INTRODUCTION
Prehypertension (PreHTN) is defined as systolic BP 120–139 mmHg and/or diastolic BP 80–89 mmHg, and is between normal blood pressure and hypertension1; and prediabetes (PreDM) defined as fasting plasma glucose levels 5.6–6.9 mmol/L, is between normal blood glucose and DM2. People with PreHTN/PreDM are at high risk of developing HTN and DM3,4, largely contributing to cardiovascular disease (CVD) and mortality5-9.
The prevalence of coexisting PreHTN and PreDM was 11.3% (≥18 years) in Jilin Province in 2013, China10, 11.0% (≥18 years) in northern and northeastern China in 2008–201011, 11.2% (≥20 years) in 1999 in USA12, and 10.4% (adults) in Enugu state in 2013, Nigeria13. The prevalence of PreDM only was 4.3% and PreHTM only was 34.2% in Jilin Province, China10. In Peru in 2017–2018, the national prevalence of PreHTN was 21.1%14. Among overweight/obese people in a small community study in the inner-city of Portoviejo in 2010 (Ecuador), undiagnosed PreHTN, HTN, preDM, and DM were highly prevalent15. We could not identify any study on the national prevalence and associated factors of PreHTN/PreDM in the general adult population in Ecuador, which led to the study.
In previous studies, sociodemographic and health factors were identified for coexisting PreHTN and PreDM. Sociodemographic factors associated with PreHTN/PreDM include older age10-13,16, male sex10,12,16, lower education10, ethnicity (lowest in non-Hispanic African American12; lowest among Mongolian-Chinese16), and region16. The health factors associated with PreHTN/PreDM include physical inactivity13, obesity10-12,16, abnormal triglyceride (TG)10,12,16, low-density lipoprotein cholesterol (LDL-C)16, and lower high-density lipoprotein cholesterol12.
Factors associated with PreHTN include older age14, men14,17,18, lower socioeconomic status17-19, high salt intake20, physical inactivity17-19, low fruit and vegetable intake17, smoking17, alcohol use17,19, obesity14,17,18,20, abnormal triglycerides14. Factors associated with PreDM include older age21, lower socioeconomic status21, ethnicity21,22, smoking, alcohol use, obesity23, abnormal triglycerides22, and normal total cholesterol22,23. The aim of the study was to assess the prevalence and associated factors of PreHTN/PreDM among people aged 18–69 years, in Ecuador.
METHODS
Setting
With a population of 17.5 million, Ecuador is an upper middle-income nation, 25% of which live in poverty. Mestizo (mixed White and Native American) makes up 71.9% of the population, followed by Montubio (7.4%), Native Americans 7%, Whites 6.1%, Afro-Ecuadorians 4.3%, Mulatto 1.9%, Blacks 1%, and Other 0.4%. With significant concentrations also found along the western coastal strip, almost half of the population is concentrated in the interior in the Andean intermontane basins and valleys; the eastern rainforests are still sparsely populated. The majority of people (64.8%) reside in urban areas; the life expectancy at birth for men and women was 75.3 and 81.3 years, respectively; among those aged >15 years, 94.9% of men and 94% of women could read and write24.
Sample and procedure
We analyzed cross-sectional secondary data from the 2018 Ecuador STEPS survey25, including participants with complete fasting blood glucose and blood pressure measurements. Participants with a self-reported history of heart attack/angina/stroke were excluded26. Using the 3 STEPS process, interviews, physical and biomedical measures were assessed26. A nationally representative community sample of participants aged 18–69 years (excluding Galapagos) was randomly selected through a stratified multistage sampling process26. Probability sampling scheme of elements was used with the following three selection stages: 1) First stage, selecting primary sampling units (PSUs) by layer; 2) Second stage: selecting 12 occupied residences within each PSU selected in the first phase; and 3) Third stage: selecting 1 person aged 18–69 years per household. The selection of PSUs, according to the established size, was conducted independently and randomly in each of the strata. They also randomly selected 12 homes from each previously selected cluster. Since the second period of uprising, given the high rate of change in occupation, 16 homes per cluster were chosen to counteract this effect. The change affected the remaining 230 conglomerates, which gave a total of 6680 residential areas to be examined. Finally, an enrolment of the eligible persons within each residence, selecting in a way random of them26. The Ecuador Ministry of Health Ethical Committee had provided ethics approval, and participants had given written informed consent.
Measures
For the STEPS key survey measures, construct validity and associative validity have been demonstrated, ensuring aggregate data suitable for reliable cross-country comparisons27.
Outcome variables
PreHTN was defined as systolic BP 120–139 mmHg and/or diastolic BP 80–89 mmHg; and HTN as systolic BP ≥140 mmHg and/or diastolic BP ≥90 mmHg or taking anti-hypertensive medication in the past two weeks1,26. Of the three systolic and diastolic blood pressure readings (participants rested for three minutes between each reading) obtained using the Omron BP apparatus automatic blood pressure monitor, the last two were averaged26. PreDM was defined as fasting plasma glucose levels 5.6–6.9 mmol/L; and DM ≥7.0 mmol/L or currently on medication for raised blood glucose2.
Sociodemographic factors included education level, age, sex, ethnicity, and marital status25. Health risk behaviors included daily servings of fruits and vegetables, dietary salt intake (often or always adding salt/salty sauce before/during meals), smoking (daily), and alcohol dependence (3 items of the Alcohol Use Disorder Identification Test, scores ≥4)28. Physical activity was evaluated with the Global Physical Activity Questionnaire (GPAQ) and classified by median metabolic equivalent (MET) of the activities performed as low, moderate, and high29. Body mass index (BMI, kg/m2) was classified as: underweight (<18.5), normal weight (18.5–24.9), overweight (25.0–29.9), and obesity (≥30.0)25,26. Elevated total cholesterol (TC) was defined as: TC ≥5.17 mmol/L (200 mg/dL) or on antilipidemic medication30.
Data analysis
Chi-squared tests were used to estimate differences in the proportion of outcome variables. Adjusted multinomial logistic regression was applied to assess the factors associated with PreHTN and PreDM, PreHTN only, and PreDM only (with both normoglycemia and normotension group as reference category). Covariates were selected based on literature review10-13,16-23, and included sociodemographic factors (age, gender, ethnicity, education level, and marital status), health risk behaviors (fruit/vegetable intake, salt intake, smoking, alcohol dependence, and physical activity), BMI, and elevated TC. The missing values were discarded and the significance was set at p<0.05. The sample with missing values differed from the complete data in terms higher education, but no other differences in relation to social and health characteristics. STATA software version 14.0 (Stata Corporation, College Station, TX, USA) was used for the statistical analysis taking account complex sampling.
RESULTS
Characteristics of the participants
The sample with complete blood pressure and glucose measurements, excluding pregnant women and those with cardiovascular disease, included 3608 adults, aged 18–69 years (median: 39; IQR: 25), in 2018. Of the 3608 individuals, 247 (6.5%) had coexisting PreHTN and PreDM, 1353 (39.9%) had normoglycemia and normotension, 823 (23.2%) had PreHTN only, and 321 (7.5%) had PreDM only (Table 1).
Table 1
Classification of hypertension (HTN) and diabetes (DM), cross-sectional population survey, Ecuador, 2018 (N=3608)
| Variable | Normal blood pressure | PreHTN | HTN | Total |
|---|---|---|---|---|
| n (%)* | n (%)* | n (%)* | n (%)* | |
| Normal blood glucose | 1353 (39.9) | 823 (23.2) | 423 (12.0) | 2603 (75.1) |
| PreDM | 321 (7.5) | 247 (6.5) | 153 (3.9) | 727 (17.9) |
| DM | 79 (1.7) | 104 (2.5) | 105 (2.8) | 291 (7.1) |
| Total | 1753 (49.1) | 1174 (32.2) | 681 (18.7) | 3608 (100) |
* n is unweighted and % is weighted. PreHTN: systolic BP 120–139 mmHg and/or diastolic BP 80–89 mmHg. HTN: systolic BP ≥140 mmHg and/or diastolic BP ≥90 mmHg or taking anti-hypertensive medication in the past two weeks. PreDM: fasting plasma glucose levels 5.6–6.9 mmol/L. DM: ≥7.0 mmol/L or currently on medication for raised blood glucose.
Compared to the group with both normoglycemia and normotension, the group of coexisting PreHTN and PreDM, the group of PreHTN only, and the group of PreDM only, differed in terms of age, sex, marital status, ethnicity, education level, daily smoking, physical activity, body mass index, and elevated TC (Table 2).
Table 2
Normal, prehypertension (PreHTN) and prediabetes (PreDM), PreHTN only, and Pre DM only, crosssectional population survey, Ecuador, 2018 (N=2744)
| Variable | Sample | Normal | PreHTN and PreDM | PreHTN only | PreDM only | pa |
|---|---|---|---|---|---|---|
| n (%) | n (%) | n (%) | n (%) | n (%) | ||
| All | 2744 (100) | 1353 (51.8) | 247 (8.5) | 823 (30.1) | 321 (9.6) | |
| Age (years) | <0.001 | |||||
| 18–29 | 882 (36.6) | 549 (64.7) | 35 (3.8) | 209 (24.2) | 89 (7.3) | |
| 30–49 | 1256 (41.7) | 604 (50.2) | 108 (8.0) | 386 (31.3) | 158 (10.5) | |
| 50–69 | 606 (21.7) | 200 (33.1) | 104 (17.6) | 228 (37.7) | 74 (11.6) | |
| Gender | <0.001 | |||||
| Female | 1639 (52.8) | 909 (57.9) | 122 (6.6) | 359 (21.8) | 249 (13.7) | |
| Male | 1105 (47.2) | 444 (45.1) | 125 (10.7) | 464 (39.3) | 72 (5.0) | |
| Marital status | 0.098 | |||||
| Not married | 1279 (44.1) | 651 (55.8) | 106 (6.5) | 390 (29.5) | 132 (8.2) | |
| Married | 1463 (55.9) | 702 (48.7) | 141 (10.2) | 433 (30.6) | 187 (10.6) | |
| Ethnicity | 0.014 | |||||
| Mestizo | 2145 (79.5) | 1060 (52.1) | 196 (8.7) | 636 (29.6) | 253 (9.7) | |
| Amerindian | 212 (6.6) | 117 (60.8) | 18 (7.2) | 43 (19.6) | 34 (12.4) | |
| Montubio | 190 (6.7) | 84 (44.1) | 16 (9.9) | 75 (37.0) | 15 (8.9) | |
| Afro-Ecuadorian/Mulato | 133 (4.4) | 58 (38.7) | 13 (9.1) | 48 (46.3) | 14 (5.9) | |
| Whites/Other | 63 (2.8) | 34 (63.2) | 4 (3.7) | 20 (25.4) | 5 (7.7) | |
| Education level | <0.001 | |||||
| Lower than secondary | 1391 (45.9) | 618 (45.2) | 145 (10.5) | 438 (32.2) | 190 (12.1) | |
| Secondary | 610 (23.3) | 324 (56.6) | 39 (5.0) | 184 (29.5) | 63 (8.8) | |
| Higher than secondary | 742 (30.8) | 410 (57.7) | 63 (8.3) | 200 (27.4) | 68 (6.5) | |
| Fruit/vegetables (servings) | 0.563 | |||||
| 0–1 | 1650 (59.2) | 816 (50.7) | 151 (8.4) | 490 (30.9) | 193 (10.0) | |
| 2–3 | 833 (31.5) | 401 (52.2) | 76 (9.2) | 249 (28.8) | 107 (9.8) | |
| ≥4 | 254 (9.3) | 132 (57.6) | 20 (7.2) | 81 (29.0) | 21 (6.2) | |
| Salt intake (often/always) | ||||||
| No | 2438 (88.2) | 1195 (50.9) | 224 (8.8) | 739 (30.7) | 280 (9.6) | |
| Yes | 295 (11.8) | 152 (57.5) | 23 (6.9) | 82 (25.9) | 38 (9.7) | |
| Daily smoking | ||||||
| No | 2641 (96.3) | 1315 (52.2) | 231 (8.3) | 781 (29.8) | 314 (9.7) | |
| Yes | 103 (3.7) | 38 (41.9) | 16 (14.4) | 42 (38.0) | 7 (5.6) | |
| Alcohol dependence | 0.037 | |||||
| No | 2440 (88.1) | 1218 (52.3) | 207 (8.1) | 728 (30.0) | 287 (9.7) | |
| Yes | 304 (11.9) | 135 (48.3) | 40 (11.7) | 95 (31.1) | 34 (8.9) | |
| Physical activity (low) | 0.144 | |||||
| No | 2125 (76.3) | 1040 (50.7) | 200 (8.8) | 649 (31.2) | 236 (9.3) | |
| Yes | 615 (23.7) | 313 (55.4) | 47 (7.8) | 172 (26.3) | 83 (10.5) | |
| Body mass index (kg/m2) | <0.001 | |||||
| Normal | 1035 (39.7) | 614 (63.2) | 65 (5.9) | 239 (22.2) | 117 (8.8) | |
| Underweight | 44 (2.0) | 30 (71.9) | 0 (0.0) | 7 (17.7) | 7 (10.3) | |
| Overweight | 1060 (38.5) | 474 (46.4) | 93 (8.1) | 360 (35.1) | 133 (10.4) | |
| Obesity | 601 (19.8) | 233 (37.2) | 89 (15.6) | 216 (37.8) | 63 (9.4) | |
| Elevated total cholesterol | <0.001 | |||||
| No | 2078 (77.2) | 1104 (55.4) | 150 (6.8) | 602 (29.1) | 222 (8.7) | |
| Yes | 666 (22.8) | 249 (39.4) | 97 (14.6) | 221 (33.4) | 99 (12.6) |
Multinomial regression results
Compared to the group with both normoglycemia and normotension, older age (50–69 years) (adjusted relative risk ratio, ARRR=5.91; 95% CI: 3.34–10.45), male sex (ARRR=3.16; 95% CI: 1.08–4.80), obesity (ARRR=4.05; 95% CI: 2.57–6.41), and elevated TC (ARRR=2.24; 95% CI: 1.54–3.26) increased the risk, while having secondary education (ARRR=0.51; 95% CI: 0.32–0.83) decreased the risk of coexisting PreHTN and PreDM. Furthermore, compared to the group with both normoglycemia and normotension, older age (50–69 years) (ARRR=2.43; 95% CI: 1.71–3.46), male sex (ARRR=3.14; 95% CI: 2.42–4.08), being Afro-Ecuadorian/Mulato (ARRR=2.24; 95% CI: 1.29–3.87), overweight (ARRR=2.03; 95% CI: 1.55–2.67), obesity (ARRR=3.07; 95% CI: 2.21–4.28), and elevated TC (ARRR=1.41; 95% CI: 1.07–1.85) increased the risk, while Amerindian ethnicity (ARRR=0.52; 95% CI: 0.35–0.78) decreased the risk of PreHTN only. Finally, compared to the group with both normoglycemia and normotension, older age (50–69 years) (ARRR= 2.20; 95% CI: 1.43–3.40), and elevated TC (ARRR=1.51; 95% CI: 1.07–2.14) increased the risk, while being male (ARRR=0.56; 95% CI: 0.37–0.83), having secondary (ARRR=0.64; 95% CI: 0.42–0.99) and more than secondary education (ARRR=0.53; 95% CI: 0.35–0.79) decreased the risk of PreDM only (Table 3).
Table 3
Multinominal logistic regression with prehypertension (PreHTN) and prediabetes (PreDM), PreHTN only, and Pre DM only (with normal group as reference category), cross-sectional population survey, Ecuador, 2018 (N=2744)
| Variable | PreHTN and PreDM | PreHTN only | PreDM only |
|---|---|---|---|
| ARRR (95% CI) | ARRR (95% CI) | ARRR (95% CI) | |
| Age (years) | |||
| 18–29 ® | 1 | 1 | 1 |
| 30–49 | 1.87 (1.15–3.02)* | 1.39 (1.06–1.83)* | 1.31 (0.89–1.93) |
| 50–69 | 5.91 (3.34–10.45)*** | 2.43 (1.71–3.46)*** | 2.20 (1.43–3.40)*** |
| Gender | |||
| Female ® | 1 | 1 | 1 |
| Male | 3.16 (1.08–4.80)*** | 3.14 (2.42–4.08)*** | 0.56 (0.37–0.83)** |
| Marital status | |||
| Not married ® | 1 | 1 | 1 |
| Married | 1.17 (0.82–1.65) | 0.85 (0.66–1.10) | 1.18 (0.82 – 1.71) |
| Ethnicity | |||
| Mestizo ® | 1 | 1 | 1 |
| Amerindian | 0.68 (0.33–1.38) | 0.52 (0.35–0.78)** | 0.78 (0.41–1.47) |
| Montubio | 0.90 (0.46–1.77) | 1.12 (0.71–1.78) | 0.80 (0.41–1.56) |
| Afro-Ecuadorian/Mulato | 1.50 (0.68–3.30) | 2.24 (1.29–3.87)** | 0.82 (0.40–1.66) |
| Whites/other | 0.31 (0.09–1.09) | 0.70 (0.34–1.44) | 0.69 (0.21–2.23) |
| Education level | |||
| Lower than secondary ® | 1 | 1 | 1 |
| Secondary | 0.51 (0.32–0.83)** | 0.79 (0.59–1.06) | 0.64 (0.42–0.99)* |
| Higher than Secondary | 0.99 (0.65–1.51) | 0.86 (0.65–1.13) | 0.53 (0.35–0.79)** |
| Fruit/vegetables intake (servings) | |||
| 0–1 ® | 1 | 1 | 1 |
| 2–3 | 1.10 (0.76–1.57) | 0.90 (0.68–1.17) | 0.96 (0.69–1.34) |
| ≥4 | 0.75 (0.42–1.33) | 0.83 (0.56–1.22) | 0.65 (0.37–1.14) |
| Salt intake (often/always) | |||
| No ® | 1 | 1 | 1 |
| Yes | 0.83 (0.46–1.49) | 0.83 (0.57–1.19) | 1.06 (0.66–1.71) |
| Daily smoking | |||
| No ® | 1 | 1 | 1 |
| Yes | 1.70 (0.81–3.55) | 1.21 (0.69–2.13) | 1.01 (0.41–2.46) |
| Alcohol dependence | |||
| No ® | 1 | 1 | 1 |
| Yes | 1.38 (0.85–2.24) | 0.83 (0.59–1.17) | 1.59 (0.94–2.68) |
| Physical activity (low) | |||
| No ® | 1 | 1 | 1 |
| Yes | 0.74 (0.48–1.15) | 0.82 (0.62–1.09) | 0.95 (0.67–1.34) |
| Body mass index (kg/m2) | |||
| Normal ® | 1 | 1 | 1 |
| Underweight | - | 0.69 (0.23–2.03) | 1.30 (0.45–3.79) |
| Overweight | 1.43 (0.95–2.17) | 2.03 (1.55–2.67)*** | 1.25 (0.88–1.78) |
| Obesity | 4.05 (2.57–6.41)*** | 3.07 (2.21–4.28)*** | 1.20 (0.76–1.89) |
| Elevated total cholesterol | |||
| No ® | 1 | 1 | 1 |
| Yes | 2.24 (1.54–3.26)*** | 1.41 (1.07–1.85)* | 1.51 (1.07–2.14)* |
DISCUSSION
This national survey among adults (18–69 years) in Ecuador showed a prevalence of 6.5% coexisting PreHTN and PreDM, which appears lower than in Jilin Province, China (11.3%; ≥18 years)10, in northern and northeastern China (11.0%; ≥18 years)11, in the USA (11.2%; ≥20 years)12, and in Nigeria (10.4%; adults)13. Furthermore, we found that the prevalence of PreHTN only (23.2%) and PreDM only (7.5%) was in terms of PreHTN only (34.2%) lower and in terms of PreDM only (4.3%) higher than in Jilin Province, China10. The lower prevalence of PreDM only in China can be attributed to the higher threshold for defining PreDM (IFG: 6.1–6.9)6. The high rates of PreHTN and PreDM in Ecuador may be an indication of the high rate of noncommunicable diseases (NCDs) (72%) that account for all deaths31, reflecting the epidemiological transition.
We found that compared to the group with both normoglycemia and normotension, older age (50–69 years), male sex, obesity, and elevated total cholesterol (TC) increased the risk, while having secondary education decreased the risk of coexisting PreHTN and PreDM; older age (50–69 years), male sex, Afro-Ecuadorian/Mulato, overweight, obesity, and elevated TC increased the risk, while Amerindian ethnicity decreased the risk of PreHTN only; and older age (50–69 years), and elevated TC increased the risk, while being male, secondary or higher education level decreased the risk of PreDM only. Consistent with previous research10-13,16, we found that older age, male sex, lower education level, obesity, and elevated TC increased the risk for coexisting PreHTN and PreDM. Adults with lower education level may have less focus on health and develop PreHTN/PreDM, calling for increased health education for this group10. Obesity and elevated TC increase both PreHTN and PreDM, calling for weight reduction and dietary programs10. Although some previous studies found ethnic differences12,13,16 and an association between physical inactivity and coexisting PreHTN and PreDM, we did not find a significant association. Being obese is a pro-inflammatory condition that is often linked to extensive metabolic changes, such as insulin resistance and blood pressure dysregulation32. The main cardio-metabolic risk factors include, central obesity, raised triglycerides, reduced HDL-cholesterol, raised blood pressure, and raised fasting plasma glucose33.
Furthermore, we found that older age (50–69 years), male sex, being Afro-Ecuadorian/Mulato, overweight and obesity, and elevated TC increased the risk, while Amerindian ethnicity decreased the risk of PreHTN only. Consistent with previous research14,17,18,20, we found that older age, men, obesity, and elevated TC increased the risk of PreHTN. Compared to Mestizos, Amerindians had a lower chance of PreHTN, and being Afro-Ecuadorian/Mulato had a higher chance of PreHTN. Previous studies also identified that the rate of HTN was higher in Afro-Ecuadorians than in other ethnicities34, and at all altitudes indigenous people had lower SBP and DBP compared to Mestizos35, and Amerindians in Ecuador had low prevalence of atrial fibrillation, explained by racially determined short stature and frequent intake of oily fish in the diet36. The higher prevalence of PreHTN in men than women may be attributed to biological and social differences37.
While former studies17-19 found an association between physical inactivity and PreHTN, we found that low physical activity was not significantly associated with PreHTN. Furthermore, we did not find any significant association between lower education level, low fruit and vegetable intake, high salt intake and alcohol use, and PreHTN, as was found in previous research17-20.
In terms of PreDM, we found that older age, and elevated TC increased the risk, and male sex, secondary or higher education level decreased the risk of PreDM. Consistent with some previous studies21-23, we found that older age, lower education level, and elevated TC increased the odds of PreDM. Furthermore, unlike previous research21-23, we did not find significant differences in the prevalence of PreDM in terms of ethnicity, obesity, daily smoking and alcohol use.
Considering that PreHTN and PreDM are reversible conditions38, interventions are indicated, including lifestyle modification and pharmacological treatment12,38,39. Policy implications are that increased local awareness campaigns on PreHTN/PreDM, screening for PreHTN/PreDM to improve early identification and integrated care, including lifestyle interventions, are needed to reduce HTN and DM in Ecuador40.
Limitations
The study was limited because of its cross-sectional design and some variables were assessed by self-report, and other variables such as lipid profile, a family history of HTN and DM, psychological distress, geographical regions, and residence status, were not assessed. A further limitation was that for some of the variables, such as ethnicity, the sample size for some of the response options was small.
CONCLUSIONS
Almost one in ten adults in Ecuador had coexisting PreHTN and PreDM, and several associated factors (older age, male sex, lower education level, obesity, and elevated TC) that can help guide interventions, were identified. The male sex was positively associated with PreHTN/PreDM and PreHTN only, but negatively associated with PreDM only. Compared to Mestizos, Amerindians had a lower chance of PreHTN only and being Afro-Ecuadorian/Mulato had a higher chance of PreHTN only. Early detection and interventions to control both PreHTN and PreDM are indicated to prevent HTNH and DM from occurring.
