Abstract
Abstract
Objectives
To examine trends in the prevalence of diabetes and pre-diabetes in Indonesia from 2013 to 2023 and to explore demographic and socioeconomic factors associated with these changes.
Design
Secondary data analysis on multiseries cross-sectional study.
Setting
Three waves of the Indonesian National Health Survey (2013, 2018 and 2023), each employing nationally representative, stratified multistage sampling.
Participants
Nationally representative respondents aged 15 years and older who completed fasting plasma glucose (FPG) and oral glucose tolerance tests (OGTT).
Primary and secondary outcome measures
Diabetes and pre-diabetes were defined based on FPG and OGTT tests and self-reported diagnosis. Multivariable and ordinal logistic regression models assessed associations between glycaemic status and demographic, socioeconomic and health-related factors.
Results
From 2013 to 2023, the prevalence of diabetes rose from 10.7% (95% CI: 10.2% to 11.2%) in 2013 to 11.8% (11.3% to 12.3%) in 2018, before declining to 11.3% (10.7% to 11.9%) in 2023. Meanwhile, pre-diabetes prevalence decreased from 44.5% (43.6% to 45.3%) in 2013 to 39.2% (38.0% to 40.3%) in 2023. Age-standardised and synthetic cohort analysis revealed that younger birth cohorts had lower diabetes prevalence at the same age compared with older generations. In contrast, diabetes prevalence remained high and stable among older adults, suggesting that an increase in diabetes prevalence was due to the increase in older population size rather than increased risk. Multivariable regression confirms that higher age and BMI were strong predictors for diabetes, pre-diabetes and abnormal glycaemic states. Wealth quintiles showed different associations: higher wealth was linked to lower pre-diabetes odds, but not consistently to diabetes.
Conclusions
The ageing population drives the rise of diabetes prevalence in Indonesia. Generational improvements were shown among younger adults, while persistent high diabetes prevalence in older adults underscores ongoing challenges. These findings highlight the importance of age-targeted and cohort-targeted screening and prevention strategies.
Keywords: Diabetes Mellitus, Type 2; Epidemiology; Prevalence
Strengths and limitations of this study.
This is the first study to assess the trends of national diabetes and pre-diabetes in Indonesia using repeated waves of standardised household survey data.
Nationally representative sampling across three time points enables robust comparisons across subpopulations and over time.
Standardised biomarker measurements (fasting plasma glucose and oral glucose tolerance tests) were used consistently across all survey years, improving data comparability.
The cross-sectional study design limits the ability to infer individual-level transitions or causality.
Synthetic cohort analysis assumes population representativeness but does not replace longitudinal follow-up for tracking disease progression.
Background
The burden of non-communicable diseases (NCDs) disproportionately affects low-income and middle-income countries (LMICs), where 86% of premature deaths due to NCDs occur. Notably, diabetes has emerged as a significant contributor to this burden. In the USA, the prevalence of diabetes remained relatively stable, ranging from 9.8% to 12.4% between 1988 and 2012. In contrast, China experienced a sharp increase in diabetes prevalence over a similar period, with rates rising from 2.6% in 2002 to 11.9% in 2018.1 2 However, diabetes is not the sole concern, as pre-diabetes, a precursor of diabetes, also increases the risk of developing cardiovascular disease and kidney disease. Studies have shown that over a 12 year follow-up, 13% of individuals with pre-diabetes progress to diabetes, and 23% succumb to complications. The prevalence of pre-diabetes is generally four to five times higher than that of diabetes, and it continues to escalate, particularly in low-income countries. Unfortunately, due to inadequate data collection and registries, pre-diabetes remains an under-reported and neglected NCD in many LMICs. Given these alarming trends, the burden of pre-diabetes should not be underestimated.3 4
Indonesia, the fourth most populous country, faces a challenge in the diabetes epidemic. According to projections, the number of people living with diabetes in Indonesia is expected to rise from 18.69 million (9.19%) in 2020 to 40.7 million (16.09%) by 2045.5 6 Furthermore, the Indonesian National Health Survey 2007 reported that 73.2% of the diabetic population were undiagnosed,7 a higher percentage than countries in the region; for example, Thailand’s undiagnosed diabetes prevalence was 46.1% in males and 23.3% in females in 2009.5 8 9 Given the high prevalence of metabolic and cardiovascular risk factors in Indonesia, the actual burden of diabetes may be even higher than current estimates suggest.10 11 Despite these pressing concerns, research on diabetes and pre-diabetes trends in Indonesia remains limited. This study aims to fill this knowledge gap by investigating the recent trends in diabetes and pre-diabetes prevalence in Indonesia. We examine changes in demographic characteristics and blood glucose profiles from the national health surveys over time.
Methods
Data source and population
This study utilised secondary data from the 2013, 2018 and 2023 Indonesian Health Surveys (known locally as RISKESDAS and SKI) to determine diabetes and pre-diabetes status at the population level. RISKESDAS and SKI are cross-sectional nationally representative health surveys conducted by the Indonesian government every 5 years and employ a complex survey design with stratification based on census blocks to represent district-level representativeness and the national level as well. Participants were selected using a multistage systematic random sampling, with a household response rate of up to 77% in 2023. Details of design and sampling are provided in online supplemental text 1 and table S1. The biomedical test was done on a subsample within the national health survey, which measured fasting plasma glucose (FPG), oral glucose tolerance test (OGTT) and HbA1c (only in 2023). Details on how the test was performed within the timeframe were provided in online supplemental text 2. The inclusion criteria for this study were all respondents aged 15 years and older with complete data regarding both OGTT and FPG tests, for those who had missing values in BMI, wealth quintile, education and occupation were categorised as missing. Exclusion criteria for this study were pregnancy, as the impact of pregnancy on glucose metabolism. Characteristics of those who were excluded are provided in online supplemental table S2.
Outcome
This study assessed the prevalence of diabetes and pre-diabetes following the WHO guidelines on diabetes diagnosis and pre-diabetes classification.12 Diabetes was identified through either laboratory testing or self-reported diagnosis. Laboratory-defined diabetes included FPG≥126 mg/dL (7.0 mmol/L) or OGTT≥200 mg/dL (11.1 mmol/L). Participants who reported a prior diagnosis of diabetes by a healthcare provider were classified as having diagnosed diabetes. Those who met the laboratory criteria but did not report a prior diagnosis were categorised as having undiagnosed diabetes. Pre-diabetes was identified exclusively through laboratory testing and only among participants without a prior diabetes diagnosis. We use both impaired fasting glucose (IFG), indicated by FPG between 100 and 125 mg/dL (5.6–6.9 mmol/L), and impaired glucose tolerance (IGT), indicated by OGTT between 140 and 199 mg/dL (7.8–11.0 mmol/L). The prevalence estimates for diabetes and pre-diabetes in this study differ from those reported by the Ministry of Health (MoH), as MoH reports separately diagnosed diabetes and elevated glucose cases, whereas our analysis combines them into a single measure of diabetes prevalence. Additionally, we applied weighting adjustments to address the over-representation of diagnosed diabetes cases in our sample, with details explained in online supplemental text 3 and figures S1 and S2.
Covariates
Covariates were obtained through household interviews and health examinations. Demographic variables included age, sex and residence (urban or rural), collected via structured questionnaires. Socioeconomic status was assessed using educational attainment, occupation and wealth indicators at both household and district levels. Household wealth was measured using an asset and housing quality index and categorised into national quintiles (quintile 1=poorest; quintile 5=richest). Health-related covariates included Body Mass Index (BMI), waist circumference and smoking status. BMI and waist circumference were measured by trained health professionals using standard protocols. Smoking status was self-reported during interviews. Details on covariate categorisation are provided in online supplemental table S3.
Statistical analysis
Descriptive statistics were used to summarise the baseline characteristics of individuals with diabetes and pre-diabetes. Trends in the prevalence of diabetes and pre-diabetes were assessed across various demographic subgroups, including age, sex, BMI and smoking status. Prevalence estimates for diabetes and pre-diabetes were calculated separately for each survey year (2013, 2018 and 2023). The variation of prevalence between age, BMI on a continuous scale was modelled using spline regression with knots at key percentiles (2.5th to 97.5th) to capture non-linear trends. LOESS was used. A synthetic cohort approach was used by aligning age groups across repeated cross-sectional surveys to approximate cohort trajectories between 2013 and 2023. Specifically, we compared the prevalence of diabetes and pre-diabetes among individuals of the same birth group (eg, those with birth cohort 1980–1989 who aged 25–34 in 2013 with birth cohort 1990–1999 who also aged 25–34 in 2023), allowing for the visualisation of birth group cohort-specific trends. This method assumes that different individuals sampled from the same birth cohort across surveys are representative of that birth group’s experience.
Multivariate logistic regression analyses were used to examine the associations between covariates and the prevalence of diabetes and pre-diabetes. Covariates included survey period (2013, 2018 and 2023), age (categorised into <40, 40–60 and >60 years), sex (male and female), domicile, wealth quintile and BMI (categorised based on standard WHO classifications)13 and smoking status. In the pre-diabetes analysis, individuals with diagnosed diabetes were excluded to isolate factors specifically associated with pre-diabetes. Additionally, an ordinal logistic regression was performed to assess the influence of covariates on the level of dysglycaemic regulation. In this model, participants were classified as ‘non-diabetes’ (coded as 0), ‘pre-diabetes’ (coded as 1) and ‘diabetes’ (coded as 2). Regression results were reported as adjusted ORs (AORs) with 95% CIs. Statistical significance was set at p<0.05. Analyses accounted for the complex survey design using weighted estimates to adjust for selection probability and non-response. All analyses were performed using the ‘survey’ package in R V.4.2.
Sensitivity analysis
We calculated prevalence using different diagnostic thresholds and test types. The results are shown in online supplemental text 4 (online supplemental table S4 and figure S3–S5). We also ran two alternative models for multivariable analyses. The first used overall diabetes (diagnosed and undiagnosed) as the outcome, while the second focused solely on undiagnosed diabetes, excluding diagnosed cases from the analysis. This approach accounts for potential differences in covariate relationships between diagnosed and undiagnosed diabetes, as individuals with a diagnosis may have altered glucose and demographics due to treatment interventions.
Patient and public involvement
Patients and the public were not involved in the design, conduct, or reporting plans of this research.
Results
The study included participants over 15 years with complete FPG and OGTT results from the Indonesian Health Survey (Riskesdas 2013, 2018 and SKI 2023). Our sample characteristics are described in table 1. In 2023, the majority of participants were predominantly between 40 and 60 years old (43.6%), female (62.7%), living in urban residency (56.4%) and in the non-smokers group (75.4%). This trend showed a shift in the sample observed from 2013, which showed the predominant 15–40 year-old group (43.3%) and rural origin (58.0%). In 2023, nearly half of the participants’ BMI was within the normal range, with a notable proportion in the overweight (27.4%) and obese (12.6%) categories.
Table 1. Characteristics of the unweighted sample with complete FPG, OGTT and older than 15 years, from Riskesdas 2013, 2018 and SKI 2023.
| 2013 | 2018 | 2023 | ||||
|---|---|---|---|---|---|---|
| Female | Male | Female | Male | Female | Male | |
| N=15 887 | N=10 561 | N=14 697 | N=8330 | N=12 022 | N=7137 | |
| Age | ||||||
| Mean (SD) | 42.7 (14.9) | 44.5 (16.3) | 43.2 (14.9) | 45.6 (16.1) | 43.6 (14.6) | 46.3 (16.0) |
| Median (min, max) | 42.0 (15.0, 98.0) | 45.0 (15.0, 98.0) | 43.0 (15.0, 98.0) | 46.0 (15.0, 97.0) | 43.0 (15.0, 94.0) | 47.0 (15.0, 97.0) |
| Age group | ||||||
| 40–60 | 6713 (42.3%) | 4552 (43.1%) | 6390 (43.5%) | 3704 (44.5%) | 5234 (43.5%) | 3122 (43.7%) |
| 15–40 | 7224 (45.5%) | 4228 (40.0%) | 6434 (43.8%) | 3060 (36.7%) | 5149 (42.8%) | 2546 (35.7%) |
| >60 | 1950 (12.3%) | 1781 (16.9%) | 1873 (12.7%) | 1566 (18.8%) | 1639 (13.6%) | 1469 (20.6%) |
| Domicile | ||||||
| Rural | 9009 (56.7%) | 6339 (60.0%) | 7536 (51.3%) | 4582 (55.0%) | 5128 (42.7%) | 3208 (44.9%) |
| Urban | 6878 (43.3%) | 4222 (40.0%) | 7161 (48.7%) | 3748 (45.0%) | 6894 (57.3%) | 3929 (55.1%) |
| Education | ||||||
| Primary school | 5695 (35.8%) | 3532 (33.4%) | 4658 (31.7%) | 2576 (30.9%) | 3607 (30.0%) | 1980 (27.7%) |
| Secondary school | 5519 (34.7%) | 4356 (41.2%) | 5700 (38.8%) | 3444 (41.3%) | 5824 (48.4%) | 3723 (52.2%) |
| Tertiary school | 683 (4.3%) | 545 (5.2%) | 749 (5.1%) | 509 (6.1%) | 1039 (8.6%) | 654 (9.2%) |
| Unschooled | 3990 (25.1%) | 2128 (20.1%) | 3590 (24.4%) | 1801 (21.6%) | 1552 (12.9%) | 780 (10.9%) |
| Occupation | ||||||
| Formal sector | 939 (5.9%) | 1279 (12.1%) | 798 (5.4%) | 1031 (12.4%) | 838 (7.0%) | 1016 (14.2%) |
| Informal sector | 3949 (24.9%) | 5220 (49.4%) | 3376 (23.0%) | 4091 (49.1%) | 1923 (16.0%) | 3315 (46.4%) |
| Others | 574 (3.6%) | 399 (3.8%) | 873 (5.9%) | 396 (4.8%) | 1118 (9.3%) | 370 (5.2%) |
| Private sector | 1538 (9.7%) | 1664 (15.8%) | 1670 (11.4%) | 1450 (17.4%) | 1223 (10.2%) | 1514 (21.2%) |
| Student/unemployed | 8887 (55.9%) | 1999 (18.9%) | 7980 (54.3%) | 1362 (16.4%) | 6920 (57.6%) | 922 (12.9%) |
| Wealth quintile | ||||||
| 1 (poorest) | 2653 (16.7%) | 1787 (16.9%) | 3156 (21.5%) | 1779 (21.4%) | 2524 (21.0%) | 1577 (22.1%) |
| 2 | 3469 (21.8%) | 2370 (22.4%) | 2911 (19.8%) | 1705 (20.5%) | 2534 (21.1%) | 1495 (20.9%) |
| 3 | 3782 (23.8%) | 2513 (23.8%) | 2661 (18.1%) | 1486 (17.8%) | 2502 (20.8%) | 1452 (20.3%) |
| 4 | 3421 (21.5%) | 2196 (20.8%) | 2965 (20.2%) | 1688 (20.3%) | 2408 (20.0%) | 1403 (19.7%) |
| 5 (wealthiest) | 2562 (16.1%) | 1695 (16.0%) | 3004 (20.4%) | 1672 (20.1%) | 2054 (17.1%) | 1210 (17.0%) |
| BMI category | ||||||
| <18.5 | 1666 (10.5%) | 1651 (15.6%) | 1104 (7.5%) | 1101 (13.2%) | 772 (6.4%) | 788 (11.0%) |
| 18.5–24.9 | 8500 (53.5%) | 6875 (65.1%) | 6596 (44.9%) | 5119 (61.5%) | 5218 (43.4%) | 4202 (58.9%) |
| 25.0–29.9 | 4126 (26.0%) | 1659 (15.7%) | 4666 (31.7%) | 1636 (19.6%) | 3938 (32.8%) | 1644 (23.0%) |
| ≥30.0 | 1518 (9.6%) | 306 (2.9%) | 2254 (15.3%) | 441 (5.3%) | 1999 (16.6%) | 427 (6.0%) |
| Missing | 77 (0.5%) | 70 (0.7%) | 77 (0.5%) | 33 (0.4%) | 95 (0.8%) | 76 (1.1%) |
| Smoking status | ||||||
| Not smoking | 15 296 (96.3%) | 2403 (22.8%) | 14 048 (95.6%) | 2209 (26.5%) | 11 786 (98.0%) | 2653 (37.2%) |
| Quit smoking | 170 (1.1%) | 1275 (12.1%) | 278 (1.9%) | 1036 (12.4%) | 66 (0.5%) | 519 (7.3%) |
| Smoking | 421 (2.7%) | 6883 (65.2%) | 371 (2.5%) | 5085 (61.0%) | 170 (1.4%) | 3965 (55.6%) |
| Insurance status | ||||||
| Have insurance | 8442 (53.1%) | 5626 (53.3%) | 9666 (65.8%) | 5527 (66.4%) | 9110 (75.8%) | 5358 (75.1%) |
| No insurance | 7445 (46.9%) | 4935 (46.7%) | 5031 (34.2%) | 2803 (33.6%) | 2912 (24.2%) | 1779 (24.9%) |
BMI, body mass index; FPG, fasting plasma glucose; OGTT, oral glucose tolerance tests.
The prevalence of diabetes fluctuates from 2013 to 2023, with 10.7% (95% CI: 10.2% to 11.2%) of adults above 15 having diabetes in 2013, rising to 11.8% (11.3%–12.3%) in 2018 and declining to 11.3% (10.7%–11.9%) in 2023 (table 2). Age-standardised prevalence follows the trend with 11.9% (11.4%–12.5%) in 2013, to 12.3% (11.7%–12.8%) in 2018 and steeper reduction to 11.3% (10.6%–11.9%) in 2023 (figure 1, online supplemental table S5). There was no prevalence difference between urban and rural, with urban showing an increase from 10.6% (9.9%–12.0%) to 11.5% (10.7%–12.3%), while rural showed a rise from 10.8% (10.2%–11.4%) to 11.0% (10.0%–12.0%) (table 2, online supplemental table S5). More pronounced differences show between sexes, with females showing higher prevalence of 11.7% (11.1%–12.4%) in 2013, 13.4% (12.7%–14.0%) in 2018 and 12.6% (11.8%–13.5%) in 2023. Male shows stable prevalence with 9.6% (8.9%–10.3%) in 2013, 10.2% (9.5%–11.0%) in 2018 and 10.0% (9.0%–10.9%). Undiagnosed diabetes remained the dominant contributor to total cases, with 9.1% (8.6%–9.5%) in 2013 to 9.0% (8.4%–9.6%) in 2023. The ratio of diagnosed to undiagnosed diabetes improved from 1:5.9 to 1:3.9.
Table 2. Prevalence of diabetes by urbanicity and sex, based on the Indonesia Health Survey 2013 to 2023.
| Variable | Year | Diabetes | |||
|---|---|---|---|---|---|
| Diagnosed diabetes (A) | Undiagnosed diabetes (B) | Total diabetes (A+B) | Ratio diagnosed to undiagnosed (A)/(B) | ||
| Overall | 2013 | 1.6% (1.4%–1.8%) | 9.1% (8.6%–9.5%) | 10.7% (10.2%–11.2%) | 1:4.2 |
| 2018 | 2.2% (2.0%–2.4%) | 9.6% (9.2%–10.1%) | 11.8% (11.3%–12.3%) | 1:4.4 | |
| 2023 | 2.3% (2.0%–2.6%) | 9.0% (8.4%–9.6%) | 11.3% (10.7%–11.9%) | 1:3.9 | |
| Rural | 2013 | 1.2% (1.0%–1.4%) | 9.6% (9.0%–10.2%) | 10.8% (10.2%–11.4%) | 1:8 |
| 2018 | 1.4% (1.2%–1.6%) | 10.7% (10.0%–11.3%) | 12.0% (11.4%–12.7%) | 1:7.6 | |
| 2023 | 1.7% (1.4%–2.0%) | 9.3% (8.3%–10.2%) | 11.0% (10.0%–12.0%) | 1:5.5 | |
| Urban | 2013 | 2.1% (1.8%–2.4%) | 8.6% (7.9%–9.2%) | 10.6% (9.9%–12.0%) | 1:4.1 |
| 2018 | 2.8% (2.5%–3.1%) | 8.8% (8.2%–9.5%) | 11.6% (10.9%–12.3%) | 1:3.1 | |
| 2023 | 2.7% (2.4%–3.1%) | 8.8% (8.1%–9.5%) | 11.5% (10.7%–12.3%) | 1:3.3 | |
| Male | 2013 | 1.5% (1.2%–1.8%) | 8.1% (7.4%–8.8%) | 9.6% (8.9%–10.3%) | 1:5.4 |
| 2018 | 2.0% (1.7%–2.3%) | 8.2% (7.5%–8.9%) | 10.2% (9.5%–11.0%) | 1:4.1 | |
| 2023 | 2.1% (1.6%–2.5%) | 7.9% (7.0%–8.8%) | 10.0% (9.0%–10.9%) | 1:3.8 | |
| Female | 2013 | 1.7% (1.5%–2.0%) | 10.0% (9.4%–10.6%) | 11.7% (11.1%–12.4%) | 1:5.9 |
| 2018 | 2.3% (2.1%–2.6%) | 11.1% (10.4%–11.7%) | 13.4% (12.7%–14.0%) | 1:4.8 | |
| 2023 | 2.6% (2.2%–2.9%) | 10.1% (9.3%–10.8%) | 12.6% (11.8%–13.5%) | 1:3.9 | |
Prevalence shown in this table was crude prevalence with detailed categories defined as diagnosed diabetes: self-reported diagnosis by a healthcare provider regarding glucose status; undiagnosed diabetes: no history of diabetes and FPG≥126 mg/dL (7.0 mmol/L) or OGTT ≥200 mg/dL (11.1 mmol/L); diabetes: includes both diagnosed and undiagnosed diabetes cases; ratio of diagnosed to undiagnosed diabetes: proportion of diagnosed cases relative to undiagnosed cases.
FPG, fasting plasma glucose; OGTT, oral glucose tolerance test.
Figure 1. Prevalence of (a) diabetes, (b) pre-diabetes, (c) diabetes by sex, (d) diabetes by age group, (e) diabetes by BMI category, (f) diabetes by domicile in Indonesia from 2013 to 2023. BMI, body mass index.
Total pre-diabetes prevalence declined, from 44.5% (43.6%–45.3%) in 2013 to 40.2% (39.3%–41.0%) in 2018 and 39.2% (38.0%–40.3%) in 2023 (table 3), with more marked reductions in rural areas (47.2% to 39.1%) than urban (41.9% to 39.2%). Subtype analysis shows that IFG is the sole contributor to a pre-diabetes reduction (29.9% in 2023 to 21.4% in 2023); meanwhile, IGT slightly increases from 24.5% in 2013 to 26.1% in 2023, the trend was shown in both urban–rural and male–female subgroups (table 3, online supplemental tables S6–S8). The ratio of diabetes to pre-diabetes is shifting from 1:4.2 in 2013 to 1:3.5 in 2023.
Table 3. Prevalence of pre-diabetes by urbanicity and sex, based on the Indonesia Health Survey 2013 to 2023.
| Variable | Year | Pre-diabetes | ||||
|---|---|---|---|---|---|---|
| Isolated IFG (C) | Isolated IGT (D) | IFG and IGT (E) | Total pre-diabetes (C+D+E) | Ratio of diabetes to pre-diabetes (A+B)/(C+D+E) | ||
| Overall | 2013 | 19.9% (19.2%–20.6%) | 14.5% (13.9%–15.1%) | 10.0% (9.6%–10.5%) | 44.5% (43.6%–45.3%) | 1:4.2 |
| 2018 | 13.0% (12.4%–13.6%) | 19.2% (18.5%–19.9%) | 8.0% (7.6%–8.5%) | 40.2% (39.3%–41.0%) | 1:3.4 | |
| 2023 | 13.2% (12.2%–13.8%) | 17.9% (17.0%–18.8%) | 8.2% (7.6%–8.8%) | 39.2% (38.0%–40.3%) | 1:3.5 | |
| Rural | 2013 | 20.9% (20.0%–21.8%) | 15.4% (14.6%–16.1%) | 11.0% (10.3%–11.6%) | 47.2% (46.1%–48.3%) | 1:4.4 |
| 2018 | 13.9% (13.0%–14.7%) | 21.7% (20.7%–22.6%) | 8.6% (8.0%–9.3%) | 44.2% (43.0%–45.3%) | 1:3.7 | |
| 2023 | 12.7% (11.5%–13.9%) | 18.3% (17.0%–19.6%) | 8.1% (7.2%–9.0%) | 39.1% (37.3%–40.9%) | 1:3.6 | |
| Urban | 2013 | 19.0% (17.9%–20.1%) | 13.7% (12.8%–14.6%) | 9.2% (8.5%–9.9%) | 41.9% (40.5%–43.2%) | 1:4.0 |
| 2018 | 13.3% (12.2%–14.4%) | 17.1% (16.2%–18.1%) | 7.5% (6.9%–8.1)% | 36.9% (35.7%–38.2%) | 1:3.2 | |
| 2023 | 16.6% (15.4%–17.7%) | 17.7% (16.5%–19.0%) | 8.3% (7.5%–9.0%) | 39.2% (37.7%–40.8%) | 1:3.4 | |
| Male | 2013 | 23.5% (22.4%–24.7%) | 10.7% (9.8%–11.5%) | 9.8% (9.1%–10.6%) | 44.0% (42.7%–43.3%) | 1:4.6 |
| 2018 | 15.3% (14.3%–16.4%) | 15.7% (14.7%–16.7%) | 7.6% (6.9%–8.3%) | 38.6% (37.2%–40.0%) | 1:3.8 | |
| 2023 | 15.1% (13.7%–16.4%) | 13.9% (12.6%–15.2%) | 7.7% (6.8%–8.6%) | 36.7% (34.8%–38.5%) | 1:3.7 | |
| Female | 2013 | 16.3% (15.6%–17.2%) | 18.3% (17.5%–19.2%) | 10.3% (9.7%–10.9%) | 44.9% (43.9%–46.0%) | 1:3.8 |
| 2018 | 10.6% (10.0%–11.3%) | 22.7% (21.8%–23.6%) | 8.5% (7.9%–9.0%) | 41.7% (40.7%–42.8%) | 1:3.1 | |
| 2023 | 11.0% (10.1%–11.8%) | 22.0% (20.9%–23.2%) | 8.7% (8.0%–9.4%) | 41.7% (40.3%–43.1%) | 1:3.3 | |
Pre-diabetes—isolated IFG: no history of diabetes and FPG 100–125 mg/dL (5.6–6.9 mmol/L) with OGTT < 140 mg/dL (7.8 mmol/L); pre-diabetes—isolated IGT: no history of diabetes and OGTT 140–199 mg/dL (7.8–11.0 mmol/L) with FPG < 100 mg/dL (5.6 mmol/L); pre-diabetes—IFG and IGT: no history of diabetes and FPG 100–125 mg/dL (5.6–6.9 mmol/L) and OGTT 140–199 mg/dL (7.8–11.0 mmol/L); ratio of diabetes to pre-diabetes: proportion of diabetes cases (diagnosed and undiagnosed) relative to all pre-diabetes cases (IFG–IGT and both).
FPG, fasting plasma glucose; IFG, impaired fasting glucose; IGT, impaired glucose tolerance; OGTT, oral glucose tolerance test.
Smoothed prevalence shows that for both diabetes and pre-diabetes, prevalence increased consistently with age, with females exhibiting higher prevalence across all age groups (figure 2). Diabetes prevalence nearly doubled from 5.95% at age 30 to 11.2% at age 40 and further to 24% by age 60. Pre-diabetes prevalence rose from 33.2% at age 20 to 44.1% by age 40, before plateauing. BMI also had a substantial impact on diabetes prevalence, which doubled the prevalence from 8.7% at a BMI of 20 to 18.7% at 30 kg/m². Notably, males had a higher diabetes prevalence than females at BMIs above 27.25 kg/m², with rates of 19.7% and 17.8%, respectively.
Figure 2. Smoothed prevalence of (a) diabetes by age and sex, (b) pre-diabetes by age and sex, (c) diabetes by BMI and sex, (d) pre-diabetes by BMI and sex in 2023. BMI, body mass index.
Synthetic cohort analysis revealed a generational decline in diabetes prevalence among younger adults (figure 3, online supplemental table S9). For females aged 15–24, those born after 2000 had a diabetes prevalence of 3.4% (95% CI: 2.2% to 4.6%), compared with 4.0% (95% CI: 3.2% to 4.9%) in the 1990–1999 cohort. Similarly, males in the same age group showed a decrease from 2.6% (95% CI: 1.8% to 3.4%) in the 1990–1999 cohort to 1.4% (95% CI: 0.8% to 2.1%) in the cohort born after 2000. This generational difference was also observed in the 25–34 age group. For females, the prevalence declined from 7.0% (95% CI: 6.1% to 7.9%) in the 1980–1989 cohort to 4.8% (95% CI: 3.7% to 5.8%) in the 1990–1999 cohort. Among males aged 25–34, diabetes prevalence was 4.8% (95% CI: 3.6% to 5.9%) for those born in 1980–1989 and 4.4% (95% CI: 2.8% to 5.9%) for the 1990–1999 cohort. In contrast, among older adults, diabetes prevalence was stable across all birth cohorts. For example, in the 55–64 age group, females born before 1960 had a prevalence of 25.6% (95% CI: 23.7% to 27.6%) and those born in 1960–1969 had a prevalence of 24.2% (95% CI: 22.1% to 26.4%). Among males aged 55–64, prevalence was 21.7% (95% CI: 19.6% to 23.9%) in the cohort born before 1960 and 20.8% (95% CI: 18.2% to 23.3%) in the 1960–1969 cohort.
Figure 3. Diabetes prevalence across age groups and synthetic birth cohorts, stratified by sex. Diabetes prevalence by 10-year age group and synthetic birth cohort FPG ≥126, OGTT ≥200 or diagnosed—survey-weighted with 95% CI. Line plots show the prevalence of diabetes (%) with 95% CIs across 10-year age groups, stratified by synthetic birth cohort for females (left) and males (right). Diabetes was defined as fasting plasma glucose (FPG) ≥126 mg/dL, oral glucose tolerance test (OGTT) ≥200 mg/dL or self-reported diagnosis. Each coloured line represents a different birth cohort, as indicated in the legend. The data are survey-weighted estimates.
The shifts in diabetes prevalence between 2013, 2018 and 2023 were reflected in the AOR after controlling for key covariates (figure 4, online supplemental figure S6). Compared to 2013, there were no statistically significant differences in diabetes prevalence in 2018 (AOR 0.98, 95% CI: 0.92 to 1.06, p=0.669) but a slight reduction in 2023 (AOR 0.89, 0.82–0.97, p=0.00.06). In contrast, there was a significant decline in pre-diabetes over time, with AORs of 0.78 (95% CI: 0.74 to 0.82, p<0.001) in 2018 and 0.73 (95% CI: 0.69 to 0.78, p<0.001) in 2023, compared with 2013. Older age and higher BMI remained strong predictors for diabetes, pre-diabetes and abnormal glycaemic states. For diabetes, compared with the 40–60 reference, individuals aged ≥60 years had significantly higher odds (AOR 1.80, 95% CI: 1.66 to 1.94, p<0.001), while the youngest group (15–40 years) had markedly lower odds (AOR 0.27, 95% CI: 0.25 to 0.29, p<0.001). Compared with BMI <18.5, obesity (BMI≥30) was associated with more than double the odds of diabetes (AOR 2.11, 95% CI: 1.75 to 2.54, p<0.001), and similar patterns were observed for pre-diabetes and abnormal glycaemic states. Male sex was associated with lower odds of diabetes (AOR 0.80, 95% CI: 0.75 to 0.86, p<0.001). J-shaped association between wealth and diabetes was shown where the second quintile showed statistically significantly lower odds of diabetes (AOR 0.89, 95% CI: 0.80 to 0.99, p=0.034) compared with the lowest quintile, while the association was not significant for higher quintiles. In contrast, the third, fourth (AOR 0.87, 95% CI: 0.80 to 0.96, p=0.004) and fifth quintiles have a lower risk of pre-diabetes (AOR 0.81, 95% CI: 0.74 to 0.88, p<0.001).
Figure 4. Forest plot of adjusted OR in determining (a) diabetes prevalence, (b) pre-diabetes prevalence and (c) total abnormal glycaemic states prevalence.
Discussion
The number of diabetes cases in Indonesia increased from 18.6 million (11.1%) in 2013 to 23.9 million (11.3%) in 2023. Intergenerational synthetic cohort analysis shows a lower prevalence of diabetes in the same age group in the younger generation, which means the overall increase in Indonesian diabetes cases appears to be largely driven by the increase in older population size rather than the odds of having diabetes between 2013 and 2023. While the prevalence of pre-diabetes declined, from 44.5% to 39.2%, the absolute number of pre-diabetes cases still rose, from 73.6 million in 2013 to 83.1 million in 2023, driven by the expanding population.
Our results are aligned with estimates from the Indonesia Family Life Survey (IFLS), with slightly higher prevalence numbers. The 2014 IFLS reported a diabetes prevalence of 9.1%, with a similar pattern of higher prevalence in females.14 IFLS estimates HbA1c for diagnosis, whereas our study used a combination of FPG and OGTT. The International Diabetes Federation (IDF) reported a national prevalence of 9.8% in 2021.6 In contrast, our findings differ substantially from the Global Burden of Disease (GBD) 2021 estimates, which reported 12.1 million diabetes cases in Indonesia, corresponding to a prevalence of 4.35% (95% CI: 3.93 to 4.81). A notable discrepancy was also observed in sex-specific patterns: while GBD estimated a higher prevalence among males, our analysis indicated a greater burden among females.6 15 These variations across sources likely stem from differences in case definitions (inclusion of diagnosed diabetes), diagnostic criteria (eg, inclusion of HbA1c vs FPG/OGTT) and population sampling. As previous studies have shown, diabetes prevalence can vary considerably depending on the biomarker used and the thresholds applied.16 17
Global and regional comparison
The regional diabetes prevalence in Southeast Asia (SEA) (WHO country grouping) was estimated at 10%, making it the third-highest region in the world after the Middle East, North Africa (18.1%) and North America and the Caribbean (11.9%).6 In contrast, Europe reported a lower prevalence of 7%, and South and Central America had a prevalence of 8.2%. This heightened the link between South Asia and SEA in dietary patterns of white rice to increase susceptibility to diabetes incidence.18 Genetic susceptibility to diabetes in SEA populations has been well documented, with factors such as higher visceral fat, increased inflammatory markers and insufficient β-cell responses contributing to a greater risk of diabetes.19 20 Within the Association of Southeast Asian Nations (ASEAN) region, IDF estimates Indonesia experienced the second-greatest increase in diabetes prevalence from 2011 to 2021 after Malaysia, and it had a higher prevalence than Thailand, with 9.7%, and Cambodia, with 8.2%.6 Indonesia’s pre-diabetes prevalence is comparable to that of other countries. For instance, Thailand recorded 38.8% of total pre-diabetes (both IFG and IGT), while the USA and China, where total pre-diabetes prevalence was reported at 44.2% and 38.1%, respectively.3 21
Trend in diabetes and pre-diabetes
The reduction in age-standardised pre-diabetes was followed by a very slight decrease in age-standardised diabetes populations. Synthetic cohort shows the reduction not only between generations but also within generations. Other studies in India reported a similar pattern, showing a concomitant decrease in pre-diabetes (16.7% in 2006 to 8.4% in 2021) and an increase in diabetes.22 Such a scenario has also been well documented in South Asia and Mexico.23,25 Most of the authors predicted that the trend was explained by a shift from pre-diabetes towards diabetes. Previous studies postulated two possibilities of scenarios: either the epidemic is plateauing due to the shrinking pre-diabetes population, or there is an acceleration in the diabetes epidemic.26 27
Our results demonstrate that the decline in pre-diabetes prevalence was greater than the observed increase in diabetes. The change of proportion between pre-diabetes and diabetes was more prominent among adults aged 60 years and older, whereas a shift towards normoglycaemia was predominantly seen in individuals aged 15–40 years. Synthetic cohort analysis further revealed that younger birth cohorts consistently exhibited lower diabetes prevalence at the same age compared with older generations, suggesting an improvement in risk profile among younger adults.
A more detailed analysis indicated that the overall decline in pre-diabetes was primarily driven by a reduction in IFG, rather than IGT. Although IFG and IGT share overlapping risk factors, IGT is more closely associated with unhealthy diet and physical inactivity, while IFG is more strongly linked to genetic predisposition, smoking and male sex.27 Factors such as lower systolic blood pressure, weight loss and absence of cardiac abnormalities have been associated with a greater likelihood of reverting to normoglycaemia. Notably, IFG has been shown to carry a lower risk of progression to diabetes and a higher probability of reversion to normoglycaemia. We hypothesise that the steady increase in IGT was the reason for shifting towards diabetes. Additionally, the IGT increase was higher among those within the 40–60 year-old group, suggesting that early intervention may be given to delay the epidemic.28 29 The presence of NCD programmes such as NCD integrated Service in Primary Health Centres (Pandu), NCD Integrated Posts (Posbindu) and Chronic Disease Service (Prolanis) likely contributed to the reduction of IFG through early detection and management.4 5
Subpopulation variation
Females show a higher prevalence than males across all age groups, which is consistent with previous studies in Indonesia.7 14 This contrasts with findings from neighbouring countries like Thailand and Malaysia, where males show a higher burden of diabetes.30 31 The greater prevalence among Indonesian women may be attributed to higher BMI and a greater association with multimorbidity, particularly hypertension, compared with their male counterparts.32 33 A sharp increase in prevalence was observed between the ages of 30 and 40, where prevalence doubled from 5.7% to 11.3% and to 24.3% by age 60. This pattern suggests that targeted screening should be prioritised for these age groups. Similar findings have been consistently documented in other studies, where ageing is strongly linked to diabetes risk due to β-cell degeneration and increased insulin resistance.7 14 Moreover, a previous study showed that the gap between diagnosed and undiagnosed diabetes begins around age 31, with a significant rise in undiagnosed cases compared with diagnosed ones (0.7% vs 5.7%, respectively), indicating the need for early detection efforts.7 14 Other countries, such as India, report similar trends, with diabetes prevalence rising after age 30 and peaking between ages 50 and 54, while in Thailand, an increase is observed as early as the 20–39 age group, plateauing before age 60.8 34 These international comparisons highlight the importance of age-specific screening strategies, though national screening programmes pose challenges in developing countries like Indonesia, where diversity and geographic barriers complicate implementation.35
Obesity is a well-known, major risk factor for diabetes, especially in Asian populations, where visceral fat accumulation accelerates β-cell degeneration through lipotoxicity, leading to chronic hyperglycaemia.7 36 In the interaction term, males show faster progressivity on the risk of diabetes with an increase in BMI, especially after a BMI above 27.25 kg/m2. Such a phenomenon might occur due to the male tendency to accumulate visceral fat, which is more linked to diabetes.37 However, females are more likely to store subcutaneous fat at lower BMI due to their oestrogenic effects, combined with greater exposure to hormonal changes such as pregnancy and polycystic ovarian syndrome, they are more prone to insulin resistance at lower BMI.38 Central obesity has been reported to promote inflammatory pathways and insulin resistance faster than subcutaneous tissue. According to other studies, the risk of metabolic syndrome has markedly increased among those with a BMI above 27 kg/m2 or within the overweight range.39 40
Wealth quintiles have a notable effect on pre-diabetes but not on diabetes prevalence, which could be attributed to two potential explanations. First, survival bias may be at play among individuals in lower wealth quintiles, who typically have reduced access to healthcare. As a result, those with diabetes in these groups may have higher mortality rates, leading to an under-representation of diabetes cases, especially after the COVID-19 pandemic, which could obscure any observable differences in prevalence across wealth quintiles.41 42 Second, disparities in the timing of risk factor transitions for NCDs between wealthier and poorer populations may contribute to this pattern. The higher prevalence of pre-diabetes within lower wealth quintiles is a more recent development, possibly driven by improved nutritional status over the past decade. However, this shift may not yet have fully manifested as a corresponding increase in diabetes prevalence. Both scenarios could occur synergistically, as Indonesia is currently experiencing transitions in its demographic and economic status.43 44
This study’s strength lies in the use of three consecutive waves of nationally representative health surveys using consistent protocols. The combination of self-reported diagnosis with standardised laboratory testing captured both diagnosed and undiagnosed diabetes, providing comprehensive prevalence estimates. Synthetic cohort analysis offered insights into generational differences, while complex survey adjustments ensured representativeness at national and subnational levels. However, it has several limitations. First, the cross-sectional design limits the ability to establish causal relationships between the observed trends and the associated risk factors. Second, classification of ‘diagnosed diabetes’ relied on participants’ self-report of a prior diagnosis by a healthcare provider, without confirmation from medical records; this is a common approach used in other national and multinational studies, and it may introduce recall bias. However, the true prevalence estimates are less likely to be affected, as laboratory measurements were also used to identify undiagnosed cases. Third, the survey did not distinguish between type 1 and type 2 diabetes; as such, the reported prevalence reflects both types combined, although in the Indonesian adult population, type 2 diabetes is expected to account for the vast majority of cases.
Conclusion
Between 2013 and 2023, the prevalence of diabetes in Indonesia slightly declined, but the absolute number of cases increased due to population ageing. Younger generations showed lower prevalence at the same age than older cohorts, suggesting improvements in early-life risk profiles. The largest relative increases were seen among middle-aged adults with higher BMI, making this group a priority for prevention, early detection efforts and comprehensive management.
Supplementary material
Acknowledgements
We acknowledge the Health Policy Agency, Ministry of Health, Indonesia, which provided us with the National Health Survey data.
Footnotes
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Prepublication history and additional supplemental material for this paper are available online. To view these files, please visit the journal online (https://doi.org/10.1136/bmjopen-2024-098575).
Provenance and peer review: Not commissioned; externally peer reviewed.
Patient consent for publication: Not applicable.
Ethics approval: Ethical approval for the 2013–2023 RISKESDAS surveys was granted by the Ethical Committee of Health Research, NIHRD, Ministry of Health, Republic of Indonesia (no. LB.02.01/2/KE.267/2017). I conducted a secondary analysis of the 2018 RISKESDAS data set through the NIHRD data management laboratory under this clearance. Additional approval for secondary data analysis was obtained from the Harvard University Institutional Review Board (IRB23-1673).
Data availability free text: The data used to support this study are available from the Data Management Laboratory of layanandata.kemkes.go.id by Indonesia’s Ministry of Health on reasonable request with prior official written permission.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting or dissemination plans of this research.
Data availability statement
Data are available upon reasonable request. Data may be obtained from a third party and are not publicly available.
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