interview measures

Interviews involved 19 scales and checklists measuring sociodemographic characteristic, health and functioning measures including structured assessments of psychiatric disorders, health behaviours, stressful life events and social support, and zygosity (see Additional file 1: Table S1).

Sociodemographic characteristics

Sociodemographic information was collected through adapted Sri Lankan census measures [22], including gender, age, ethnicity, occupation, education, household composition and housing quality.

Health and functioning measures

Structured symptom screens measured several psychiatric disorders. Section E of the World Health Organization’s Composite International Diagnostic Interview (CIDI) captured probable depression diagnoses over the past year [26], and the Beck Depression Inventory (BDI) captured depressive symptom severity over the past 2 weeks [27]. Post-traumatic stress disorder and anxiety were measured using the PTSD Checklist Civilian version [28] and the Generalised Anxiety Disorder questionnaire [29] respectively. The Bradford Somatic Inventory [30] screened for somatic symptoms while the Chalder Fatigue Scale [31] measured the severity and extent of fatigue. The Dutch Eating Behaviour Questionnaire [32] and the Three-Factor Eating Questionnaire [33] identified eating disorders. The Short Form 36 Health Survey Questionnaire [34] measured general health and wellbeing. Sleep over the month was assessed using the Pittsburgh Sleep Quality Index [35]. A physical illness checklist, developed by the Institute of Research and Development (IRD) Sri Lanka, identified significant current and life-time physical illnesses (requiring medication).

Health behaviours

The International Physical Activity Questionnaire [36], an internationally validated scale, captured exercise over the past 7 days. Dietary patterns were measured using a culturally adapted version of the food frequency questionnaire; revised in consultation with local experts [37]. Tobacco and alcohol use were measured using adapted versions of the Tobacco Use questionnaire of the WHO STEPS Instrument [38] and the WHO’s Alcohol Use Disorders Identification Test (AUDIT) [39].

Stressful life events and social support

Stressful life events were measured using a culturally adapted version of the list of threatening experiences questionnaire [40]. Social support and psychological wellbeing was measured using a modified version of the multi-dimensional support scale questionnaire [41].

Zygosity and closeness-of-twins measures

Zygosity was ascertained in COTASS-1 using a questionnaire measure of similarity [24, 42]. If zygosity was missing in COTASS-1 it was replaced with zygosity information collected using the same questionnaire in COTASS-2 (n = 88). The closeness of twins within pairs was measured using items adapted from a study which measured individual differences in personality, ability and interests [43], and another study on psychiatric disorders which measured similarity of environmental experiences of twins [44].

Translation and adaptation of new scales for COTASS-2

Many of the questionnaire components had already been translated and validated in COTASS-1 and other projects [24, 45]. The new scales for COTASS-2 were translated by a panel consisting of Sri Lankan health professionals, academics, and a scholar fluent in both Sinhala and English. Translations were not literal, but cross-culturally adapted to Sinhala in wording which best conveyed the same meaning [46]. Interviews were conducted in Sinhala or English depending on participants’ preference.

Anthropometric and blood pressure measurements

Twelve FRAs were trained to collect anthropometric and blood pressure measurements according to standard protocols, contemporaneously with the interviews. Anthropometric measures included standing height, sitting height, weight, waist circumference and were measured to the closest 0.1 cm/kg. Leg length was estimated from standing height and sitting height measured using portable stadiometers (Seca, Germany). BMI was calculated from standing height and weight using electronic weighing scales (Seca, Germany). Waist circumference was measured using measuring tapes. Blood pressure was measured using Omron HEM-7200 automatic blood pressure monitors (Omron Healthcare, Japan). Three blood pressure recordings were obtained from after 10 min of rest with 2–3 min intervals between measurements. Quality checks were done by random spot checking, and by the data entry team.

Biosample collection

Fasting blood and first morning urine samples were collected during early morning visits to participants’ homes, at the IRD, or specified laboratories. Blood samples were collected using evacuated blood collection tubes (Becton Dickinson, USA). From blood samples the following clinical measures were extracted: fasting blood glucose, HbA1C (high-performance liquid chromatography method), lipid profile, serum glutamic oxaloacetic transaminase (SGOT), serum insulin levels, serum creatinine, and highly sensitive C-reactive protein. Separate blood samples were collected for extracting DNA and serum separation to be stored in a biobank for future genetic analyses. Urine samples provided measures of urine creatinine, urine microalbumin, and urine microalbumin-to-creatinine ratio.

Derived measures of metabolic syndrome

We used the most frequently applied metS definitions: the International Federation of Diabetes (IDF) criteria [47], and the revised National Cholesterol Education Programme Adult Treatment Panel (NCEP ATP III) criteria [48]. The revised NCEP ATP III and the IDF criteria share the same components and cut-offs, but are combined differently to indicate metS. These components include: (1) central obesity (waist circumference: ≥90 cm in men, ≥80 cm in women; South Asian population-specific); (2) raised triglycerides (≥1.7 mmol/l); (3) reduced HDL-cholesterol (<1.03 mmol/l in men, <1.29 mmol/l in women); (4) raised blood pressure (systolic ≥130 mmHg, or diastolic ≥85 mmHg, or hypertensive treatment or previously diagnosed hypertension); and (5) raised fasting plasma glucose (≥5.6 mmol/l or previously diagnosed type 2 diabetes). The IDF criteria defines metS as central obesity in combination with any two of the other four components. The revised NCEP ATP III criteria accept the presence of any three components to indicate metS, without considering any component essential.

Statistical analyses

Percentage prevalence estimates described the sociodemographic and socioeconomic characteristics of the full sample and for twin and singletons separately. To examine possible attritional bias, we used COTASS-1 data to describe differences between participants who were in both study waves, and those who only part-took in COTASS-1. Chi-square tests and t-tests tested distribution differences, and logistic regression controlled for potential confounding factors. Analyses were conducted in Stata 14 [49] and SPSS 13 [25]. Interferential statistics accounted for clustering by twins using the ‘svy’ command in Stata.

The prevalence of cardiometabolic risk indicators and mental disorders were estimated and stratified by twin/singleton status, as preliminary analysis indicated that they differed on key demographic variables. Psychiatric disorders included depressive disorder (CIDI), depressive symptoms (BDI-II 16-point cut-off [50]), generalised anxiety disorder (GAD-7 10-point cut-off), hazardous alcohol use (AUDIT 8-point cut-off), and post-traumatic stress disorder (PTSD; PCL-C coded according to DSM criteria). Cardiometabolic risk indicators included metS and all its component parts, and a binary BMI measure applying a South Asian specific cut-off of 23 kg/m2 [51]. MetS was only estimated for respondents with complete data for all components. Cross-tabulations described the prevalence distributions of psychiatric disorders and cardiometabolic risk indicators by gender, education, and age groups of 19–34, 35–54, 55+. Separate logistic regression models tested associations with these demographic characteristics, whilst adjusting for gender, education, age (continuous measure when used as covariate), marital status, ethnicity and financial strain.

Results

Recruitment

The recruitment process is outlined in Fig. 1. We attempted to trace 5809 of 6043 COTASS-1 participants, and successfully traced 5032. Seven hundred seventy-seven could not be traced, and 603 of the traced participants were ineligible. Of the 4429 eligible participants, 446 refused to participate and 14 were excluded due to poor data quality. Thus, the final COTASS-2 sample consisted of 3969 participants who completed at least one study component. Ninety-three participants from the final COTASS-2 sample had been excluded from COTASS-1 due to poor data quality, or because they were younger than 16 at the time. Thus, the cohort size was 3876. The overall participation rate in COTASS-2 was 76.4, and 83.4% for twins and 61.8% for singletons. These participation rates excluded from the denominator COTASS-1 participants where recruitment was not attempted, those who were ineligible, and participants with poor quality data. Further excluding those not possible to trace produced an overall response rate of 89.9% (91.9% for twins, 84.6% for singletons). Detailed participation rates for each study component are presented as Additional file 1: Table S2. Participation rates were high, including 64.7% providing consent for their DNA to be stored for future genetic research.
Fig. 1

Flowchart of recruitment

Sample characteristics

The COTASS-2 sample consisted of 73.4% (n = 2934) twins and 26.1% (n = 1035) singletons (Table 1). 57.6% were female, and ages ranged from 19 to 91 years, with a mean age of 42.8 years. The majority of the sample were married and lived in urban areas. The dominant ethnic group was Sinhalese (92.7%), and Buddhism was the most commonly reported religion (86.5%). Over half of the sample were in employment, and of those in employment, the most frequently reported occupation type was non-manual or skilled manual labour. Most COTASS-2 participants were educated beyond Grade 6, and 5% reported university education. Approximately 10% reported that making ends meet financially was “difficult” or “very difficult”, 3.9% reported hunger in the past 3 months because of insufficient money to buy food.
Table 1

Demographic and socio-economic characteristics of the COTASS-2 sample

 

Total sample

N = 3969

Twins

N = 2934

Singletons

N = 1035

Twins vs Singletonsa

%/mean (n)

%/mean (n)

%/mean (n)

Χ2/t-test

p-value

Gender

   

0.001

 Male

42.4 (1681)

43.9 (1289)

37.9 (392)

 

 Female

57.6 (2288)

56.1 (1645)

62.1 (643)

 

Mean age

42.8 (3969)

39.9 (2934)

51.2 (1035)

<0.001

Age categories

   

<0.001

 19–29

21.6 (865)

26.3 (772)

8.3 (86)

 

 30–39

25.8 (1025)

29.0 (852)

16.7 (173)

 

 40–49

21.0 (835)

20.7 (607)

22.0 (228)

 

 50–59

16.8 (667)

15.3 (449)

21.1 (218)

 

 60–69

9.6 (380)

5.9 (173)

20.0 (207)

 

 70+

5.1 (204)

2.8 (81)

11.9 (123)

 

Marital status

   

<0.001

 Married

72.2 (2838)

70.7 (2048)

76.5 (790)

 

 Widowed

6.3 (246)

3.8 (111)

13.1 (135)

 

 Divorced/Separated

2.1 (83)

1.8 (53)

2.9 (30)

 

 Never married

19.4 (763)

23.6 (685)

7.6 (78)

 

Ethnicity

   

<0.001

 Sinhala

92.7 (3647)

91.6 (2656)

95.7 (991)

 

 Tamil

3.1 (120)

3.5 (101)

1.8 (19)

 

 Muslim

3.8 (150)

4.5 (130)

1.9 (20)

 

 Other

0.4 (16)

0.4 (11)

0.5 (5)

 

Religion

   

<0.001

 Buddhist

86.5 (3401)

85.4 (2475)

89.5 (926)

 

 Hindu

1.6 (64)

2.0 (57)

0.7 (7)

 

 Islam

4.0 (159)

4.7 (137)

2.1 (22)

 

 Christian

7.9 (309)

7.9 (229)

7.7 (80)

 

Urbanicity

   

<0.001

 Urban

60.2 (2390)

59.9 (1758)

61.1 (632)

 

 Rural

13.4 (533)

13.0 (380)

14.8 (153)

 

 Mixed

20.8 (826)

19.9 (585)

23.3 (241)

 

 Outside Colombo

5.6 (221)

7.2 (212)

0.9 (9)

 

Employment

   

<0.001

 Employed

56.1 (2206)

59.6 (1727)

46.3 (479)

 

 At home

40.8 (1605)

36.9 (1070)

51.7 (535)

 

 Student

1.2 (48)

1.6 (46)

0.2 (2)

 

 Unemployed off sick

1.3 (52)

1.2 (35)

1.6 (17)

 

 Other

0.6 (23)

0.7 (21)

0.2 (2)

 

Social class

   

<0.001

 Managers/Professionals

8.1 (318)

9.3 (267)

4.9 (51)

 

 Non-manual workers/ Skilled manual workers

39.5 (1540)

41.7 (1197)

33.2 (343)

 

 Elementary occupations

8.3 (322)

8.3 (239)

8.0 (83)

 

 Not in employment

44.1 (1723)

40.7 (1167)

53.8 (556)

 

Education

   

0.002

 No education

1.8 (321)

1.8 (52)

1.6 (16)

 

 Grade 1–5

9.0 (2389)

9.2 (264)

8.6 (87)

 

 Grade 6-o/ls

42.9 (929)

40.9 (1174)

48.7 (490)

 

 Passed o/ls

18.5 (276)

18.8 (541)

17.6 (177)

 

 A/ls

22.3 (865)

23.3 (669)

19.5 (196)

 

 University or higher

5.0 (193)

5.4 (156)

3.7 (37)

 

 Other

0.5 (20)

0.6 (17)

0.3 (3)

 

Financial strain

   

<0.001

 Living comfortably

9.3 (365)

9.4 (273)

8.9 (92)

 

 Doing alright

66.5 (2616)

68.3 (1979)

61.5 (637)

 

 Just about getting by

13.9 (547)

13.4 (388)

15.4 (159)

 

 Difficult to make ends meet

7.2 (284)

6.3 (182)

9.9 (102)

 

 Very difficult to make ends meet

3.1 (121)

2.6 (76)

4.3 (45)

 

Hungry due to lack of money

   

<0.001

 No

96.1 (3779)

97.2 (2815)

93.1 (964)

 

 Yes

3.9 (153)

2.8 (82)

6.9 (71)

 

Due to incomplete questionnaire data, the numbers do not add up to totals presented beyond gender and age

aAnalyses account for clustering by twins

Twins and singletons differed on several socio-demographic and socio-economic characteristics (Table 2). Notably, singletons were older and a higher proportion were women, married or widowed, Colombo residents, of Sinhalese ethnicity and of Buddhist religious affiliation. Singletons had lower socio-economic status: they were less likely to be employed or in managerial/professional occupational positions, reported lower education, and greater financial strain. Post-hoc analyses tested socio-demographic differences between twins and singletons, by including age, gender, marital status, and ethnicity simultaneously in a logistic regression model (religion substantially overlapped with ethnicity and was excluded to avoid collinearity; analyses not shown). These variables were found to independently distinguish twins from singletons. Further post-hoc regression tests indicated that differences in employment, occupation and education differences were driven by age. However, singletons remained at greater risk of reporting making ends meet “difficult” (p = 0.035), and hunger due to insufficient money (p < 0.001). Zygosity characteristics presented in Table 2 are in line with the usual distribution seen in population studies: slightly more MZ pairs and slightly more female participants, with opposite sex pairs being the biggest group.
Table 2

Twins participating in COTASS-2

Zygosity

Number of Individual

Number of Twin Families

Monozygotic Males (MZM)

533

295

Dizygotic Males (DZM)

366

215

Monozygotic Females (MZF)

730

397

Dizygotic females (DZF)

485

281

Dizygotic Opposite Sex (DZOS)

809

467

Triplets

11

5

Zygosity missing

1

Number of individuals in the twin groups is not twice the number of families due to missing data. Actual numbers of ‘complete pairs’ are not given since those differ per variable

Systematic differences due to loss to follow-up

Compared to those lost to follow-up (n = 2059), a greater proportion of the followed-up participants in COTASS-2 (n = 3876) were twins, female, of younger age, of Sinhalese ethnicity, and residing in semi-urban areas. Those who were followed-up also had slightly poorer self-rated health; post-hoc analyses indicated that gender drove this association, as more women reported fair or poor health (not shown). There were no attritional differences according to marital status, socio-economic status or depression (Table 3).
Table 3

Characteristics of respondents not followed-up and followed-up in COTASS-2, using data from COTASS-1

 

Lost to follow up (COTASS 1 only, N = 2059)

Followed-up (COTASS1 + 2, N = 3876)a

Lost to follow-up VS followed upb

% /mean (n)

% /mean (n)

p-value

Χ2 / t-test

Twin or singleton

  

<0.001

 Twin

52.4 (1078)

73.5 (2849)

 

 Singleton

47.6 (981)

26.5 (1027)

 

Gender

  

<0.001

 Male

52.5 (1082)

42.1 (1633)

 

 Female

47.5 (977)

57.9 (2243)

 

Mean age (years)

38.0 (2055)

36.7 (3876)

<0.001

Age (years)

  

<0.001

 16–24

25.1 (515)

23.7 (918)

 

 25–34

24.0 (494)

26.4 (1024)

 

 35–44

18.4 (379)

20.6 (798)

 

 45–54

15.0 (308)

16.2 (627)

 

 55–64

10.0 (205)

9.0 (349)

 

 65–74

5.3 (109)

3.5 (136)

 

 75 or over

2.2 (45)

0.6 (24)

 

Marital status

  

0.114

 Married

58.0 (1194)

61.4 (2378)

 

 Widowed

5.1 (105)

4.8 (185)

 

 Divorced/Separated

1.6 (32)

1.4 (53)

 

 Never married

35.3 (727)

32.4 (1255)

 

Ethnicity

  

0.001

 Sinhala

90.4 (1862)

93.4 (3619)

 

 Tamil

3.1 (63)

2.6 (102)

 

 Muslim

6.1 (126)

3.7 (145)

 

 Other

0.4 (8)

0.3 (10)

 

Urbanicity

  

<0.001

 Semi-urban

56.8 (1170)

62.9 (2436)

 

 Urban

43.2 (889)

37.1 (1438)

 

Employment

  

0.118

 Not in employment

39.4 (809)

41.8 (1617)

 

 Student

8.2 (169)

8.0 (308)

 

 Part-time employment

13.0 (267)

14.2 (549)

 

 Full-time employment

39.3 (806)

36.1 (1396)

 

Social class

  

0.079

 Rich

30.5 (252)

25.9 (385)

 

 Middle class

33.3 (276)

34.9 (518)

 

 Poor

36.2 (299)

39.2 (583)

 

Financial strain

  

0.177

 Living comfortably

8.9 (183)

7.9 (304)

 

 Doing alright

64.1 (1316)

64.7 (2504)

 

 Just about getting by

13.0 (267)

14.6 (563)

 

 Difficult to make ends meet

8.9 (182)

8.7 (336)

 

 Very difficult to make ends meet

5.2 (106)

4.2 (162)

 

Hungry due to lack of money

  

0.653

 No

96.3 (1978)

96.0 (3716)

 

 Yes

3.7 (77)

4.0 (155)

 

Self-rated health

  

0.032

 Good, very good or excellent

57.5 (1179)

54.6 (2109)

 

 Fair or poor

42.5 (870)

45.4 (1756)

 

Depressive episode (lifetime)c

  

0.750

 No

94.6 (1945)

94.4 (3653)

 

 Yes

5.4 (110)

5.6 (215)

 

aFollowed-up respondents represent the cohort with data at both COTASS 1 and 2; COTASS 2 respondents which were excluded from COTASS 1 could not be included in these analyses (n = 93)

bAnalyses account for clustering by twins

cEstimated using the Composite International Diagnostic Interview

Prevalence and distribution of mental disorders

Table 4 presents prevalence estimates of mental disorder and associations with demographic characteristics, separately by twin and singleton status. Informed by the differences observed Table 1, associations adjusted for age, sex, education marital status and financial strain. Twins and singletons reported similar levels of depressive disorder and PTSD. Just under 4% of twins and singletons met the DSM-IV criteria for major depressive episode in the past year using the CIDI, and approximately 5% reported met the DSM criteria for PTDS. Hazardous alcohol use was highly prevalent in male twins and singletons (28.1 and 30.9%, respectively). Twins had lower estimates depressive and anxiety symptoms compared to singletons (5.9% vs. 9.8%, and 3.6% vs. 5.1%, respectively). Twins and singletons’ mean scores of depressive symptoms and anxiety symptoms were, respectively, 4.4 and 6.0 on the BDI-II, and 1.7 and 2.2 on the GAD-7 (not shown).
Table 4

Prevalence and adjusted associations of mental disorders with sociodemographic indicators in twins and singletons

 

Total sample

Gender

Education

Age

Male

Female

Higher

Lower

19–34

35–54

55+

Twins

(N = 2934)

(n = 1289)

(n = 1645)

(n = 960)

(n = 1923)

(n = 1197)

(n = 1302)

(n = 435)

 Depressive disorder (N = 2899)

  % (n)

3.8 (110)

3.1 (39)

4.4 (71)

3.6 (35)

3.9 (75)

4.1 (48)

4.0 (52)

2.3 (10)

  AOR (95% CI)

 

ref.

1.3 (0.9–2.0)

ref.

1.1 (0.7–1.7)

ref.

0.9 (0.6–1.4)

0.3* (0.1–0.8)

 Depressive symptoms (N = 2883)

  % (n)

5.9 (170)

4.1 (52)

7.3 (118)

3.6 (34)

7.1 (136)

5.5 (65)

5.9 (76)

6.8 (29)

  AOR (95% CI)

 

ref.

1.7** (1.2–2.5)

ref.

2.0** (1.3–3.1)

ref.

0.9 (0.6–1.4)

0.8 (0.4–1.4)

 Generalised anxiety disorder (N = 2893)

  % (n)

3.6 (103)

2.5 (32)

4.4 (71)

2.7 (26)

4.0 (77)

3.6 (42)

3.6 (46)

3.5 (15)

  AOR (95% CI)

 

ref.

1.7* (1.1–2.6)

ref.

1.5 (0.9–2.6)

ref.

0.9 (0.5–1.5)

0.6 (0.3–1.5)

 PTSD (N = 2896)

  % (n)

4.5 (131)

3.6 (46)

5.2 (85)

2.7 (26)

5.4 (104)

4.1 (49)

5.1 (65)

4.0 (17)

  AOR (95% CI)

 

ref.

1.4 (0.9–2.0)

ref.

2.1** (1.3–3.4)

ref.

1.0 (0.7–1.6)

0.4* (0.4–0.8)

Men only a

(N = 1289)

(n = 400)

(n = 861)

(n = 557)

(n = 562)

(n = 170)

 Hazardous alcohol use (N = 1658)

  % (n)

28.1 (356)

18.0 (72)

32.6 (280)

20.9 (115)

36.7 (202)

23.2 (39)

  AOR (95% CI)

 

ref.

1.9*** (1.4–2.6)

ref.

1.7** (1.2–2.4)

1.0 (0.5–1.3)

Singletons

(N = 1035)

(n = 392)

(n = 643)

(n = 245)

(n = 787)

(n = 146)

(n = 450)

(n = 439)

 Depressive disorder (N = 1035)

  % (n)

3.9 (40)

2.6 (10)

4.7 (30)

4.9 (12)

3.6 (28)

7.5 (11)

4.0 (18)

2.5 (11)

  AOR (95% CI)

 

ref.

1.7 (0.8–3.6)

ref.

0.7 (0.4–1.5)

ref.

0.5 (0.2–1.1)

0.3** (0.1–0.7)

 Depressive symptoms (N = 1030)

  % (n)

9.8 (101)

6.7 (26)

11.7 (75)

5.7 (14)

11.0 (86)

6.2 (9)

10.5 (47)

10.3 (45)

  AOR (95% CI)

 

ref.

1.6 (0.98–2.6)

ref.

1.7 (0.9–3.1)

ref.

1.9 (0.8–4.5)

1.6 (0.7–3.6)

 Generalised anxiety disorder (N = 1034)

  % (n)

5.1 (53)

4.1 (16)

5.8 (37)

2.9 (7)

5.9 (46)

4.8 (7)

6.4 (29)

3.9 (17)

  AOR (95% CI)

 

ref.

1.3 (0.7–2.6)

ref.

2.3 (0.99–5.3)

ref.

1.7 (0.6–4.7)

0.9 (0.3–2.6)

 PTSD (N = 1035)

  % (n)

5.4 (56)

4.1 (16)

6.2 (40)

4.5 (11)

5.7 (45)

5.5 (8)

7.3 (33)

3.4 (15)

  AOR (95% CI)

 

ref.

1.5 (0.8–2.7)

ref.

1.3 (0.7–2.7)

ref.

1.3 (0.5–3.2)

0.6 (0.2–1.5)

Men only a

(n = 392)

(n = 101)

(n = 290)

(n = 65)

(n = 143)

(n = 184)

 Hazardous alcohol use (N = 391)

  % (n)

30.9 (121)

24.0 (24)

33.4 (97)

33.8 (22)

44.8 (64)

19.1 (35)

  AOR (95% CI)

 

ref.

1.9* (1.1–3.3)

ref.

1.9 (0.9–3.9)

0.5 (0.2–1.1)

Odds ratios adjust for age, gender, education, marital status, ethnicity and financial strain

AOR Adjusted odds ratio, CI confidence interval; PTSD Post-Traumatic Stress Disorder, DSM Diagnostic and Statistical Manual of Mental Disorders

p < 0.05, ** p < 0.01, ***p < 0.001 aLimited to men only, due to very low prevalence in female group (0.04%, n = 1); AORs adjusted for education and age only

The socio-demographic distributions of mental disorders followed similar patterns in twins and singletons. Mental disorders were more generally more common in women, and among those with lower educational attainment. Regression analyses controlling for demographic characteristics indicated few statistically significant differences. These included gender differences for depressive and anxiety symptoms in twins, and educational differences in hazardous alcohol use in twins and singletons, and depressive symptoms and PTSD in twins. Depressive disorder and PTSD was less common in the older age group (55+) compared to those aged 19–34. Depressive symptoms and anxiety symptoms did not vary by age, while hazardous alcohol use peaked in men aged 35–54 years.

Prevalence and distribution of metabolic syndrome

Table 5 presents prevalence distributions of cardiometabolic risk factors, and adjusted associations with demographic characteristics for twins and singletons. A third of the twin sample and nearly half of the singletons met the metS criteria (Table 5). The prevalence was slightly higher according to the NCEP ATP III definition in both samples. There were substantial differences in metS by gender. Approximately 34% of female twins, and 55% of female singletons met the metS criteria; whereas 26 and 36% of men in the respective samples met these criteria. These differences were driven by a high proportion of women meeting the gender-specific criteria for central obesity: over 78% of female twins and singletons had waist circumferences ≥80 cm while only 35% of male twins and 42% of male singletons were above the ≥90 cm threshold. This put female twins and singletons at 7- and 12-fold increased odds of central obesity, respectively. In comparison, the gender differences in the BMI indicator of overweight by were not as pronounced (AORs ≥ 2.1; Table 5). The prevalence of individual metS components ranged from 25 to 59% in twins and 30–71% in singletons. Aside from waist circumference, raised fasting glucose or diabetes was the most common metS component, with 38% of twins and 58% of singletons above the threshold. Raised triglyceride levels was the least common component in both samples, but still over a quarter of twins and singletons were at elevated risk. In both samples, women were at three times’ increased odds of reduced HDL-cholesterol, while men were at greater odds of raised triglyceride levels. The blood pressure component and raised fasting glucose or diabetes component did not vary by gender. There were few cardiometabolic risk differences by education; only BMI and waist circumference was lower among those with lower education (differences were statistically significant in twins, and marginally significant in singletons). MetS components were more common in the older age groups, and those aged 55+ were at particularly elevated odds of raised blood pressure (AOR ≥ 10.5) and raised fasting glucose or diabetes (AOR ≥ 2.8), compared to those aged 19–34. Age distributions of cardiometabolic risk were similar between twins and singletons, with the exception of central obesity and BMI. These indicated that younger (19–34) and middle-age (35–54) singletons had higher BMI and waist circumference than equivalent age groups in twins, whilst prevalence estimates in the 55+ age groups were comparable.
Table 5

Prevalence and adjusted associations of cardiometabolic risk indicators with sociodemographic indicators in twins and singletons

 

Total sample

Gender

Education

Age

Male

Female

Higher

Lower

19–34

35–54

55+

Twins

(N = 2934)

(n = 1289)

(n = 1645)

(n = 960)

(n = 1923)

(n = 1197)

(n = 1302)

(n = 435)

 MetS: IDF (N = 2553)

  % (n)

27.4 (700)

19.9 (229)

33.6 (471)

25.0 (200)

28.7 (491)

13.8 (142)

33.7 (388)

45.5 (170)

  AOR (95% CI)

 

ref.

2.0*** (1.6–2.4)

ref.

0.8 (0.7–1.0)

ref.

2.6*** (2.0–3.4)

4.3*** (3.1–5.9)

 MetS: NCEP ATP III (N = 2553)

  % (n)

30.6 (780)

25.9 (298)

34.4 (482)

27.6 (221)

32.1 (549)

16.1 (166)

37.0 (425)

50.5 (189)

  AOR (95% CI)

 

ref.

1.4** (1.2–1.7)

ref.

0.8 (0.7–1.0)

ref.

2.5*** (1.9–3.1)

4.3*** (3.1–5.8)

 BMI ≥23 (N = 2738)

  % (n)

52.3 (1431)

45.7 (559)

57.6 (872)

55.2 (487)

50.9 (920)

45.5 (502)

57.3 (703)

55.5 (226)

  AOR (95% CI)

 

ref.

1.5*** (1.3–1.8)

ref.

0.7*** (0.6–0.8)

ref.

1.4** (1.1–1.7)

1.3* (1.0–1.8)

 Central obesity (N = 2743)

  % (n)

59.2 (1623)

34.8 (425)

78.8 (1198)

62.9 (555)

57.7 (1045)

48.4 (535)

65.9 (810)

68.0 (278)

  AOR (95% CI)

 

ref.

7.4*** (6.0–9.0)

ref.

0.5*** (0.4–0.7)

ref.

1.8*** (1.4–2.3)

2.0*** (1.5–2.8)

 Raised triglycerides (N = 2577)

  % (n)

25.0 (644)

35.6 (413)

16.3 (231)

24.4 (197)

25.5 (439)

21.1 (220)

28.6 (331)

24.7 (93)

  AOR (95% CI)

 

ref.

0.3*** (0.3–0.4)

ref.

0.9 (0.7–1.1)

ref.

1.5** (1.2–1.9)

1.4 (0.8–1.6)

 Low HDL cholesterol (N = 2577)

  % (n)

32.3 (832)

20.2 (234)

42.2 (598)

30.8 (249)

33.0 (569)

34.7 (362)

31.5 (364)

28.2 (106)

  AOR (95% CI)

 

ref.

2.9*** (2.4–3.5)

ref.

1.2 (0.9–1.5)

ref.

0.7** (0.5–0.8)

0.5*** (0.4–0.8)

 Raised BP /hypertensive tx (N = 2747)

  % (n)

27.4 (754)

28.0 (343)

27.0 (411)

19.9 (176)

31.2 (565)

11.4 (126)

30.3 (373)

62.2 (255)

  AOR (95% CI)

 

ref.

0.8 (0.7–1.0)

ref.

1.1 (0.9–1.4)

ref.

3.0*** (2.3–3.9)

10.5***(7.6–14.5)

 Raised fasting glucose/diabetes (N = 2597)

  % (n)

38.2 (992)

38.1 (444)

38.3 (548)

34.1 (279)

40.1 (695)

20.3 (212)

46.4 (541)

61.8 (239)

  AOR (95% CI)

 

ref.

0.9 (0.8–1.1)

ref.

0.9 (0.7–1.1)

ref.

3.0*** (2.4–3.7)

5.9*** (4.4–7.8)

Singletons

(N = 1035)

(n = 392)

(n = 643)

(n = 245)

(n = 787)

(n = 146)

(n = 450)

(n = 439)

 MetS: IDF (N = 891)

  % (n)

44.6 (397)

28.3 (98)

54.9 (299)

41.2 (89)

45.4 (305)

27.9 (34)

43.1 (175)

51.8 (188)

  AOR (95% CI)

 

ref.

3.5*** (2.5–4.8)

ref.

1.0 (0.7–1.4)

ref.

1.6 (1.0–2.5)

2.6*** (1.6–4.3)

 MetS: NCEP ATP III (N = 891)

  % (n)

48.6 (433)

36.4 (126)

56.3 (307)

45.8 (99)

49.3 (331)

29.5 (36)

46.3 (188)

57.6 (209)

  AOR (95% CI)

 

ref.

2.6*** (1.9–3.6)

ref.

1.0 (0.7–1.4)

ref.

1.7* (1.0–2.7)

3.0*** (1.9–4.9)

 BMI ≥23 (N = 925)

  % (n)

61.0 (564)

49.7 (177)

68.0 (387)

65.0 (147)

59.6 (415)

63.0 (80)

68.1 (282)

52.6 (202)

  AOR (95% CI)

 

ref.

2.1*** (1.6–2.8)

ref.

0.8 (0.6–1.1)

ref.

1.0 (0.6–1.5)

0.6* (0.4–0.9)

 Central obesity (N = 926)

  % (n)

71.2 (659)

41.7 (149)

89.6 (510)

72.1 (163)

70.7 (493)

63.8 (81)

75.4 (313)

69.0 (265)

  AOR (95% CI)

 

ref.

12.4*** (8.6–18.0)

ref.

0.7 (0.5–1.1)

ref.

1.1 (0.6–1.9)

1.0 (0.6–1.7)

 Raised triglycerides (N = 899)

  % (n)

29.6 (266)

37.2 (130)

24.7 (136)

32.7 (71)

28.7 (195)

31.5 (39)

31.0 (127)

27.4 (100)

  AOR (95% CI)

 

ref.

0.6*** (0.4–0.8)

ref.

0.8 (0.6–1.1)

ref.

1.0 (0.6–1.6)

0.8 (0.6–1.2)

 Low HDL cholesterol (N = 899)

  % (n)

39.9 (359)

24.9 (87)

49.5 (272)

39.2 (85)

39.9 (271)

38.7 (48)

44.6 (183)

35.1 (128)

  AOR (95% CI)

 

ref.

2.9*** (2.2–4.0)

ref.

1.0 (0.7–1.4)

ref.

1.1 (0.7–1.8)

0.8 (0.5–1.3)

 Raised BP /hypertensive tx (N = 928)

  % (n)

46.6 (432)

48.7 (175)

45.2 (257)

40.7 (92)

48.5 (339)

12.5 (16)

33.0 (137)

72.5 (279)

  AOR (95% CI)

 

ref.

0.9 (0.7–1.3)

ref.

1.0 (0.7–1.4)

ref.

3.3*** (1.8–6.1)

16.6***(8.9–30.9)

 Raised fasting glucose/diabetes (N = 923)

  % (n)

56.7 (523)

54.9 (196)

57.8 (327)

52.3 (115)

57.9 (405)

39.5 (49)

51.2 (212)

68.1 (262)

  AOR (95% CI)

 

ref.

1.3 (0.97–1.7)

ref.

1.2 (0.8–1.6)

ref.

1.2 (0.8–1.9)

2.8*** (1.8–4.4)

Odds ratios adjust for age, gender, education, marital status, ethnicity and financial strain

AOR Adjusted odds ratio, CI confidence interval, MetS metabolic syndrome, IDF International Diabetes Federation criteria, NCEP ATP III revised National Cholesterol Education Programme Adult Treatment Panel criteria, BMI Body-Mass Index, BP blood pressure

* p < 0.05, ** p < 0.01, ***p < 0.001

Discussion

COTASS-2 is a well-phenotyped, genetically-sensitive, epidemiological sample within a middle income country setting. As such it provides a unique resource to explore the associations between the underlying constituents of metS and wider risk factors for CVD and diabetes and mental disorders. It also provides a basis for a better understanding of the inter-relationships between mental and physical health more broadly.

Prevalence of mental disorders

Our sample had a generally low prevalence of most mental disorders. 3.8% of twins and 3.9% of singletons met the criteria for major depressive disorder (DSM-IV), and 3.6% of twins and 5.1% of singletons reported moderate or severe anxiety symptoms (GAD-7 ≥ 10). Whilst the prevalence of major depressive disorder are in line with previous studies in Colombo (6.4%) and other South Asian populations (4.5% in India and 4.0% in Indian minority groups in Singapore [45, 52, 53]), most international studies report higher estimates (e.g. 8.3% in USA, and 5.5 and 5.9% in high-income countries and LMIC, respectively [52]). The estimates of anxiety symptoms were low within an international context (e.g. 8.2% (GAD-7 ≥ 8) in North America, 5.1% (GAD-7 ≥ 10) in Europe, and 11.2% (GAD-7 ≥ 10) in Pakistan [54]). In contrast, the prevalence of PTSD symptoms of 4.5% in twins and 5.4% in singletons was broadly consistent with international research (3.5% in USA [55], 5.5% in UK [56]). Virtually no women reported hazardous alcohol use, while approximately one third of men were hazardous drinkers. The prevalence is high compared to Asian groups in the UK [57], and is consistent with alcohol use being recognised as an increasingly common problem in Sri Lanka [58].

Prevalence of metabolic syndrome and its constituent components

By contrast with mental disorders, we report a very high prevalence of metS (IDF: 27.4% in twins, 44.6% in singletons; NCEP ATP III: 30.6% in twins, 48.6% in singletons). These findings are consistent with previously observed estimates in urban areas of Sri Lanka (IDF: 34.8% [59]; IDF: 38.9%, revised NCEP ATP III: 46.1% in adults aged 35–65 [60]). Gender-specific prevalence estimates are also comparable to those found in the Sri Lankan population (COTASS-2: 19.9–28.3% and 33.6–54.9%; Sri Lanka Diabetes and Cardiovascular Study: 24.3 and 40.8%; in men and women, respectively [59]). This suggests that future analyses of this sample are likely to be broadly generalisable for Sri Lanka. More broadly, urban household studies in South Asia typically find prevalence estimates that are comparable to the singleton estimates in COTASS-2, both according to IDF criteria (35% in Pakistan) [61], and various versions of the NCEP ATP III criteria (41–49% in India and Pakistan) [6163]. In wider international comparisons, the COTASS-2 estimates are in line with the US general population (NCEP ATP III: 33% [64]), South Asian immigrant adults in the US (IDF: 29.7% [65]; IDF: 38.2%, revised NCEP ATP III: 32.7% [66]), and men of South Asian ethnicity aged 40–69 in the UK (IDF: 44.5%) [67].

The prevalence of metS increased with age, consistent with the literature [59, 60, 64, 68], but few differences were observed by levels of education. While US and other Western studies indicate higher prevalence of metS in groups with low education, the Sri Lanka Diabetes and Cardiovascular Study, carried out in 2005–2006, found that higher education groups were more likely to meet the criteria for metS [59, 69]. Given that the association between metS and socioeconomic status seems to be heavily influenced by health behaviours [70], the findings from our more recent study may indicate a shift of socioeconomic distributions of health behaviours in Sri Lanka towards those observed in Western populations.

The gender-specific distributions of metS components were broadly consistent with previous research [9, 60, 66, 71, 72]. Women were more likely to have low HDL cholesterol and higher waist circumference, while men more likely to have raised triglyceride levels, and there were no differences in hypertension or raised blood glucose levels. The most common metS components were central obesity and raised fasting blood glucose or diabetes. Central obesity was a more prominent feature compared to other Sri Lankan studies where the most prevalent components were hypertension [59] and fasting blood glucose [60]. The variability of individual metS components within Sri Lanka may be indicative of a lack of consistent clustering of metS components in the population.

The high estimates of central obesity are consistent with the increasing prevalence of obesity in Sri Lanka which has been described as reaching epidemic proportions [10, 72]. Central obesity was particularly prevalent among women, and this gender discrepancy was much greater than the differences in overweight defined by BMI. However, the very high prevalence of central obesity (78.8% in female twins; 89.6% in female singletons) was not consistent with the prevalence of other metS components (<60%). This may suggest that the 80 cm cut-off of waist circumference overestimates cardiometabolic risk for Sri Lankan women. The observation that twins were less likely than singletons to be overweight, particularly in younger age groups, could be an effect of twins’ lower birth weight, affecting weight in adulthood [73].

Raised blood glucose levels were alarmingly high at 38.2% of twins and 56.7% of singletons. This was considerably higher than observed in studies in the US and India [74, 75], and substantially higher than previously observed rates of dysglycaemia in urban areas of the Sri Lanka (30.0%) [7]. The prevalence was particularly high among older adults, but also elevated in younger adults. These findings re-affirm concerns of a diabetes epidemic in Sri Lanka [76].

Limitations

Our original sample had an exceptionally high participation rate (91% for twins and 87% for singletons). The present study achieved good follow up rates, particularly for twins (83.4%), where participants usually had contact details for their co-twins. There is always a risk of bias caused by non-participation, which is particularly important for mental disorders, which have a critical impact on participation [77]. However, unlike many surveys we did not find that depression influenced participation, suggesting that attrition will have minimal influence on analyses examining mental health outcomes. Furthermore, given that neither depression nor self-rated health determined follow-up, it is unlikely that loss to follow-up will influence the findings. As this sample had completed a psychiatric interview during the previous wave of data collection (COTASS-1), it is possible that individuals identified as having a mental disorder followed our advice to seek treatment, which caused a reduction in prevalence of mental disorders in COTASS-2. However, given that this was merely a recommendation rather than a formal intervention, it is unlikely that a high proportion advised to consult did so. We therefore believe that this did not have a major impact on prevalence. Furthermore, the lifetime depressive disorder outcome captured by the CIDI will not have been affected by ascertaining mental disorder at a previous timepoint.

It is possible that twins differ from non-twins in ways that affect health and limit the interpretation of our prevalence estimates. Our analyses presented the results by twins and singletons, and showed some modest differences in mental health, whilst, singletons were at greater cardiometabolic risk compared to twins. Twins and singletons’ sociodemographic distributions of mental disorders and cardiometabolic risk were similar.

In terms of data collection, due to the multicomponent nature of the study with separate specialist data collection teams, it was not always possible to collect data from all components simultaneously for each participant. In addition, biospecimens were collected early in the morning whereas questionnaire data and anthropometric measurements could be collected at any time during the day when participants were available.

Conclusions

We successfully followed up the previous cohort of twins and singletons. The initial findings indicate that an exceptionally high proportion of the singleton population we studied have metS – a finding of profound importance for public health planning, which suggests that over the next decades its impact will be felt in terms of increasing prevalence of diabetes and cardiovascular disease. Our findings indicate the need for population level preventive approaches particularly targeting diet and exercise. Future papers will describe in greater depth the association between metS variables, their genetic architecture and association with mental health.

Notes

Declarations

Acknowledgements

We gratefully acknowledge the contribution of the teams who have contributed immensely to the collection and preparation of the data. The data collection team includes: Maneesha Jayaweera, Geetha Weerasekara, Ajith Weerasekara, Chandrika Wickramasuriya, Ravindra Perera, Aruna Walisundara, Nadun Perera, Thirumalayandi Jayakumar, Kasun Dias, Amila Sandaruwan, Nipuna Siribaddana, and Vidath Samarakkody. The IRD office and data entry team: Chamali Jayasinghe, Mekala Narangoda, Janani Marasinghe, Dr. Jeewani Hettiarachchi, Udeni Samanmali, Nilanthi Priyadharshani, Hansika Gamage, and Pavithra Lakmali. The IRD junior academic team consisting of Lasith Dissanayake, Lakshan Warnakula, Shavindya Abeygunaratne, Rosanthy Skandhakumar, Ravindi Madanayake, Thathmi Wathsara and Charuni Rajapaksha who were involved in data collection and assisted in establishment of the biobank. We would also like thank and acknowledge Upul Tudawe and the staff of Durdans hospital: Dr. Sunil Navaratinaraja, Chandana Jayasinghe, Yasanthi Warusavithana, Vajira Jayasekara, the OPD nursing staff involved in biosample collection, and the medical laboratory technicians involved in sample analysis.

Funding

This study was supported by the Wellcome Trust (Grant number: 093206/Z/10/Z). LA, MH, CMP and PAZ currently receive salary support from the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The sleep substudy of COTASS-2 led by NG was supported by the NHMRC of Australia (Grant numbers: 566529 and 571421).

The views expressed are those of the authors and not necessarily those of the Wellcome Trust, the NHS, the NIHR or the Department of Health. The funders did not have a role in the study design; collection, analysis or interpretation of data; the writing of the manuscript; or in the decision to submit the manuscript for publication.

Availability of data and materials

The datasets analysed during the current study available from the corresponding author on reasonable request.

Authors’ contributions

KJ and LA contributed at all stages of planning and writing the article. LA conducted the statistical analyses, and GP produced the flow chart. KJ, AA, GP, worked on acquisition of data, data management and preparation. AS and MH contributed to drafting the manuscript and revised the article for important intellectual content. NG, KI, CP, FR, SS, HMSZ, PZ, AS and MH contributed to revisions of the paper and interpretations of data. All authors approved the final manuscript.

Ethics approval and consent to participate

The study received ethical approval from Psychiatry, Nursing & Midwifery Research Ethics Subcommittee, King’s College London, UK (reference number: PNM/10/11-124), and the Faculty of Medical Sciences University of Sri Jayewardenepura Ethical Review Committee (USJP ERC) (reference number: 596/11). Ethical principles were followed by collecting written informed consent from all respondents, for each study component they opted to participate in. Participants were informed that they could withdraw from the study or any individual component at any time without giving any reason, and without any consequences.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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’ Affiliations

(1)
Institute for Research and Development, Colombo, Sri Lanka
(2)
Psychological Medicine Department, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
(3)
NIHR Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust, King’s College London, London, UK
(4)
Brain and Mind Centre, University of Sydney, Sydney, Australia
(5)
Stress, Psychiatry and Immunology Laboratory, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
(6)
Social Genetic and Developmental Research Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
(7)
Department of Medicine, University of Rajarata, Anuradhapura, Sri Lanka
(8)
Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
(9)
Research Institute for Primary Care & Health Sciences, Faculty of Medicine & Health Sciences, Keele University, Keele, UK

References

  1. Yusuf S, Reddy S, Ôunpuu S, Anand S. Clinical cardiology: new frontiers global burden of cardiovascular diseases. Circulation. 2001;104:2746–53.View ArticlePubMedGoogle Scholar
  2. World Health Organization. Global status report on noncommunicable diseases 2014. Geneva; 2014. http://www.who.int/nmh/publications/ncd-status-report-2014/en/. Accessed 25 Aug 2016.
  3. Richards NC, Gouda HN, Durham J, Rampatige R, Whittaker M. Disability, noncommunicable disease and health information. Bull World Health Organ. 2016;94:230–2.View ArticlePubMedPubMed CentralGoogle Scholar
  4. World Health Organization. The global burden of disease: 2004 update. Geneva; 2008. http://www.who.int/healthinfo/global_burden_disease/2004_report_update/en/. Accessed 25 Aug 2016.
  5. Prince M, Patel V, Saxena S, Maj M, Maselko J, Phillips MR, et al. No health without mental health. Lancet. 2007;370:859–77.View ArticlePubMedGoogle Scholar
  6. Ministry of Healthcare and Nutrition Sri Lanka. The National Policy & Strategic Framework For Prevention And Control Of Chronic Non-Communicable Diseases. 2009. https://www.mindbank.info/item/5279. Accessed 21 Dec 2017.
  7. Katulanda P, Constantine GR, Mahesh JG, Sheriff R, Seneviratne RDA, Wijeratne S, et al. Prevalence and projections of diabetes and pre-diabetes in adults in Sri Lanka - Sri Lanka Diabetes, Cardiovascular Study (SLDCS). Diabet Med. 2008;25:1062–9.View ArticlePubMedGoogle Scholar
  8. Premaratne R, Amarasinghe A, Wickremasinghe AR. Hospitalisation trends due to selected non-communicable diseases in Sri Lanka, 2005–2010. Ceylon Med J. 2005;50:51–4.View ArticlePubMedGoogle Scholar
  9. Katulanda P, Jayawardena MAR, Sheriff MHR, Constantine GR, Matthews DR. Prevalence of overweight and obesity in Sri Lankan adults. Obes Rev. 2010;11:751–6.View ArticlePubMedGoogle Scholar
  10. Wijesuriya M, Gulliford M, Charlton J, Vasantharaja L, Viberti G, Gnudi L, et al. High prevalence of cardio-metabolic risk factors in a young urban Sri-Lankan population. PLoS One. 2012;7:e31309.Google Scholar
  11. Wijewardene K, Mohideen MR, Mendis S, Fernando DS, Kulathilaka T, Weerasekara D, et al. Prevalence of hypertension, diabetes and obesity: baseline findings of a population based survey in four provinces in Sri Lanka. Ceylon Med J. 2005;50:62–70.View ArticlePubMedGoogle Scholar
  12. Alberti KGMM, Zimmet P, Shaw J. Metabolic syndrome—a new world-wide definition. A consensus statement from the international diabetes federation. Diabet Med. 2006;23:469–80.View ArticlePubMedGoogle Scholar
  13. Morrison JA, Friedman LA, Wang P, Glueck CJ. Metabolic syndrome in childhood predicts adult metabolic syndrome and type 2 diabetes mellitus 25 to 30 years later. J Pediatr. 2008;152:201–6.View ArticlePubMedGoogle Scholar
  14. Morrison JA, Friedman LA, Gray-McGuire C. Metabolic syndrome in childhood predicts adult cardiovascular disease 25 years later: the Princeton lipid research clinics follow-up study. Pediatrics. 2007;120:340–5.View ArticlePubMedGoogle Scholar
  15. McNeill AM, Rosamond WD, Girman CJ, Golden SH, Schmidt MI, East HE, et al. The metabolic syndrome and 11-year risk of incident cardiovascular disease in the atherosclerosis risk in communities study. Diabetes Care. 2005;28:385–90.View ArticlePubMedGoogle Scholar
  16. Lakka HM, Laaksonen DE, Lakka TA, Niskanen LK, Kumpusalo E, Tuomilehto J, et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA. 2002;288:2709–16.View ArticlePubMedGoogle Scholar
  17. Laaksonen DE, Lakka HM, Niskanen LK, Kaplan GA, Salonen JT, Lakka TA. Metabolic syndrome and development of diabetes mellitus: application and validation of recently suggested definitions of the metabolic syndrome in a prospective cohort study. Am J Epidemiol. 2002;156:1070–7.View ArticlePubMedGoogle Scholar
  18. Kassi E, Pervanidou P, Kaltsas G, Chrousos G. Metabolic syndrome: definitions and controversies. BMC Med. 2011;9:48.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Tillin T, Forouhi NG. Metabolic syndrome and ethnicity. In: Byrne CD, Wild SH, editors. The metabolic syndrome. 2nd ed. Chichester: Wiley-Blackwell; 2011. p. 19–44.View ArticleGoogle Scholar
  20. Neel JV. Diabetes mellitus: a “thrifty” genotype rendered detrimental by “progress”? 1962. Bull World Health Organ. 1999;77:353–62.Google Scholar
  21. Hales CN, Barker DJP. Type 2 (non-insulin-dependent) diabetes mellitus: the thrifty phenotype hypothesis. Int J Epidemiol. 2013;42:1215–22.View ArticlePubMedGoogle Scholar
  22. Department of Census and Statistics Sri Lanka. Highlights: Census of Population and Housing 2012, Western Province. 2012. http://www.statistics.gov.lk/pophousat/cph2011/index.php?fileName=Western&gp=Activities&tpl=3. Accessed 6 Apr 2016.
  23. Department of Census & Statistics. Census of Population and Housing 2012. Ministry of Policy Planning and Economic Affairs. http://www.statistics.gov.lk/PopHouSat/CPH2011/Pages/Activities/Reports/FinalReport/FinalReportE.pdf.
  24. Siribaddana SH, Ball HA, Hewage SN, Glozier N, Kovas Y, Dayaratne D, et al. Colombo twin and singleton study (CoTASS): a description of a population based twin study of mental disorders in Sri Lanka. BMC Psychiatry. 2008;8:49.View ArticlePubMedPubMed CentralGoogle Scholar
  25. SPSS Inc. Released 2005. PASW statistics for windows, version 13.0. Chicago: SPSS Inc.Google Scholar
  26. Andrews G, Peters L. The psychometric properties of the composite international diagnostic interview. Soc Psychiatry Psychiatr Epidemiol. 1998;33:80–8.View ArticlePubMedGoogle Scholar
  27. Beck A, Steer R, Brown G. Beck depression inventory-II (BDI-II) manual for the Beck depression inventory-II. San Antonio: Psychological Corporation; 1996.Google Scholar
  28. Weathers FW, Litz BT, Huska JA, Keane TM. PTSD checklist—civilian version. Boston: National Center for PTSD, Behavioral Science Division; 1994.Google Scholar
  29. Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166:1092–7.View ArticlePubMedGoogle Scholar
  30. Mumford DB, Bavington JT, Bhatnagar KS, Hussain Y, Mirza S, Naraghi MM. The Bradford somatic inventory. A multi-ethnic inventory of somatic symptoms reported by anxious and depressed patients in Britain and the indo-Pakistan subcontinent. Br J Psychiatry. 1991;158:379–86.View ArticlePubMedGoogle Scholar
  31. Chalder T, Berelowitz G, Pawlikowska T, Watts L, Wessely S, Wright D, et al. Development of a fatigue scale. J Psychosom Res. 1993;37:147–53.View ArticlePubMedGoogle Scholar
  32. van Strien T, Frijters JER, Bergers GPA, Defares PB. The Dutch eating behavior questionnaire (DEBQ) for assessment of restrained, emotional, and external eating behavior. Int J Eat Disord. 1986;5:295–315.View ArticleGoogle Scholar
  33. Stunkard AJ, Messick S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. J Psychosom Res. 1985;29:71–83.View ArticlePubMedGoogle Scholar
  34. McHorney CA, Ware JE, Raczek AE. The MOS 36-item short-form health survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs. Med Care. 1993;31:247–63.View ArticlePubMedGoogle Scholar
  35. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28:193–213.View ArticlePubMedGoogle Scholar
  36. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35:1381–95.View ArticlePubMedGoogle Scholar
  37. Iqbal R, Anand S, Ounpuu S, Islam S, Zhang X, Rangarajan S, et al. Dietary patterns and the risk of acute myocardial infarction in 52 countries: results of the INTERHEART study. Circulation. 2008;118:1929–37.View ArticlePubMedGoogle Scholar
  38. World Health Organization. WHO STEPwise approach to surveillance (STEPS). 2010.Google Scholar
  39. Saunders JB, Aasland OG, Babor TF, de la Fuente JR, Grant M. Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption—II. Addiction. 1993;88:791–804.View ArticlePubMedGoogle Scholar
  40. Brugha TS, Cragg D. The list of threatening experiences: the reliability and validity of a brief life events questionnaire. Acta Psychiatr Scand. 1990;82:77–81.View ArticlePubMedGoogle Scholar
  41. Winefield HR, Winefield AH, Tiggemann M. Social support and psychological well-being in young adults: the multi-dimensional support scale. J Pers Assess. 1992;58:198–210.View ArticlePubMedGoogle Scholar
  42. Ooki S, Yamada K, Asaka A. Zygosity diagnosis of twins by questionnaire for twins’ mothers. Acta Genet Med Gemellol. 1993;42:17–22.View ArticlePubMedGoogle Scholar
  43. Loehlin JC, Nichols RC. Heredity, environment, & personality: a study of 850 sets of twins. Austin: University of Texas Press; 1976.Google Scholar
  44. Kendler KS, Gardner CO. Twin studies of adult psychiatric and substance dependence disorders: are they biased by differences in the environmental experiences of monozygotic and dizygotic twins in childhood and adolescence? Psychol Med. 1998;28:625–33.View ArticlePubMedGoogle Scholar
  45. Institute for Research and Development. National Mental Health Survey Report. Colombo: Institute for Research and Development; 2007.Google Scholar
  46. Sumathipala A, Murray J. New approach to translating instruments for cross-cultural research: a combined qualitative and quantitative approach for translation and consensus generation. Int J Methods Psychiatr Res. 2000;9:87–95.View ArticleGoogle Scholar
  47. Alberti GM, Zimmet PZ, Shaw J. The metabolic syndrome — a new worldwide definition. Lancet. 2005;366:1059–62.View ArticlePubMedGoogle Scholar
  48. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation. 2005;112:2735–52.View ArticlePubMedGoogle Scholar
  49. StataCorp. Stata statistical software: release 14. College Station: StataCorp LP; 2015.Google Scholar
  50. Rodrigo A, Kuruppuarachchi K, Pathmeswaran A. Validation of the Beck depression inventory II among the Sinhalese speaking population in Sri Lanka. Sri Lanka J Psychiatry. 2015;6:20–4.View ArticleGoogle Scholar
  51. WHO expert consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–63.View ArticleGoogle Scholar
  52. Kessler RC, Birnbaum HG, Shahly V, Bromet E, Hwang I, McLaughlin KA, et al. Age differences in the prevalence and co-morbidity of DSM-IV major depressive episodes: results from the WHO world mental health survey initiative. Depress Anxiety. 2010;27:351–64.View ArticlePubMedPubMed CentralGoogle Scholar
  53. Chong SA, Vaingankar J, Abdin E, Subramaniam M. The prevalence and impact of major depressive disorder among Chinese, Malays and Indians in an Asian multi-racial population. J Affect Disord. 2012;138:128–36.View ArticlePubMedGoogle Scholar
  54. Khan MS, Ahmed U, Adnan M, Khan MA, Bawany FI. Frequency of generalised anxiety disorder and associated factors in an urban settlement of Karachi. J Pak Med Assoc. 2013;63:1451–5.PubMedGoogle Scholar
  55. Kessler RC, Chiu WT, Demler O, Walters EE. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62:617–27.View ArticlePubMedPubMed CentralGoogle Scholar
  56. Frissa S, Hatch SL, Gazard B, Fear NT, Hotopf M. Trauma and current symptoms of PTSD in a south East London community. Soc Psychiatry Psychiatr Epidemiol. 2013;48:1199–209.View ArticlePubMedGoogle Scholar
  57. Hatch SL, Woodhead C, Frissa S, Fear NT, Verdecchia M, Stewart R, et al. Importance of thinking locally for mental health: data from cross-sectional surveys representing south East London and England. PLoS One. 2012;7:e48012.View ArticlePubMedPubMed CentralGoogle Scholar
  58. Siriwardhana P, Dawson AH, Abeyasinge R. Acceptability and effect of a community-based alcohol education program in rural Sri Lanka. Alcohol Alcoholism. 2013;48:250–6.View ArticlePubMedGoogle Scholar
  59. Katulanda P, Ranasinghe P, Jayawardana R, Sheriff R, Matthews DR. Metabolic syndrome among Sri Lankan adults: prevalence, patterns and correlates. Diabetol Metab Syndr. 2012;4:24.Google Scholar
  60. Chackrewarthy S, Gunasekera D, Pathmeswaren A, Wijekoon CN, Ranawaka UK, Kato N, et al. A comparison between revised NCEP ATP III and IDF definitions in diagnosing metabolic syndrome in an urban Sri Lankan population: the Ragama health study. ISRN Endocrinol. 2013;2013:1-7. Article ID 20176.Google Scholar
  61. Hydrie MZ, Shera AS, Fawwad A, Basit A, Hussain A. Prevalence of metabolic syndrome in urban Pakistan (Karachi): comparison of newly proposed international diabetes federation and modified adult treatment panel III criteria. Metab Syndr Relat Disord. 2009;7:119–24.View ArticlePubMedGoogle Scholar
  62. Prasad DS, Kabir Z, Dash AK, Das BC. Prevalence and risk factors for metabolic syndrome in Asian Indians: a community study from urban eastern India. J Cardiovasc Dis Res. 2012;3:204–11.View ArticlePubMedPubMed CentralGoogle Scholar
  63. Ramachandran A, Snehalatha C, Satyavani K, Sivasankari S, Vijay V. Metabolic syndrome in urban Asian Indian adults – a population study using modified ATP III criteria. Diabetes Res Clin Pract. 2003;60:199–204.View ArticlePubMedGoogle Scholar
  64. Aguilar M, Bhuket T, Torres S, Liu B, RJ W. Prevalence of the metabolic syndrome in the United States, 2003–2012. JAMA. 2015;313:1973–4.View ArticlePubMedGoogle Scholar
  65. Dodani S, Henkhaus R, Wick J, Vacek J, Gupta K, Dong L, et al. Metabolic syndrome in south Asian immigrants: more than low HDL requiring aggressive management. Lipids Health Dis. 2011;10:45.View ArticlePubMedPubMed CentralGoogle Scholar
  66. Misra R, Patel T, Kotha P, Raji A, Ganda O, Banerji M, et al. Prevalence of diabetes, metabolic syndrome, and cardiovascular risk factors in US Asian Indians: results from a national study. J Diabetes Complicat. 2010;24:145–53.View ArticlePubMedGoogle Scholar
  67. Forouhi NG, Sattar N, Tillin T, McKeigue PM, Chaturvedi N. Do known risk factors explain the higher coronary heart disease mortality in south Asian compared with European men? Prospective follow-up of the Southall and Brent studies, UK. Diabetologia. 2006;49:2580–8.View ArticlePubMedGoogle Scholar
  68. Ford E, Giles WH, Dietz WH. Increasing prevalence of the metabolic syndrome among US adults. Diabetes Care. 2004;27:2444–9.View ArticlePubMedGoogle Scholar
  69. Montez JK, Bromberger JT, Harlow SD, Kravitz HM, Matthews KA. Life-course socioeconomic status and metabolic syndrome among midlife women. J Gerontol Ser B Psychol Sci Soc Sci. 2016;0:1–11.Google Scholar
  70. Ramsay SE, Whincup PH, Morris R, Lennon L, Wannamethee SG. Is socioeconomic position related to the prevalence of metabolic syndrome? Am J Public Health. 2008;31:2380–2.Google Scholar
  71. Wong R. Trends in prevalence of the metabolic syndrome [reply to letter to the editor]. JAMA. 2015;314:950–1.View ArticlePubMedGoogle Scholar
  72. Jayawardena R, Byrne NM, Soares MJ, Katulanda P, Hills AP. The obesity epidemic in Sri Lanka revisited. Asia Pac J Public Health. 2015;27:NP1298–9.View ArticlePubMedGoogle Scholar
  73. Zhao Y, Wang S-F, Mu M, Sheng J. Birth weight and overweight/obesity in adults: a meta-analysis. Eur J Pediatr. 2012;171:1737–46.View ArticlePubMedGoogle Scholar
  74. Deepa M, Farooq S, Datta M, Deepa R, Mohan V. Prevalence ofmetabolic syndrome using WHO, ATPIII and IDF definitions in Asian Indians: the Chennai urban rural epidemiology study (CURES-34). Diabetes Metab Res Rev. 2007;23:127–34.View ArticlePubMedGoogle Scholar
  75. Beltrán-Sánchez H, Harhay MO, Harhay MM, McElligott S. Prevalence and trends of metabolic syndrome in the adult U.S. population, 1999–2010. J Am Coll Cardiol. 2013;62:697–703.View ArticlePubMedPubMed CentralGoogle Scholar
  76. Katulanda P, Sheriff MHR, Matthews DR. The diabetes epidemic in Sri Lanka – a growing problem. Ceylon Med J. 2006;51:26–8.View ArticlePubMedGoogle Scholar
  77. Knudsen AK, Hotopf M, Skogen JC, Overland S, Mykletun A. The health status of nonparticipants in a population-based health study: the Hordaland health study. Am J Epidemiol. 2010;172:1306–14.View ArticlePubMedGoogle Scholar

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