Metabolic and genetic risk factors associated with pre-diabetes and type 2 diabetes in Thai healthcare employees: a long-term study from the Siriraj Health (SIH) Cohort Study

doi:10.21203/rs.3.rs-3603572/v1

Download PDF

Research Article

Metabolic and genetic risk factors associated with pre-diabetes and type 2 diabetes in Thai healthcare employees: a long-term study from the Siriraj Health (SIH) Cohort Study

https://doi.org/10.21203/rs.3.rs-3603572/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

The study of non-communicable diseases (NCDs) in a developing country like Thailand has rarely conducted in long-term cohort, especially in working-age population. We aim to assess the prevalence and incidence of risk factors and their associations underlying NCDs, especially type-2 diabetes mellitus (T2DM) among healthcare workers enrolled in the Siriraj Health (SIH) study cohort.

Methods

SIH study was designed as a longitudinal cohort and conducted at Siriraj hospital, Thailand. A total 5,011 participants (77% Female) were recruited and follow-up. Physical examinations, blood biochemical, family history, behavior and genetics factors were assessed.

Results

The average age was 35.44 ± 8.24 years and 51% of participants were overweight and obese. We observed men were more likely to have prevalence to T2DM and dyslipidemia (DLP) more than woman. Obese were significantly increased with prediabetes and T2DM (P < 0.001). Additionally, aging, obesity, metabolic syndrome, and DLP were associated with the development of prediabetes and T2DM. The minor T allele of the rs7903146(C/T) and rs4506565 (A/T) was associated with high risk of development of T2DM with an odds ratio of 2.74 (95% confidence interval [CI]: 0.32–23.3) and 2.71 (95% CI: 0.32–23.07), respectively; however, they were statistically insignificant (P > 0.05).

Conclusion

The SIH study's findings provide a comprehensive understanding of the health status, risk factors, and genetic factors related to T2DM in a specific working population and highlight areas for further research and intervention to address the growing burden of T2DM and NCDs.

Non-communicable diseases

Type 2 diabetes mellitus

Metabolic syndrome

Risk factor

Non-communicable diseases (NCDs) represent a global health crisis, driven by factors such as rapid urbanization, unhealthy diets, physical inactivity, and an aging population [1–4]. Metabolic risk factors, including high blood glucose, dyslipidemia, and obesity, contribute to 41 million NCD-related deaths annually, primarily among those aged 30 to 69 years [5–7]. Thailand, an upper middle-income country (UMIC), has seen a substantial rise in its older adult population from 10.7% in 2007 to 19.2% in 2020, with a projected surge to 23% by 2030, aligning with the broader trends observed in the South Eastern Asia region (12.2 to 16.7% in 2020; and 15.7 to 20% in 2030, respectively) [8, 9]. In 2018, half of Thailand's population resided in urban areas, and the United Nation predicts that 68% of the global population will be urban dwellers by 2050 [10]. This demographic shift poses substantial challenges to healthcare and sustainable development. Recent data from the Thailand National Health Examination Survey show nearly 30% of Thai adults over 40 exhibit dysglycemia, mainly concentrated in urban areas [11]. Obesity, hypertension (HTN), and type-2 diabetes mellitus (T2DM), NCD risk factors, have surged in Thailand in the last decade [12–14].

Promoting healthy diets and physical activity is vital to mitigate NCDs across the lifespan [15, 16]. Maintaining good glycemic control can prevent microvascular complications [17]. Addressing modifiable risk factors early and empowering younger populations with relevant knowledge and interventions are crucial steps. Urbanization and mobility bring both challenges and opportunities for research. A quarter of Thai Open University students moved from rural to urban areas from 2005 to 2009, impacting their health behaviours [18]. The Electricity Generating Authority of Thailand (EGAT) cohort study found diverse socioeconomic backgrounds, a wealthier profile, higher male participation in smoking and alcohol, and an upper-class healthcare scheme [19]. Longitudinal urban cohort studies focused on NCDs are essential.

Therefore, we established a long-term cohort database at a Thai urban medical university, implementing rigorous data quality control. A biobank has been established to store specimens for the assessment of genetic factors pertinent to NCD development, especially T2DM. The TCF7L2 gene plays a crucial role in pancreatic β-cell proliferation and insulin secretion regulation. Changes in this pathway can lead to T2DM [20]. A common single nucleotide polymorphism (SNP) in the TCF7L2 gene region is associated with T2DM [21]. Our study aims to explore NCD risk factors, biomarker relationships, and develop a T2DM risk prediction model while investigating the association between T2DM and genetic variants of the TCF7L2 gene.

Study population

The Siriraj Health (SIH) study, conducted at Siriraj Hospital, Mahidol University, Bangkok, Thailand (Fig. 1), is a comprehensive longitudinal cohort comprising diverse healthcare professionals (doctors, nurses, pharmacists, medical technicians etc.), support staff (drivers, engineers, security officers, clerks etc.), and academic personnel (lecturers, researchers, research assistants etc.). There were 20,967 individuals working in this university hospital and approximately 80% undergo annual health check-ups. SIH provides a platform for in-depth studies of extensive, long-term data, and biological specimens from Siriraj hospital personnel based on an annual health screening surveillance program. This study estimated the sample size at 5,000 individuals based on NCD prevalence with a 95% confidence interval (CI) and standardized for age and sex according to the total population of the study [11]. Between September 2017 and December 2019, 4,518 participants aged 18 to 55 years were enrolled in phase 1 (SIH1). Beginning in January 2020, SIH in phase 2 (SIH2) included participants who were aged over 55 years. However, due to COVID-19 pandemic in 2020–2022, only 493 participants were enrolled. A total of 5,011 participants are illustrated in Fig. 1. The follow-up data from health check-up in 2020 of SIH1’s participants were retrieved from electronic medical records in the hospital database. Out of the 4,518 people enrolled in the first cohort (SIH1), 484 participants (10.7%) were lost to follow-up in 2020 due to death, changes of a workplace, and loss of contact.

Data collection

During the annual health check-up, Siriraj’s personnel were provided some initial information about the SIH cohort study. Written informed consent was obtained from all participants before enrollment and specimen collection. The SIH study was performed according to the principles established by the Declaration of Helsinki and was approved by the Ethics Committee of the Human Research Protection Unit, Faculty of Medicine Siriraj Hospital, Mahidol University board (SIRB 508/2559). We provided instructions on the study's standard operating procedures (SOPs) to a team of ten well-trained research staff members involved in the SIH study. These staff members were responsible for securing informed consent, conducting face-to-face questionnaire interviews, and collecting biological specimens. This collection took place within a well-equipped laboratory equipped with a standard biobank for preserving deoxyribonucleic acid (DNA) and plasma samples. A decoded identification number was generated for each participant, which was used to label all biological specimens and subject data. The management of study data was entrusted to a bioinformatician utilizing the R program (version 4.1.3, Revolution Analytics, Dallas, TX, USA) and Research Electronic Data Capture (REDCap, version 12.0.13, Vanderbilt, TN, USA) [22]. The computer servers were located at the Siriraj Informatics and Data Innovation Center (SiData+), part of the Faculty of Medicine Siriraj Hospital, Mahidol University.

Physical examinations

Trained nurses from the Department of Preventive and Social Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, interacted with participants using established standard procedures. Data measurements included weight, height, waist circumference (WC), and blood pressure. Weight measurements were obtained to the nearest 0.1 kg (Tanita BWB-800, Tanita Corporation, Tokyo, Japan), while standing height was measured to the closest 0.1 cm. WC was determined using a flexible, non-stretchable plastic tape positioned across the midpoint between the lowest rib and the upper lateral border of the right iliac crest. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) measurements were conducted using a digital blood pressure monitor (HEM-907, Omron Corporation, Tokyo, Japan).

Laboratory measurements

A 34 ml venous blood sample was collected after 12-hour fast and 30 ml of urine were collected from each participant. To prevent the occurrence of hypoglycemic events, all blood samples were collected between 07:00 and 09:00 A.M. Blood samples were distributed into four anticoagulation tubes for complete blood count (CBC), glycated hemoglobin (HbA1c) analysis, plasma separation, and DNA extraction. A separate sodium fluoride tube inhibited glycolysis for fasting blood glucose (FBG) analysis, while a lithium heparin tube facilitated total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), creatinine (Cr), estimated glomerular filtration rate (eGFR) assessment. Urine samples were analyzed for spot urine albumin-to-creatinine ratio (MAU/Cr). After centrifugation at 3,000 rounds per minute for 10 minutes at 4 ºC, blood samples were aliquoted into cryotubes (Thermo Fisher Scientific, Jiangsu, China). Biochemical assays were performed using automated systems with various methods by an accredited clinical laboratory (Siriraj hospital, Thailand) as shown in supplementary Table S1.

DNA extraction and genotyping

Human DNA was extracted from 4 ml of whole venous blood using the Chemagic 360 automated platform and Chemagic DNA blood kits (CMG-1074, PerkinElmer, Baesweiler, Germany), following the manufacturer’s instructions. DNA concentration and a purity ratio (within the acceptable in range 1.8–1.9) were determined using FLUOstar Omega (software version 5.5 R4, BMG LABTECH, Ortenberg, Germany). DNA samples were stored at -80°C in the Siriraj Biobank. Genotyping involved the analysis 3,960 DNA samples for SNPs using Infinium Asian Screening Array (ASA, Illumina, San Diego, CA, USA). The basic microarray technical data for ASA were downloaded from Illumina official website (http://www.illumina.com). An extensive analysis of 659,184 SNPs was analyzed from each individual. Before conducting the genotyping analysis, we assessed the quality call rate for both samples (97% cutoff) and SNPs (90% cutoff), excluding those with call rates below the specified thresholds. Additionally, a comparison was made between the reported sex based on demographic data and the sex predicted from genotypes. Genotyping analysis was performed using the PLINK program, version 1.9 (supplementary Fig. S1 - S3, Table S2-S3).

Self-report underlying diseases and family histories

Participants completed questionnaires about underlying diseases (T2DM, HTN, dyslipidemia (DLP), ischemic heart, stroke, and gout) and family history (heart failure, cancer, and stroke). HTN was defined as a SBP and DBP above the threshold as mentioned above or the current use of antihypertensive medication [23]. The diagnostic HbA1c criteria for non-diabetes (non-DM) was < 5.7%, prediabetes (pre-DM) 5.7–6.4%, and diabetes mellitus (DM) > 6.4%, or the current use of DM medication [24]. Individuals reported that they did not have DM but they had abnormal biochemical measurements in range of pre-DM were classified as unaware pre-DM [25] (supplementary Fig. S4).

Behavioral risk factors

Participants completed questionnaires about exercise, smoking, and alcohol consumption. Regular exercise was defined as an exercise at least once a week. Smoking was defined as either a former smoker or a current smoker. Alcohol drinking habits were defined as individuals consumed alcohol at least once a month in the past year. A semi-quantitative food frequency questionnaire on how often and how much food individuals have eaten over specific periods will be implemented in the next survey in late 2023. This process was validated alongside with the cohort study [26].

Statistical analysis

All continuous variables in the demographic data were expressed as the mean ± standard deviation, whereas binary variables were expressed as numbers and percentages. Self-report data were expressed as percentage with 95% CI. Statistical analyses were performed using multinomial logistic regression by STATA version 14 (STATA Corp., Texas). Genotype frequencies were tested for Hardy-Weinberg equilibrium (HWE) using the HWE Institute of Human Genetics calculator (https://ihg.gsf.de/ihg/index_engl.html) for both cases and controls through the Pearson χ² test. The relationships between the two groups (T2DM and non-DM) were analyzed using R version 3.3.1. All statistical tests performed in this study were two-tailed, and a significance level of P < 0.05 was considered statistically significant.

Among all 5,011 participants, predominantly women (3,854 [77%]) with an average age of 35.44 ± 8.24 years, the study revealed that 51% of participants were overweight or obese (Table 1). Notably, men showed significantly higher prevalence of obesity, abdominal obesity, and HTN compared to women. Men were also more likely to have T2DM (elevated FBG and/or HbA1c) and DLP (high TC, TG, and LDL-C, but low HDL-C) compared to women (P<0.001).

A total of 1,464 participants (29.2%) met the criteria for MetS, with a higher prevalence among men (544 [47.0%]) than women (920 [23.9%]). Furthermore, 2,540 participants (50.6%) reported regular exercise (at least once a week), and 3,168 (63.2%) reported monthly alcohol consumption. During follow-up, data from 4,038 participants showed significant increases in body mass index (BMI), WC, SBP, DBP, FBG, MetS, and LDL-C levels, particularly among women. Meanwhile, eGFR and Cr levels declined over time. Unfortunately, follow-up data were limited to routine electronic health check-ups; therefore, data on HbA1c, underlying diseases, family history, and lifestyle were not available.

Among the participants, 279 (6%) were identified with T2DM. Remarkably, 3,487 participants (72%) who denied having T2DM had normal HbA1c levels, while 1,055 (22%) had unaware pre-DM. Additionally, 70 participants (1.5%) had unaware T2DM (HbA1c > 6.4%) (see supplementary Fig. S4). The factor-stratified prevalence of diabetes status highlighted higher pre-DM and T2DM rates among older adults, particularly those aged ≥ 40 years, who were associated with being overweight, obese, smoking, and alcohol consumption (Table 2). Most individuals with pre-DM and T2DM had concurrent HTN and DLP, indicated by elevated TG and LDL-C, but decreased HDL-C. Among those with T2DM, 12.2% had microalbuminuria, and 5.9% had macroalbuminuria. These findings emphasize the importance of health promotion efforts to prevent T2DM and related complications among the working-age population since these are modifiable risk factors.

Risk factors for the development of pre-DM and T2DM were analyzed using a multivariate multinomial logistic regression model (see Table 3). Notably, several factors were significantly associated with pre-DM, including being in the age range of 30-39 years (relative risk ratio; RRR = 1.77, 95% CI: 1.40-2.25), being 40 years or older (RRR = 3.82, 95% CI: 3.00-4.86), obesity (RRR = 2.81, 95% CI: 2.01-3.92), and the presence of MetS (RRR = 1.55, 95% CI: 1.16-2.08). Interestingly, these same factors exhibited even stronger and statistically significant associations with T2DM compared to pre-DM. Furthermore, specific variables, such as TG levels and MAU/Cr, were significantly associated with T2DM but not pre-DM. For example, the presence of microalbuminuria (RRR = 2.78, 95% CI: 1.66-4.64), macroalbuminuria (RRR = 5.19, 95% CI: 2.17-12.42), hypertriglyceridemia (RRR = 1.81, 95% CI: 1.25-2.61), and low HDL-C levels (RRR = 1.61, 95% CI: 1.12-2.33) significantly increased the risk of T2DM but did not demonstrate the same associations with pre-DM. It is noteworthy that individuals with pre-DM displayed an elevated risk of progressing to T2DM, particularly when combined with MetS, microalbuminuria, macroalbuminuria, hypertriglyceridemia, or low HDL-C levels.

In this study, we observed seven SNPs within the TCF7L2 gene, including rs7903146 (C/T), rs12255372 (G/T), rs7917983 (C/T), rs4506565 (A/T), rs4132670 (C/T), rs12243326 (C/T), and rs290487 (C/T). The distribution of genotypes and allelic frequencies for these TCF7L2 gene polymorphisms was presented in Table 4. Notably, the minor T allele of both rs7903146 (C/T) and rs4506565 (A/T) SNPs showed a trend toward increased risk for developing T2DM, with odds ratios (OR) of 2.74 (95% CI: 0.32-23.3) and 2.71 (95% CI: 0.32-23.07), respectively. However, these associations did not reach statistical significance (P>0.05).

The SIH study distinguishes itself as a comprehensive, population-based cohort encompassing a diverse spectrum of hospital personnel, comprising healthcare practitioners, supporting staff, and academic faculty. An intriguing aspect pertains to the pronounced disparity in the gender composition of this cohort, with women constituting 77% of the participants. The longitudinal framework of this cohort offers a unique vantage point for monitoring health dynamics and disease progression over time. Among the 5,011 individuals drawn from the personnel of Siriraj Hospital, we achieved a remarkable 80.5% follow-up completion rate. Our findings illuminate the prevalence of critical metabolic risk factors within this working population, including obesity, abdominal obesity, HTN, T2DM, DLP, and MetS. These revelations underscore the pressing health challenges confronting this cohort. Notably, the prevalence of T2DM and MetS exhibited an upsurge with advancing age and increasing BMI, reflecting of a compelling association with both the aging demographic and the escalating obesity epidemic. This heightened prevalence carries significant implications for individual well-being and places an onus on healthcare systems, emphasizing the urgent need for preventive strategies and health promotion efforts.

In our study, we displayed a T2DM prevalence of 6% among the 5,011 participants, a figure that closely aligns with the 6th Thai National Health Examination Survey (NHES VI), which reported a 5.7% prevalence of diabetes among Thais aged 30–44 years [27]. Moreover, the observed T2DM prevalence in our study is similar to the 3.8% prevalence documented among 2,790 university hospital employees in Bangkok [28] and the 2.7% prevalence reported among 3,360 employees of the EGAT in Nonthaburi, a adjacent province of Bangkok [29]. The other striking revelation from this study was that 21% and 1.4% of participants were in unaware pre-DM and T2DM conditions, respectively. These findings indicate a significant association between T2DM and factors such as being overweight, obesity, smoking, and alcohol consumption, consistent with observations in heterogenous populations [30, 31]. Furthermore, HTN and DLP emerged as common comorbidities in the T2DM cohort, aligning with observations in diverse ethnic populations such as India and Saudi Arabia [32, 33].

Our multivariate analysis revealed several pivotal risk factors associated with the development of T2DM. Age, obesity, MetS, and DLP prominently featured in this array. Notably, the strength of association for these predictor variables varied depending on diabetes status. For instance, while DLP, microalbuminuria, and microalbuminuria were significantly associated with T2DM, they did not manifest such associations with pre-DM. The observed increasing odds of diabetes with age concurs with findings from other studies [34, 35], attributed to elevated glycated hemoglobin levels and changes in insulin sensitivity [36, 37]. Identifying adults with pre-DM assumes paramount importance for initiating early preventive or treatment measures, thereby mitigating the burden of diabetes, and reducing healthcare expenditures. In contrast to previous research indicating that women had a lower diabetes risk than men [38], our study suggests that women exhibited a heightened risk of pre-DM than men, potentially due to a higher proportion of women in the cohort leading to increased detection rates.

While our study aimed to explore the interplay between T2DM and genetic variants of the TCF7L2 gene, the results indicate that no substantive evidence to substantiate a significant association between the analyzed variants and an augmented risk of T2DM within this cohort. Nevertheless, we acknowledge that other risk factors may play an influential role in the development of T2DM development, thereby underscoring the complexity of the disease and the need for multifaceted investigations. Genotypic distribution analysis showed minimal variability of glycemic control between CC (wild genotype) and TT (mutant) of rs7917983 and rs290487 across all groups. The mutant genotype was found in all group, except for rs12255372, rs4132670, and rs12243326 in the T2DM group, an observation that may be ascribed to the inclusion of younger participants in our study. Our findings align with prior studies that found no significant association of the SNPs with T2DM [39, 40]. Similar results were obtained in studies where the T allele had no impact on the association with T2DM [41, 42]. In contrast to our study, others concluded that the presence of rs7903146 C/T SNP was associated with an increased T2DM risk, particularly the homozygous TT genotype [43, 44]. Although no statistically significant difference emerged between the T2DM and non-DM groups regarding genotype or allele frequencies, the calculation of OR indicated that the genotypic distributions of the TCF7L2 rs7903146 (TT) and rs4506565 (TT) polymorphisms carried a risk for T2DM, with an OR (95% CI) of 2.74 (0.32–23.3) and 2.71 (0.32–23.07), respectively.

The strengths of the SIH study encompass its urbanized, population-based design, the availability of repeated measures and biobank specimen storage, and annual follow-up conducted by healthcare specialists to ensure data precision and the punctual sample collection. SIH also recruits a diverse cross-section of individuals characterized by varying educational backgrounds and occupational profiles. This offers an invaluable resource for conducting workplace surveys to monitor transformations in risk determinants and explore causal relationships through interventional trials within a longitudinal cohort. However, our study has some limitations, which warrant consideration. It focuses on hospital personnel, predominantly women, which may restrict the generalizability of findings to the broader population and urban communities. Moreover, loss to follow-up and missing data could introduce bias. Maintaining the cohort and maximizing participant follow-up necessitate substantial efforts. Additionally, data on certain important potential predictors of diseases, such as associations between specific dietary patterns or eating behaviors and the outcome of interest, were not explored. Future research should delve deeper into the interplay between genetics, lifestyle factors, and T2DM development, with larger sample sizes and more diverse populations to validate findings and uncover potential genetic markers specific to this population.

The SIH study provides valuable insights into the landscape of metabolic risk factors, highlighting concerning prevalence rates of conditions like obesity, abdominal obesity, HTN, T2DM, DLP, and MetS. These findings emphasize significant healthcare challenges, particularly in the context of an aging population and a growing obesity epidemic. The study reveals a 6% prevalence of T2DM, a figure that in alignment with national and regional epidemiological data. Importantly, a substantial portion of the population remains unaware of their diabetic status, underscoring the need for proactive health promotion. Key contributors to onset of T2DM encompass advancing age, obesity, the presence of MetS, and the co-occurrence DLP, with microalbuminuria and macroalbuminuria as notable diabetes risk markers. While intended to explore T2DM's genetic basis, the analysis did not yield significant associations with the TCF7L2 gene, highlighting T2DM's multifaceted nature and its etiological underpinnings. Future research should incorporate larger, diverse cohorts to delve deeper into genetics, lifestyle, and T2DM, enhancing findings' validation and revealing population-specific genetic markers.

BMI Body mass index

CBC Complete blood count

CI Confidence interval

Cr Creatinine

DBP Diastolic blood pressure

DLP Dyslipidemia

DM Diabetes mellitus

DNA Deoxyribonucleic acid

EGAT Electricity Generating Authority of Thailand

eGFR Estimated glomerular filtration rate

FBG Fasting blood glucose

HbA1c Glycated hemoglobin

HDL-C High density lipoprotein cholesterol

HTN Hypertension

LDL-C Low density lipoprotein cholesterol

MAU/Cr Urine albumin-to-creatinine ratio

MetS Metabolic syndrome

NCDs Non-communicable diseases

non-DM Non-diabetes

OR Odds ratio

pre-DM Prediabetes

RRR Relative risk ratio

SBP Systolic blood pressure

SIH Siriraj Health

SNP Single nucleotide polymorphism

SOPs Standard operating procedures

T2DM Type-2 diabetes mellitus

TC Total cholesterol

TG Triglyceride

UMIC Upper middle-income country

WC Waist circumference

Acknowledgements

This research project is a collaboration between the Siriraj Medical Research Center and the Department of Preventive Medicine which is responsible for the staff health screening program. The authors gratefully acknowledge Prof. Dr. Ruengpung Sutthent for her guidance for initiation of this project. We thank the members of the SIH Study Group, SPHERE staff members, and research assistants. The voluntary participation of all participants is highly appreciated. We are also grateful to the management of Siriraj Medical Research Center for providing office space, a recruitment center, and a biobank.

Members of SIH study group: Winai Ratanasuwan, Keerati Charoencholvanich, Bhoom Suktitipat, Manop Pithukpakorn, Prapat Suriyaphol, Rungroj Krittayaphong, Prasert Auewarakul, Chalermchai Mitrpant, Boonrat Tassaneetritap, Mayuree Homsanit, and Naravat Poungvarin

Members of SPHERE group: Sureeporn Pumeiam, Bonggochpass Pinsawas, Pichanun Mongkolsucharitkul, Apinya Surawit, Tanyaporn Pongkunakorn, Sophida Suta, Thamonwan Manosan, Suphawan Ophakas, and Korapat Mayurasakorn

Members of Biobank: Somruedee Chatsiricharoenkul, Parichart Permpikul, Duangthip Apiratmontree, Sutee Udomchotphruet, and Pattranit Onsing

Author Contributions

KM and SP had the initial concept, supervised and designed this study. PM, BP, SP, AS, TP, SS, TM and SO performed recruitment and data analysis. PM drafted the manuscript. WR, MH, KC, YU, BS and KM commented on and revised the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by the Faculty of Medicine Siriraj Hospital, Mahidol University (R016034006). Additional funding support including infrastructure, staff and utilities, was provided by Faculty of Medicine Siriraj Hospital, Mahidol University. The funder had no role in the study design, data collection and analysis, decision to publish, and preparation of the manuscript.

Ethical approval

The SIH study was performed according to the principles established by the Declaration of Helsingki and was approved by the Ethics Committee of the Human Research Protection Unit, Faculty of Medicine Siriraj Hospital, Mahidol University board (SIRB 508/2559). Prior informed written consent was required for enrollment and specimen collection.

Competing interests

The authors declare that they have no conflicts of interest.

Martinez R, Lloyd-Sherlock P, Soliz P, Ebrahim S, Vega E, Ordunez P, et al. Trends in premature avertable mortality from non-communicable diseases for 195 countries and territories, 1990–2017: a population-based study. Lancet Glob Health. 2020;8(4):e511–e23.
Heller O, Somerville C, Suggs LS, Lachat S, Piper J, Aya Pastrana N, et al. The process of prioritization of non-communicable diseases in the global health policy arena. Health Policy Plan. 2019;34(5):370–83.
Savage A, McIver L, Schubert L. Review: the nexus of climate change, food and nutrition security and diet-related non-communicable diseases in Pacific Island countries and territories. Clim Dev. 2020;12(2):120–33.
Budreviciute A, Damiati S, Sabir DK, Onder K, Schuller-Goetzburg P, Plakys G, et al. Management and prevention strategies for non-communicable diseases (NCDs) and their risk factors. Front Public Health. 2020;8:574111.
World Health Organization. Noncommunicable diseases. 2018.https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases. Accessed 10 November 2023.
Bertram MY, Sweeny K, Lauer JA, Chisholm D, Sheehan P, Rasmussen B, et al. Investing in non-communicable diseases: an estimation of the return on investment for prevention and treatment services. Lancet. 2018;391(10134):2071–8.
Roth GA, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the global burden of disease study 2017. Lancet. 2018;392(10159):1736–88.
National Statistical Office. Report of the 2017 survey of the older persons of Thailand. Bangkok, Thailand. National Statistical Office; 2018.
Office of the Director, Population Division, United Nations. World population prospects 2019. 2019.https://population.un.org/wpp/. Accessed 10 November 2023.
Department of Economic and Social Affairs, Division P, United Nations. World urbanization prospects: the 2018 revision (ST/ESA/SER.A/420).United Nations, New York. 2019.https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf. Accessed 10 November 2023.
Aekplakorn W, Chariyalertsak S, Kessomboon P, Assanangkornchai S, Taneepanichskul S, Putwatana P. Prevalence of diabetes and relationship with socioeconomic status in the Thai population: national health examination survey, 2004–2014. J Diabetes Res. 2018;2018:1654530.
Aekplakorn W. Prevalence, treatment, and control of metabolic risk factors by BMI status in Thai adults: national health examination survey III. Asia Pac J Public Health. 2011;23(3):298–306.
Aekplakorn W, Inthawong R, Kessomboon P, Sangthong R, Chariyalertsak S, Putwatana P, et al. Prevalence and trends of obesity and association with socioeconomic status in Thai adults: national health examination surveys, 1991–2009. J Obes. 2014;2014:410259.
Aekplakorn W, Taneepanichskul S, Kessomboon P, Chongsuvivatwong V, Putwatana P, Sritara P et al. Prevalence of dyslipidemia and management in the Thai population, national health examination survey IV, 2009.J Lipids. 2014;2014:249584.
U.S. Department of Agriculture, U.S. Department of Health and Human Services. Dietary guidelines for American, 2020–2025. 2020.https://www.dietaryguidelines.gov/. Accessed 10 November 2023.
American Diabetes Association. Facilitating behavior change and well-being to improve health outcomes: standards of medical care in diabetes—2021. Diabetes Care. 2021;44(Supplement 1):53–S72.
Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HAW. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med. 2008;359(15):1577–89.
Sleigh AC, Seubsman S, Bain C, the Thai Cohort Study Team. Cohort profile: the Thai cohort of 87,134 Open University students. Int J Epidemiol. 2007;37(2):266–72.
Vathesatogkit P, Woodward M, Tanomsup S, Ratanachaiwong W, Vanavanan S, Yamwong S, et al. Cohort profile: the electricity generating authority of Thailand study. Int J Epidemiol. 2012;41(2):359–65.
Wunsch C, Dornelles TF, Girardi P, Arndt ME, Genro JP, Contini V. Lack of association between TCF7L2 gene variants and type 2 diabetes mellitus in a Brazilian sample of patients with the risk for cardiovascular disease. Endocr Regul. 2019;53(1):1–7.
Grant SFA, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J, et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet. 2006;38(3):320–3.
Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–81.
Unger T, Borghi C, Charchar F, Khan NA, Poulter NR, Prabhakaran D, et al. 2020 International society of hypertension global hypertension practice guidelines. Hypertension. 2020;75(6):1334–57.
American Diabetes Association. Classification and diagnosis of diabetes: standards of medical care in diabetes—2021. Diabetes Care. 2021;44(Supplement 1):34–9.
Ning M, Zhang Q, Yang M. Comparison of self-reported and biomedical data on hypertension and diabetes: findings from the China health and retirement longitudinal study (CHARLS). BMJ Open. 2016;6(1):e009836.
Nirdnoy N, Sranacharoenpong K, Mayurasakorn K, Surawit A, Pinsawas B, Mongkolsucharitkul P et al. Development of the Thai semiquantitative food frequency questionnaire (semi-FFQ) for people at risk for metabolic syndrome. J Public Health. 2021.
Aekplakorn W. Thai national health examination survey VI (2019–2020).2019.https://online.fliphtml5.com/bcbgj/znee/#p=194. Accessed 10 November 2023.
Jiamjarasrangsi W, Lohsoonthorn V, Lertmaharit S, Sangwatanaroj S. Incidence and predictors of abnormal fasting plasma glucose among the university hospital employees in Thailand. Diabetes Res Clin Pract. 2008;79(2):343–9.
Mai-um W, Hiransuthikul N, Srithara P, Tunlayadechanont S. Stroke incidences and related factors among employees working at the central office of the electricity generating authority of Thailand (EGAT): a prospective-descriptive study. J Health Res. 2014;28(1):13–21.
Maskarinec G, Kristal BS, Wilkens LR, Quintal G, Bogumil D, Setiawan VW, et al. Risk factors for type 2 diabetes in the multiethnic cohort. Can J Diabetes. 2023;S1499–2671(23):1–9.
Chan JCN, Malik V, Jia W, Kadowaki T, Yajnik CS, Yoon K-H, et al. Diabetes in Asia: epidemiology, risk factors, and pathophysiology. JAMA. 2009;301(20):2129–40.
Yadav D, Mishra M, Tiwari A, Bisen PS, Goswamy HM, Prasad GBKS. Prevalence of dyslipidemia and hypertension in Indian type 2 diabetic patients with metabolic syndrome and its clinical significance. Osong Public Health Res Perspect. 2014;5(3):169–75.
Alshaya AK, Alsayegh AK, Alshaya HK, Almutlaq BA, Alenazi NSG, Rasheedi HMAA, et al. The common complications and comorbidities among Saudi diabetic patients in northern Saudi Arabia. Open J Endocr Metab Dis. 2017;7:11.
Murad MA, Abdulmageed SS, Iftikhar R, Sagga BK. Assessment of the common risk factors associated with type 2 diabetes mellitus in Jeddah. Int J Endocrinol. 2014;2014:616145.
Saeed KMI, Asghar RJ, Sahak MN, Ansari J. Prevalence and risk factors associated with diabetes mellitus among Kabul citizens—Afghanistan, 2012. Int J Diabetes Dev Ctries. 2015;35(3):297–303.
Pani LN, Korenda L, Meigs JB, Driver C, Chamany S, Fox CS, et al. Effect of aging on A1C Levels in individuals without diabetes: evidence from the Framingham Offspring study and the national health and nutrition examination survey 2001–2004. Diabetes Care. 2008;31(10):1991–6.
RaviKumar P, Bhansali A, Walia R, Shanmugasundar G, Ravikiran M. Alterations in HbA1c with advancing age in subjects with normal glucose tolerance: Chandigarh Urban Diabetes Study (CUDS). Diabet Med. 2011;28(5):590–4.
Okwechime IO, Roberson S, Odoi A. Prevalence and predictors of pre-diabetes and diabetes among adults 18 years or older in Florida: a multinomial logistic modeling approach. PLoS ONE. 2016;10(12):e0145781.
Guewo-Fokeng M, Sobngwi E, Atogho-Tiedeu B, Donfack OS, Noubiap JJN, Ngwa EN, et al. Contribution of the TCF7L2 rs7903146 (C/T) gene polymorphism to the susceptibility to type 2 diabetes mellitus in Cameroon. J Diabetes Metab Disord. 2015;14(1):26.
Acharya S, Al-Elq A, Al-Nafaie A, Muzaheed M, Al-Ali A. Type 2 diabetes mellitus susceptibility gene TCF7L2 is strongly associated with hyperglycemia in the Saudi Arabia population of the eastern province of Saudi Arabia. Eur Rev Med Pharmacol Sci. 2015;19(16):3100–6.
Chang YC, Chang TJ, Jiang YD, Kuo SS, Lee KC, Chiu KC, et al. Association study of the genetic polymorphisms of the transcription factor 7-like 2 (TCF7L2) gene and type 2 diabetes in the Chinese population. Diabetes. 2007;56(10):2631–7.
Pourahmadi M, Erfanian S, Moradzadeh M, Jahromi AS. Non-association between rs7903146 and rs12255372 polymorphisms in transcription factor 7-like 2 gene and type 2 diabetes mellitus in Jahrom city, Iran. Diabetes Metab J. 2015;39(6):512–7.
Ding W, Xu L, Zhang L, Han Z, Jiang Q, Wang Z, et al. Meta-analysis of association between TCF7L2 polymorphism rs7903146 and type 2 diabetes mellitus. BMC Med Genet. 2018;19(1):38.
Lou L, Wang J, Wang J. Genetic associations between transcription factor 7 like 2 rs7903146 polymorphism and type 2 diabetes mellitus: a meta-analysis of 115,809 subjects. Diabetol Metab Syndr. 2019;11(1):56.

Table 1 Demographic data of the participants in the cohort

Characteristics (n (%) or mean±SD)		Baseline (SIH1 and SIH2)							Follow-up (SIH1)							P value^c
Characteristics (n (%) or mean±SD)		Total (N = 5,011)		Men (n = 1,157)		Women (n = 3,854)		P value^a	Total (N = 4,034)		Men (n = 925)		Women (n = 3,109)		P value^b	P value^c
Age, years		35.44 ± 8.24		35.60 ± 7.85		35.40 ± 8.35		0.491	41.00 ± 0.13		41.11 ± 0.26		40.96 ± 0.15		0.641	<0.001
	< 30	1,415	(28.2)	295	(25.5)	1,120	(29.1)	0.020	237	(5.9)	44	(4.8)	193	(6.2)	0.079
	30-39	2,073	(41.4)	515	(44.5)	1,558	(40.4)		1,646	(40.8)	374	(40.4)	1,272	(40.9)
	40-49	1,129	(22.5)	269	(23.2)	860	(22.3)		1,466	(36.3)	363	(39.2)	1,103	(35.5)
	≥ 50	394	(7.9)	78	(6.7)	316	(8.2)		685	(17.0)	144	(15.6)	541	(17.4)
Physical examinations
BMI, kg/m²																0.036
	< 18.5	351	(7.6)	23	(2.2)	328	(9.2)	<0.001	237	(6.4)	18	(2.2)	219	(7.6)	<0.001
	18.5-22.9	1,903	(41.2)	285	(26.8)	1,618	(45.5)		1,463	(39.6)	202	(25.0)	1,261	(43.7)
	23.0-24.9	728	(15.8)	241	(22.7)	487	(13.7)		627	(17.0)	178	(22.0)	449	(15.6)
	25.0-29.9	1,138	(24.6)	364	(34.2)	774	(21.8)		919	(24.9)	288	(35.6)	631	(21.9)
	> 29.9	501	(10.8)	150	(14.1)	351	(9.9)		446	(12.1)	123	(15.2)	323	(11.2)
WC, cm																<0.001
	men < 90, women < 80	3,267	(65.4)	729	(63.3)	2,538	(66.0)	0.489	2,346	(63.5)	505	(62.4)	1,841	(63.9)	0.454
	men ≥ 90, women ≥ 80	1,728	(34.6)	423	(36.7)	1,305	(34.0)		1,346	(36.5)	304	(37.6)	1,042	(36.1)
SBP, mmHg																<0.001
	< 120	4,015	(80.3)	719	(62.3)	3,296	(85.7)	<0.001	2,898	(78.5)	480	(59.3)	2,418	(83.9)	<0.001
	120-139	619	(12.4)	270	(23.4)	349	(9.1)		574	(15.5)	228	(28.2)	346	(12.0)
	140-159	316	(6.3)	147	(12.7)	169	(4.4)		199	(5.4)	95	(11.7)	104	(3.6)
	≥ 160	48	(1.0)	18	(1.6)	30	(0.8)		21	(0.6)	6	(0.7)	15	(0.5)
DBP, mmHg																<0.001
	< 80	3,918	(78.4)	791	(68.5)	3,127	(81.3)	<0.001	2,919	(79.1)	539	(66.6)	2,380	(82.6)	<0.001
	80-89	776	(15.5)	245	(21.2)	531	(13.8)		616	(16.7)	210	(26.0)	406	(14.1)
	90-99	236	(4.7)	89	(7.7)	147	(3.8)		134	(3.6)	53	(6.6)	81	(2.8)
	≥ 100	68	(1.4)	29	(2.5)	39	(1.0)		23	(0.6)	7	(0.9)	16	(0.6)
Laboratory measurements
FBG, mg/dL																<0.001
	< 100	4,486	(89.6)	959	(83.0)	3,527	(91.6)		2,979	(86.5)	587	(80.6)	2,392	(88.1)	<0.001
	100-124	431	(8.6)	169	(14.6)	262	(6.8)		368	(10.7)	115	(15.8)	253	(9.3)
	> 124	87	(1.7)	27	(2.3)	60	(1.6)		97	(2.8)	26	(3.6)	71	(2.6)
HbA1c, %																n/a
	< 5.7	3,688	(73.7)	718	(62.1)	2,970	(77.2)	<0.001	n/a		n/a		n/a		n/a
	5.7-6.4	1,157	(23.1)	388	(33.5)	769	(20.0)		n/a		n/a		n/a
	> 6.4	161	(3.2)	51	(4.4)	110	(2.9)		n/a		n/a		n/a
TC, mg/dL																0.053
	< 200	3,158	(63.1)	650	(56.3)	2,508	(65.2)	<0.001	2,106	(61.4)	403	(55.5)	1,703	(63.0)	0.001
	≥ 200	1,846	(36.9)	505	(43.7)	1,341	(34.8)		1,322	(38.6)	323	(44.5)	999	(37.0)
TG, mg/dL																0.058
	< 150	4,316	(86.3)	865	(74.9)	3,451	(89.7)	<0.001	2,903	(84.6)	525	(72.2)	2,378	(87.9)	<0.001
	≥ 150	688	(13.7)	290	(25.1)	398	(10.3)		528	(15.4)	202	(27.8)	326	(12.1)
HDL-C, mg/dL																0.044
	men < 40, women < 50	1,251	(25.0)	554	(48.0)	697	(18.1)	<0.001	835	(24.3)	346	(47.5)	489	(18.1)	<0.001
	men ≥ 40, women ≥ 50	3,753	(75.0)	601	(52.0)	3,152	(81.9)		2,595	(75.7)	382	(52.5)	2,213	(81.9)
LDL-C, mg/dL																<0.001
	< 130	3,833	(76.6)	744	(64.5)	3,089	(80.3)	<0.001	2,597	(75.9)	476	(65.7)	2,121	(78.6)	<0.001
	130-159	874	(17.5)	294	(25.5)	580	(15.1)		604	(17.7)	178	(24.6)	426	(15.8)
	> 159	295	(5.9)	115	(10.0)	180	(4.7)		220	(6.4)	70	(9.7)	150	(5.6)
MetS																<0.001
	no	3,547	(70.8)	613	(53.0)	2934	(76.1)	<0.001	2,339	(68.2)	419	(52.5)	1,920	(72.9)	<0.001
	yes	1,464	(29.2)	544	(47.0)	920	(23.9)		1,091	(31.8)	379	(47.5)	712	(27.1)
eGFR, mL/min/1.73m²		102.34 ± 14.65		95.33 ± 14.84		104.64 ± 13.84		<0.001	100.57 ± 14.78		93.43 ± 15.08		102.67 ± 14.02		<0.001	<0.001
Cr, mg/dL		24.54 ± 66.10		25.84 ± 70.31		24.12 ± 64.67		0.451	18.28 ± 55.23		20.27 ± 61.61		17.53 ± 52.66		0.513	<0.001
MAU/Cr ratio, mg/g		14.80 ± 96.32		13.65 ± 121.25		15.17 ± 86.91		0.734	n/a		n/a		n/a		n/a	n/a
Hemoglobin, g/dL		13.15 ± 1.47		14.81 ± 1.15		12.66 ± 1.15		<0.001	13.02 ± 1.43		14.70 ± 1.10		12.57 ± 1.15		<0.001	0.054
Hematocrit, %		40.37 ± 4.00		45.05 ± 3.00		38.99 ± 3.13		<0.001	39.75 ± 3.88		44.37 ± 2.92		38.53 ± 3.09		<0.001	0.408
Self-report questionnaires
Underlying diseases
DM, yes		209	(4.6)	54	(4.6)	155	(4.0)	0.278	n/a		n/a		n/a		n/a	n/a
HTN, yes		287	(6.4)	96	(8.3)	191	(4.9)	<0.001	n/a		n/a		n/a		n/a	n/a
DLP, yes		561	(12.4)	180	(15.5)	381	(9.8)	<0.001	n/a		n/a		n/a		n/a	n/a
Ischemic heart, yes		21	(0.5)	5	(0.4)	16	(0.4)	0.173	n/a		n/a		n/a		n/a	n/a
Stroke, yes		11	(0.2)	3	(0.3)	8	(0.2)	0.005	n/a		n/a		n/a		n/a	n/a
Gout, yes		69	(1.5)	48	(4.2)	21	(0.5)	<0.001	n/a		n/a		n/a		n/a	n/a
Family history
Heart failure, yes		293	(6.5)	72	(6.2)	221	(5.7)	0.439	n/a		n/a		n/a		n/a	n/a
Cancer, yes		991	(21.9)	187	(16.2)	804	(20.8)	<0.001	n/a		n/a		n/a		n/a	n/a
Stroke, yes		333	(7.3)	67	(5.7)	266	(6.9)	0.119	n/a		n/a		n/a		n/a	n/a
Lifestyle
Exercise, yes		2,540	(50.6)	740	(63.9)	1,800	(46.7)	0.001	n/a		n/a		n/a		n/a	n/a
Smoking, yes		306	(6.1)	225	(19.4)	81	(2.1)	0.001	n/a		n/a		n/a		n/a	n/a
Alcohol consumption, yes		3,168	(63.2)	953	(82.4)	2,215	(57.4)	0.013	n/a		n/a		n/a		n/a	n/a
Continuous variables were presented as mean±standard deviation. Categorical variables were presented as number (percentages). ^a,b P value is the differences between men and women based on independent t-test and chi-square for continuous and categorical variables, respectively. ^c P value is the differences between baseline and follow-up based on Wilcoxon signed rank test and McNemar chi-square test for continuous and categorical variables, respectively. n/a, not available; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting blood glucose; HbA1c, glycated hemoglobin; TC, total cholesterol; TG, triglycerides; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; MetS, metabolic syndrome; eGFR, estimated glomerular filtration rate; Cr, creatinine; MAU/Cr, urine albumin-to-creatinine ratio; DM, diabetes mellitus; HTN, hypertension; DLP, dyslipidemia

Table 2 Characteristics of the participants based on self-report diabetic status

Characteristics					Diabetic status
			All		Non-DM^a		Unaware Pre-DM^b		DM^c		P value^d
			(N = 4,821)		(n = 3,487)		(n = 1,055)		(n = 279)		P value^d
			%	(95% CI)	%	(95% CI)	%	(95% CI)	%	(95% CI)	A	B	C
Sex											<0.001	<0.001	0.159
	men		22.2	(20.1-24.5)	18.6	(16.4-27.2)	32.4	(25.9-64.0)	27.9	(15.1-42.5)
	women		77.8	(59.9-85.0)	81.4	(62.7-99.8)	67.6	(52.1-91.2)	72.1	(55.5-97.2)
Age, years											<0.001	<0.001	0.015
		< 30	27.9	(26.6-29.3)	33.2	(31.6-34.9)	14.6	(12.5-17.0)	12.1	(8.5-16.7)
		30-39	41.9	(40.4-43.4)	43.8	(42.1-45.5)	38.6	(35.6-41.7)	30.9	(25.4-36.9)
		≥ 40	30.2	(28.8-31.6)	22.9	(21.5-24.4)	46.7	(43.5-49.8)	57.0	(50.8-63.0)
BMI, kg/m2											<0.001	<0.001	<0.001
		normal (< 23.0)	49.6	(48.1-51.1)	58.9	(57.2-60.7)	27.0	(26.6-29.3)	19.8	(15.3-25.1)
		overweight (23.0-24.9)	15.9	(14.8-16.9)	15.9	(15.3-25.1)	16.4	(14.6-17.2)	14.1	(10.2-19.0)
		obesity (≥ 25.0)	34.5	(33.1-36.8)	25.2	(23.7-26.8)	56.7	(53.5-59.8)	66.1	(60.0-71.7)
WC, cm											<0.001	<0.001	<0.001
		normal (men < 90, women < 80)	66.1	(64.6-67.5)	75.1	(73.6-76.6)	45.8	(42.6-48.9)	30.6	(25.2-36.6)
		elevated (men ≥ 90, women ≥ 80)	33.9	(32.4-35.3)	24.9	(23.4-26.4)	54.2	(51.0-57.3)	69.4	(63.3-74.7)
BP, mmHg											<0.001	<0.001	0.014
		normal (< 120/80)	61.7	(60.2-63.1)	68.2	(66.5-69.8)	45.7	(42.6-48.9)	41.9	(35.9-48.1)
		elevated (≥ 120/80)	38.3	(36.8-39.7)	31.8	(30.1-33.4)	54.2	(51.0-57.3)	58.1	(51.8-64.0)
TC, mg/dL											<0.001	0.018	0.074
		normal (< 200)	63.8	(62.4-65.2)	67.4	(65.6-68.9)	53.3	(50.0-56.4)	59.8	(55.2-61.4)
		elevated (≥ 200)	36.2	(34.7-37.6)	32.6	(31.0-34.3)	46.7	(43.5-49.9)	40.2	(39.5-44.9)
TG, mg/dL											<0.001	<0.001	<0.001
		normal (< 150)	86.6	(85.5-87.5)	91.1	(90.0-92.0)	77.3	(74.5-79.8)	65.5	(64.5-69.8)
		elevated (≥ 150)	13.4	(12.4-14.4)	8.9	(7.9-9.9)	22.7	(20.1-25.4)	34.5	(30.1-36.4)
HDL-C, mg/dL											<0.001	<0.001	0.344
		normal (men ≥ 40, women ≥ 50)	24.4	(23.1-25.6)	18.5	(17.1-19.9)	38.8	(35.7-41.9)	42.2	(35.7-41.9)
		low (men < 40, women < 50)	75.7	(74.3-76.9)	81.5	(80.0-82.8)	61.2	(58.1-64.2)	57.8	(56.1-64.2)
LDL-C, mg/dL											<0.001	<0.001	<0.001
		normal (< 130)	77.1	(75.8-78.3)	80.9	(79.5-82.2)	65.2	(62.1-68.1)	75.0	(62.1-78.1)
		elevated (≥ 130)	22.9	(21.6-24.1)	19.1	(17.7-20.4)	34.8	(31.8-37.8)	25.0	(21.8-37.8)
MAU/Cr ratio, mg/g											0.013	<0.001	<0.001
		normal (< 30)	95.2	(94.4-95.8)	96.6	(95.8-97.1)	94.2	(92.4-95.5)	81.9	(80.4-95.5)
		microalbuminuria (30-299)	3.9	(3.3-4.6)	2.8	(2.2-3.5)	5.4	(4.0-7.0)	12.2	(10.0-17.0)
		macroalbuminuria (> 299)	0.9	(0.6-1.2)	0.6	(0.3-0.9)	0.5	(0.1-1.1)	5.9	(1.1-6.1)
MetS											<0.001	<0.001	<0.001
		no	76.7	(74.3-79.0)	53.4	(50.4-55.9)	61.3	(59.7-63.7)	85.3	(80.4-89.2)
		yes	28.3	(26.9-29.6)	46.6	(43.7-49.4)	38.7	(35.9-41.4)	14.7	(12.7-16.8)
Underlying diseases
HTN, yes			5.2	(4.1-7.1)	2.5	(1.9-3.4)	7.2	(5.5-9.6)	31.3	(24.1-42.2)	<0.001	<0.001	<0.001
DLP, yes			10.8	(8.3-14.5)	7.0	(5.4-9.4)	14.8	(11.3-19.9)	42.6	(32.9-57.5)	<0.001	<0.001	<0.001
Lifestyle
Exercise, yes			50.8	(39.1-68.5)	50.1	(38.6-67.6)	54.6	(42.0-73.6)	44.5	(34.3-60.0)	0.038	0.335	0.033
Smoking, yes			5.5	(4.7-9.8)	3.1	(2.3-4.1)	4.9	(3.8-6.6)	4.9	(3.7-6.6)	0.002	0.001	0.099
Alcohol consumption, yes			62.9	(50.5-78.9)	61.4	(51.9-65.6)	65.1	(54.9-68.8)	62.8	(59.8-74.3)	0.572	0.023	0.035

^aNon-DM was defined as a self-reported medical history of no diabetes mellitus and HbA1clevel < 5.7%

^bUnaware Pre-DM was defined as a self-reported medical history of no diabetes mellitus and HbA1c level 5.7-6.4%

^cDM were defined as a self-reported medical history of no diabetes mellitus and HbA1c > 6.4%, and/or self-reported medical history of diabetes mellitus.

^dP value compares diabetes status by using the chi-square test. (A) P value compared non-DM and unaware pre-DM individuals, (B) P value compared non-DM and DM individuals,

(C) P value compared unaware pre-DM and DM individuals. Data are presented as 95 confidence interval (95%CI) and percentage.

BMI, body mass index; WC, waist circumference; BP, blood pressure; TC, total cholesterol; TG, triglycerides; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; MetS, metabolic syndrome; MAU/Cr, urine albumin-to-creatinine ratio; DM, diabetes mellitus; HTN, hypertension; DLP, dyslipidemia

Table 3 Multivariate multinomial logistic regression analyses based on the diabetic status

Characteristic		Non-DM vs.						Unaware Pre-DM vs.
		Unaware Pre-DM			T2DM			T2DM
		RRR	(95% CI)	P value*	RRR	(95% CI)	P value**	RRR	(95% CI)	P value***
Sex
	men	1.00	(Reference)		1.00	(Reference)		1.00	(Reference)
	women	1.54	(1.25-1.89)	<0.001	1.34	(0.94-1.91)	0.106	0.87	(0.61-1.24)	0.442
Age, years
	< 30	1.00	(Reference)		1.00	(Reference)		1.00	(Reference)
	30-39	1.77	(1.40-2.25)	<0.001	1.63	(1.01-2.60)	0.044	0.92	(0.56-1.51)	0.729
	≥ 40	3.82	(3.00-4.86)	<0.001	5.30	(3.36-8.36)	<0.001	1.39	(0.86-2.24)	0.180
BMI, kg/m²
	normal (< 23.0)	1.00	(Reference)		1.00	(Reference)		1.00	(Reference)
	overweight (23.0-24.9)	1.75	(1.38-2.22)	<0.001	1.27	(0.82-1.96	0.283	0.72	(0.46-1.14)	0.159
	obese (≥ 25.0)	2.81	(2.01-3.92)	<0.001	2.90	(1.72-4.87)	<0.001	1.03	(0.61-1.75)	0.907
WC, cm
	normal (men < 90, women < 80)	1.00	(Reference)		1.00	(Reference)		1.00	(Reference)
	elevated (men ≥ 90, women ≥ 80)	1.35	(1.04-1.75)	0.022	1.53	(0.96-2.43)	0.073	1.13	(0.70-1.83)	0.617
BP, mmHg
	normal (< 120/80)	1.00	(Reference)		1.00	(Reference)		1.00	(Reference)
	elevated (≥ 120/80)	1.18	(0.98-1.43)	0.079	0.89	(0.64-1.24)	0.479	0.75	(0.53-1.05)	0.098
MetS
	no	1.00	(Reference)		1.00	(Reference)		1.00	(Reference)
	yes	1.55	(1.16-2.08)	0.003	5.45	(3.34-8.90)	<0.001	3.51	(2.12-5.80)	<0.001
TC, mg/dL
	normal (< 200)	1.00	(Reference)		1.00	(Reference)		1.00	(Reference)
	elevated (≥ 200)	1.40	(1.18-1.66)	<0.001	0.90	(0.66-1.22)	0.505	0.64	(0.47-0.88)	0.006
TG, mg/dL
	normal (< 150)	1.00	(Reference)		1.00	(Reference)		1.00	(Reference)
	elevated (≥ 150)	1.24	(0.96-1.60)	0.072	1.81	(1.25-2.61)	0.002	1.46	(1.01-2.11)	0.046
HDL-C, mg/dL
	normal (men ≥ 40, women ≥ 50)	1.00	(Reference)		1.00	(Reference)		1.00	(Reference)
	low (men < 40, women < 50)	0.84	(0.66-1.06)	0.144	1.61	(1.12-2.33)	0.011	1.93	(1.33-2.79)	<0.001
MAU/Cr ratio, mg/g
	normal (A1; < 30)	1.00	(Reference)		1.00	(Reference)		1.00	(Reference)
	microalbuminuria (A2; 30-299)	1.26	(0.83-1.91)	0.278	2.78	(1.66-4.64)	<0.001	2.21	(1.33-3.66)	0.002
	macroalbuminuria (A3; > 299)	0.37	(0.12-1.17)	0.089	5.19	(2.17-12.42)	<0.001	14.10	(4.41-25.08)	<0.001
* P value was significant predictors of pre-DM compared to non-DM and P value was T2DM compared to non-DM and * P value was T2DM compared to pre-DM based on the results of the multinomial logistic model for participants; RRR was relative risk ratio with 95% confidence intervals (CI) BMI, body mass index; WC, waist circumference; BP, blood pressure; MetS, metabolic syndrome; TC, total cholesterol; TG, triglycerides; HDL-C, high density lipoprotein cholesterol; MAU/Cr, urine albumin-to-creatinine ratio; DM, diabetes mellitus

Table 4 Genotypic variants of T2DM-related SNP association with non-DM and pre-DM
SNPs	Non-DM		Pre-DM		T2DM		Non-DM		Pre-DM
SNPs	(n=2,671)		(n=844)		(n=217)		OR (95%CI)	P value*	OR (95%CI)	P value*
rs7903146
CC	2439	(91.31)	765	(90.64)	195	(89.86)	1		1
CT	235	(8.80)	75	(8.89)	21	(9.68)	1.03 (0.63-1.69)	0.708	1.07 (0.64-1.79)	0.965
TT	8	(0.30)	4	(0.47)	1	(0.46)	2.74 (0.32-23.3)	0.411	1.05 (0.12-9.51)	0.972
CT+TT	243	(9.10)	79	(9.36)	22	(10.14)	1.07 (0.66-1.72)	0.615	1.07 (0.65-1.77)	0.791
P value of HWE test^a	0.381		0.136		0.459
rs12255372
GG	2512	(94.05)	788	(93.36)	198	(91.24)	1		1
GT	167	(6.25)	53	(6.28)	19	(8.76)	1.40 (0.81-2.43)	0.250	1.30 (0.77-2.19)	0.582
TT	3	(0.11)	3	(0.36)	0	(0.00)	n/a	n/a	n/a	n/a
GT+TT	170	(6.36)	56	(6.64)	19	(8.76)	1.33 (0.77-2.30)	0.317	1.29 (0.77-2.17)	0.342
P value of HWE test^a	0.755		0.763		1.000
rs7917983
CC	990	(37.06)	325	(38.51)	82	(37.79)	1		1
CT	1244	(46.57)	390	(46.21)	103	(47.47)	1.05 (0.77-1.44)	0.572	1.03 (0.74-1.42)	0.958
TT	447	(16.74)	128	(15.17)	32	(14.75)	0.84 (0.54-1.30)	0.307	0.96 (0.61-1.52)	0.804
CT+TT	1691	(63.31)	518	(61.37)	135	(62.21)	0.99 (0.74-1.34)	0.970	1.01 (0.74-1.38)	0.949
P value of HWE test^a	0.098		0.559		1.000
rs4506565
AA	2428	(90.90)	763	(90.40)	195	(89.86)	1		1
AT	235	(8.80)	76	(9.00)	20	(9.22)	0.97 (0.59-1.60)	0.713	1.01 (0.60-1.69)	0.999
TT	8	(0.30)	4	(0.47)	1	(0.46)	2.71 (0.32-23.07)	0.411	1.04 (0.11-9.47)	0.971
AT+TT	243	(9.10)	80	(9.48)	21	(9.68)	1.01 (0.62-1.64)	0.981	1.01 (0.61-1.68)	0.977
P value of HWE test^a	0.381		0.146		0.431
rs4132670
CC	2493	(93.34)	785	(93.01)	199	(91.71)	1		1
CT	179	(6.70)	55	(6.52)	18	(8.29)	1.17 (0.69-1.98)	0.636	1.25 (0.71-2.21)	0.343
TT	6	(0.22)	3	(0.36)	0	(0.00)	n/a	n/a	n/a	n/a
CT+TT	185	(6.93)	58	(6.87)	18	(8.29)	1.14 (0.67-1.93)	0.629	1.18 (0.67-2.08)	0.56
P value of HWE test^a	0.151		0.089		1.000
rs12243326
CC	2512	(94.05)	790	(93.60)	199	(91.71)	1		1
CT	167	(6.25)	51	(6.04)	18	(8.29)	1.25 (0.73-2.12)	0.678	1.38 (0.78-2.42)	0.277
TT	3	(0.11)	3	(0.36)	0	(0.00)	n/a	n/a	n/a	n/a
CT+TT	170	(6.36)	54	(6.40)	18	(8.29)	1.24 (0.73-2.10)	0.438	1.30 (0.74-2.28)	0.366
P value of HWE test^a	0.756		0.064		1.000
rs290487
CC	783	(29.31)	225	(26.66)	57	(26.27)	1		1
CT	1328	(49.72)	419	(49.64)	112	(51.61)	1.18 (0.83-1.66)	0.699	1.05 (0.74-1.51)	0.878
TT	569	(21.30)	197	(23.34)	48	(22.12)	1.18 (0.78-1.79)	0.715	0.96 (0.62-1.47)	0.673
CT+TT	1897	(71.02)	616	(72.99)	160	(73.73)	1.18 (0.85-1.63)	0.319	1.02 (0.73-1.44)	0.896
P value of HWE test^a	0.907		0.945		0.684
* Adjusted: age and sex ^a HWE, Hardy-Weinberg equilibrium n/a, not available; DM, diabetes mellitus

No competing interests reported.

Supplementarydatasubmitted.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Metabolic and genetic risk factors associated with pre-diabetes and type 2 diabetes in Thai healthcare employees: a long-term study from the Siriraj Health (SIH) Cohort Study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Methods

Study population

Data collection

Physical examinations

Laboratory measurements

DNA extraction and genotyping

Self-report underlying diseases and family histories

Behavioral risk factors

Statistical analysis

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1