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. 2024 Jun 28;19(6):e0303085. doi: 10.1371/journal.pone.0303085

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

Pichanun Mongkolsucharitkul 1,#, Apinya Surawit 1,#, Thamonwan Manosan 1, Suphawan Ophakas 1, Sophida Suta 1, Bonggochpass Pinsawas 1, Tanyaporn Pongkunakorn 1, Sureeporn Pumeiam 1, Winai Ratanasuwan 2, Mayuree Homsanit 2, Keerati Charoencholvanich 3, Yuthana Udomphorn 4, Bhoom Suktitipat 5, Korapat Mayurasakorn 1,*
Editor: Chikezie Hart Onwukwe6
PMCID: PMC11213315  PMID: 38941315

Abstract

Background

The study of non-communicable diseases (NCDs) in a developing country like Thailand has rarely been conducted in long-term cohorts, especially among the 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

The SIH study was designed as a longitudinal cohort and conducted at Siriraj hospital, Thailand. A total of 5,011 participants (77% women) were recruited and follow-up. Physical examinations, blood biochemical analyses, family history assessments, behavior evaluations, 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 that men were more likely to have a prevalence of T2DM and dyslipidemia (DLP) compared to women. Aging was significantly associated with pre-diabetes and T2DM (P<0.001). Additionally, aging, metabolic syndrome, and elevated triglycerides were associated with the development of pre-diabetes and T2DM. The minor T allele of the rs7903146(C/T) and rs4506565 (A/T) were associated with a high risk of developing pre-diabetes with odds ratios of 2.74 (95% confidence interval [CI]: 0.32–23.3) and 2.71 (95% CI: 0.32–23.07), respectively; however, these associations were statistically insignificant (P>0.05).

Conclusion

The findings of the SIH study 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.

Introduction

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 (DLP), 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 Southeastern 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 Nations 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 showed that 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]. In Asian populations, including Japanese [22], Thai [23], and Chinese [24], variants of the TCF7L2 gene, such as rs7903146, rs11196205, and rs12255372, have been identified as significant genetic risk factors for T2DM. These findings highlight the genetic heterogeneity of T2DM across different ethnic groups and underscore the importance of understanding population-specific genetic determinants of the disease. 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.

Methods

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.). As of November 2023, there were 20,967 individuals working in this university hospital and approximately 80% undergo annual health check-up. The inclusion criteria of the study were the Siriraj Hospital personnel who attended an annual health screening surveillance program. The exclusion criteria of the study were individuals who withheld treatment information, unable to consistently follow-up, unable to participate in this cohort in the next 2 years, or presence of contraindications for blood sampling, such as blood clotting. 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, the second phase of the Siriraj Health (SIH2) study included participants aged over 18 years with no upper age limit. However, due to the COVID-19 pandemic from 2020 to 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.

Fig 1. The Siriraj Health (SIH) study.

Fig 1

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(A) Geographic distribution of Siriraj Health study (marked area) in the center of Bangkok, Thailand; (B) Flow diagram of participants enrolled in the Siriraj Health cohort study; FU, Follow-up; SIH, Siriraj Health. Reprinted under a CC BY license, with permission from Ms. Apinya Surawit, original copyright [2024].

Data collection

During the annual health check-up, Siriraj’s personnel were provided with initial information about the SIH cohort study. Written informed consent was obtained from all participants before enrollment and specimen collection. The SIH study was conducted in accordance with 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 (COA no. Si 647/2016). 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) [25]. 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 (BP). 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 a 12-hour fast, and 30 ml of urine was 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), and estimated glomerular filtration rate (eGFR) assessment. Urine samples were analyzed for spot urine albumin-to-creatinine ratio (MAU/Cr). Blood samples were centrifuged at 3,000 rounds per minute for 10 minutes at 4°C, and plasma 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 S1 Table. We used the criteria of metabolic syndrome (MetS) in adults by the IDF definition with South Asian ethnic group [26]. According to the IDF definition, MetS is present if three or more of the following five criteria are met: WC ≥ 90 cm (men) or ≥ 80 cm (women), BP ≥ 130/85 mmHg, TG level ≥ 150 mg/dL, HDL-C level < 40 mg/dL (men) or < 50 mg/dL (women) and FBG ≥ 100 mg/dL.

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 of 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 (S1 File).

Self-report underlying diseases and family histories

Participants completed questionnaires about underlying diseases (T2DM, HTN, 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 (120/80 mmHg) or the current use of antihypertensive medication [27]. The diagnostic HbA1c criteria for non-diabetes (non-DM) was < 5.7%, pre-diabetes (pre-DM) 5.7–6.4%, and diabetes mellitus (DM) > 6.4%, or the current use of hypoglycemic medication [28]. Individuals who reported that they did not have DM but exhibited abnormal biochemical measurements within range of pre-DM were classified as unaware pre-DM [29] (S1 Fig).

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 who consumed alcohol at least once a month in the past year.

Statistical analysis

All continuous variables in the demographic data were expressed as the mean ± standard deviation (SD), whereas binary variables were expressed as numbers and percentages. Self-report data were expressed as percentage with 95% CI. All statistical analyses were conducted using STATA version 14 (STATA Corp., Texas). The unadjusted cross-sectional associations of each risk factor at baseline with non-DM, pre-DM, and T2DM were examined using Chi-squared (χ2) tests. Adjusted odds ratios (OR) and 95% CI for cross-sectional associations at baseline were calculated using multivariable binary logistic regression models. Separate models were used for pre-DM versus non-DM and T2DM versus non-DM. To assess the longitudinal association between risk factors and progression to pre-DM or T2DM at baseline, the sample was restricted to individuals without T2DM at baseline. Multinomial logistic regression models were then used to calculate adjusted relative risk ratios (RRR) and 95% CI for T2DM-related outcomes (non-DM, pre-DM, T2DM) at follow-up. The average predicted probabilities of being classified as having normal blood glucose, pre-DM, or T2DM, given specific characteristics (each risk factor of interest for longitudinal analyses), were obtained using the Stata margins command. In this study, we focused on seven SNPs of 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). 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. All SNPs were analyzed using a codominant, dominant, and recessive model to examine the relationships between the two groups (T2DM and non-DM) using the “SNPassoc” package of the R version 3.3.1 (R Foundation, Vienna, Austria). All statistical tests performed in this study were two-tailed, and a significance level of P<0.05 was considered statistically significant.

Results

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 a significantly higher prevalence of obesity, abdominal obesity, and HTN compared to women. Men were also more likely to have an incidence of T2DM (elevated FBG and/or HbA1c) and DLP (high TC, TG, and LDL-C, but low HDL-C) compared to women (P<0.001).

Table 1. Demographic data of the participants in the cohort.

Characteristics
(n (%) or mean±SD)		 Baseline (SIH1 and SIH2) Follow-up (SIH1) P-valuec
Total
(N = 5,011)		 Men
(n = 1,157)		 Women
(n = 3,854)		 P-valuea Total
(N = 4,034)		 Men
(n = 925)		 Women
(n = 3,109)		 P-valueb
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
100–125 431 (8.6) 169 (14.6) 262 (6.8) 368 (10.7) 115 (15.8) 253 (9.3)
> 125 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
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Continuous variables were presented as mean±standard deviation. Categorical variables were presented as number (percentages).

a,bP-value is the differences between men and women based on independent t-test and chi-square for continuous and categorical variables, respectively.

cP-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

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. We primarily collected follow-up data through routine electronic health check-ups, limiting access to key information like HbA1c levels, underlying diseases, family history, and lifestyle factors. Due to the nature of these check-ups, we couldn’t gather extra data beyond what’s routinely collected in healthcare. While participants were physically present, data collection was restricted by standard procedures during these visits.

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%) (S1 Fig). 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 SBP, 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.

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

Characteristics All Diabetes status P-value d
Non-DMa Unaware Pre-DMb T2DMc
(N = 4,821) (n = 3,487) (n = 1,055) (n = 279)
% (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)
SBP, mmHg <0.001 <0.001 0.335
normal (< 120) 60.6 (59.6–66.2) 66.9 (62.8–70.3) 44.6 (41.2–49.9) 41.4 (39.6–52.5)
elevated (≥ 120) 39.4 (30.2–45.1) 33.1 (30.6–38.2) 55.4 (51.8–58.4) 58.6 (49.6–64.3)
DBP, mmHg <0.001 <0.001 0.049
normal (< 80) 78.7 (69.4–82.9) 83.3 (79.5–85.6) 68.1 (66.7–71.3) 61.9 (59.6–62.1)
elevated (≥ 80) 21.3 (18.9–22.4) 16.7 (14.8–17.3) 31.9 (29.5–33.1) 38.1 (35.4–42.5)
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 (35.7–44.9)
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
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aNon-DM was defined as a self-reported medical history of no diabetes mellitus and HbA1c level < 5.7%

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

cT2DM 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 T2DM individuals

(C) P-value compared unaware pre-DM and T2DM individuals. Data are presented as 95 confidence interval (95%CI) and percentage.

BMI, body mass index; WC, waist circumference; BP, blood pressure; SBP, systolic blood pressure; DBP, diastolic 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

A cross-sectional analysis comparing pre-DM and T2DM to those without T2DM at baseline was conducted using a multivariate logistic regression model (Table 3). Sex, age, BMI, HTN, DLP, gout, alcohol consumption, exercise, and smoking were adjusted. Women displayed an almost two-fold increased odds (OR = 1.71, 95% CI: 1.37–2.13) of having pre-DM compared to men. Pre-DM was significantly associated with sex, older age, overweight/obesity, elevated WC, having MetS, elevated LDL-C concentrations, gout, and being non-exercise. Participants aged 40 years or older displayed three-fold increased odds of having pre-DM (OR = 3.60, 95% CI: 2.86–4.55) and T2DM (OR = 3.28, 95% CI: 2.09–5.14). Participants with pre-DM displayed the metabolic profile defined by overweight (OR = 1.45, 95% CI: 1.13–1.85), obesity (OR = 2.15, 95% CI: 1.66–2.78), having MetS (OR = 1.50, 95% CI: 1.10–1.98), and elevated LDL-C concentrations (OR = 1.30, 95% CI: 1.01–1.67). Normal HDL-C concentrations was protective against having pre-DM (OR = 0.78, 95% CI: 0.61–0.99). Associations with older age, having MetS, HTN, and DLP were noticeably significant (P>0.001) among participants diagnosed with T2DM. Obese participants displayed a 75% increased odds (OR = 1.75, 95% CI: 1.03–3.00) of having T2DM. Participants meeting the criteria for MetS according to the IDF definition displayed five-fold increased odds (OR = 5.11, 95% CI: 3.09–8.44) of having T2DM. Participants with elevated TG displayed a 64% increased odds (OR = 1.64, 95% CI: 1.11–2.42) of having T2DM. With regards to lifestyle behaviors, being a smoker was significantly associated with T2DM (OR = 1.69, 95% CI: 1.03–2.77) while lack of exercise was associated with pre-DM (OR = 1.21, 95% CI: 1.03–1.43). The risk factors showing significant association among participants diagnosed with pre-DM or T2DM are displayed in Fig 2.

Table 3. Cross-sectional analysis comparing pre-diabetesa (n = 1,055) and type 2 diabetes mellitusb (n = 279) to those without diabetesc (n = 3,487) at baseline (SIH1 and SIH2).

Factors Risk factors for Pre-DMa Risk factors for T2DMb
OR (95% CI) P-valued OR (95% CI) P-valued
Sex
  men 1.00 1.00
  women 1.71 (1.37–2.13) <0.001 1.01 (0.66–1.54) 0.954
Age, years
  < 30 1.00 1.00
  30–39 1.68 (1.34–2.10) <0.001 1.30 (0.82–2.06) 0.260
  ≥ 40 3.60 (2.86–4.55) <0.001 3.28 (2.09–5.14) <0.001
BMI, kg/m2
  normal (< 23.0) 1.00 1.00
  overweight (23.0–24.9) 1.45 (1.13–1.85) 0.003 1.23 (0.73–2.08) 0.442
  obese (≥ 25.0) 2.15 (1.66–2.78) <0.001 1.75 (1.03–3.00) 0.040
WC, cm
  normal (men < 90, women < 80) 1.00 1.00
  elevated (men ≥ 90, women ≥ 80) 1.38 (1.07–1.78) 0.012 1.34 (0.82–2.21) 0.245
BP, mmHg
  normal (< 120/80) 1.00 1.00
  elevated (≥ 120/80) 1.18 (0.98–1.42) 0.084 0.79 (0.55–1.12) 0.183
MetS
  no 1.00 1.00
  yes 1.50 (1.1–1.98) 0.005 5.11 (3.09–8.44) <0.001
TC, mg/dL
  normal (< 200) 1.00 1.00
  elevated (≥ 200) 1.19 (0.95–1.50) 0.135 1.34 (0.89–2.00) 0.163
TG, mg/dL
  normal (< 150) 1.00 1.00
  elevated (≥ 150) 1.27 (0.99–1.64) 0.059 1.64 (1.11–2.42) 0.013
HDL-C, mg/dL
  low (men < 40, women < 50) 1.00 1.00
  normal (men ≥ 40, women ≥ 50) 0.78 (0.61–0.99) 0.041 1.32 (0.90–1.94) 0.149
LDL-C, mg/dL
  normal (< 130) 1.00 1.00
  elevated (≥ 130) 1.30 (1.01–1.67) 0.039 0.69 (0.44–1.10) 0.117
HTN
  no 1.00 1.00
  yes 1.03 (0.81–1.31) 0.808 2.34 (1.75–3.14) <0.001
DLP
  no 1.00 1.00
  yes 0.95 (0.81–1.11) 0.523 1.72 (1.36–2.18) <0.001
Gout
  no 1.00 1.00
  yes 1.27 (1.01–1.59) 0.038 0.91 (0.63 to 1.31) 0.607
Exercise
  no 1.00 1.00
  yes 1.21 (1.03–1.43) 0.022 0.93 (0.69 to 1.26) 0.655
Smoking
  no 1.00 1.00
  yes 0.73 (0.54–0.97) 0.132 1.69 (1.03–2.77) 0.039
Alcohol consumption
  no 1.00 1.00
  yes 1.08 (0.91–1.28) 0.362 0.76 (0.55–1.05) 0.098
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aPre-DM was defined as a HbA1c level between 5.7 and 6.4%.

bT2DM was defined as a self-reported medical history of diabetes, current use of hypoglycemic medication, and/or a HbA1c level > 6.4%.

cNon-DM was defined as a self-reported medical history of no-DM and HbA1c level < 5.7%.

dP-value < 0.05 for test of null hypothesis that the odds ratio is equal to the odds ratio in the reference category.

Sex, age, BMI, HTN, DLP, gout, alcohol consumption, exercise, and smoking were adjusted.

OR was odd ratio with 95% confidence intervals (CI).

SIH, Siriraj Health; pre-DM, pre-diabetes; T2DM, type 2 diabetes mellitus; BMI, body mass index; WC, waist circumference; BP, blood pressure; MetS, metabolic syndrome; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; HTN, hypertension; DLP, dyslipidemia

Fig 2. Comparative analysis of pre-diabetes (Pre-DM) and type 2 diabetes (T2DM) to those without T2DM at baseline, and risk assessment for Pre-DM and T2DM development during follow-up, DLP, dyslipidemia; HTN, hypertension; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; MetS, metabolic syndrome; WC, waist circumference.

Fig 2

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The longitudinal analysis of the association between the changes in risk factors from baseline to follow-up (SIH1 only) and the relative risk of developing pre-DM or T2DM were analyzed using a multivariate multinomial logistic regression model (Table 4). Notably, several factors were significantly associated with the development of pre-DM. These included being aged 45 years or older (RRR = 1.96, 95% CI: 1.10–3.47), experiencing an increase in WC (RRR = 2.54, 95% CI: 1.12–5.75), developing MetS (RRR = 4.11, 95% CI: 2.23–5.58), remaining MetS (RRR = 2.27, 95% CI: 1.20–4.30), experiencing an increase in TG levels (RRR = 3.95, 95% CI: 1.86–8.36), remaining elevated TG levels (RRR = 5.01, 95% CI: 1.85–9.54). Interestingly, participants who maintained elevated TG exhibited even stronger and statistically significant associations with the development of T2DM (RRR = 5.27, 95% CI: 2.22–8.51). Furthermore, participants who experienced an increase in WC were significantly associated with the development of pre-DM but not T2DM. It is noteworthy that the certain risk factors associated with both the development of pre-DM and T2DM were older age, the development of MetS, the maintenance of MetS, an increase in TG, and the maintenance of elevated TG. Participants aged 45 years or older displayed an 83% increased relative risks of developing T2DM (RRR = 1.83, 95% CI: 1.10–3.05). Participants who developed MetS displayed a three-fold increased relative risk of developing T2DM (RRR = 3.10, 95% CI: 1.77–5.43), while participants with maintained MetS displayed a two-fold increased relative risk of developing T2DM (RRR = 2.00, 95% CI: 1.13–3.54). Associations with increases in TG concentrations, whether occurring or being maintained, were noticeably significant and strong among participants diagnosed with developing T2DM. The relative risk factors showing significant association among participants diagnosed with developing pre-DM or T2DM are also displayed in Fig 2.

Table 4. Changes in risk factors from baseline (SIH1) and follow-up (SIH1) and relative risk of developing pre-diabetesa (n = 120) or type 2 diabetes mellitusb (n = 48) in follow-up among individuals with normal blood glucose levelsc (N = 2,309) at baseline.

Factors Overall
(n = 2,477)		 Relative risk ratios (RRRs)
for individuals who developed pre-DM during the follow-up perioda 		Relative risk ratios (RRRs)
for individuals who developed T2DM during the follow-up periodb
RRR (95% CI) P-valued RRR (95% CI) P-valued
Sex        
  men 451 1.00   1.00  
  women 2,026 0.92 (0.51–1.64) 0.769 1.03 (0.61–1.74) 0.906
Age, years        
  < 35 848 1.00    1.00  
  35–45 1,059 0.88 (0.50–1.54) 0.659 0.77 (0.46–1.27) 0.298
  ≥ 45 570 1.96 (1.10–3.47) 0.022 1.83 (1.10–3.05) 0.020
BMI, kg/m2      
  Persistently normal 1,628 1.00     1.00  
  Becoming normal 91 1.75 (0.62–4.95) 0.292 1.87 (0.72–4.88) 0.200
  Persistently overweight 361 0.90 (0.42–1.92) 0.788 0.96 (0.47–1.97) 0.909
  Persistently obese 128 0.73 (0.25–2.16) 0.573 1.05 (0.41–2.66) 0.924
  Becoming overweight/obese 160 0.83 (0.34–2.03) 0.681 0.82 (0.35–1.91) 0.647
WC, cm          
  Never elevated 1,587 1.00     1.00   
  No longer elevated 109 0.66 (0.18–2.45) 0.538 0.75 (0.24–1.36) 0.623
  Became elevated 146 2.54 (1.12–5.75) 0.025 1.91 (0.86–4.20) 0.110
  Remained elevated 530 2.01 (0.92–4.42) 0.081 1.81 (0.87–3.76) 0.110
BP, mmHg        
  Never elevated 2,187 1.00    1.00  
  No longer elevated 74 0.92 (0.20–4.18) 0.917 1.62 (0.42–6.30) 0.486
  Became elevated 108 0.61 (0.23–1.62) 0.325 0.80 (0.32–2.00) 0.636
  Remained elevated 108 0.66 (0.19–2.29) 0.511 1.67 (0.56–4.98) 0.361
MetS        
  Never had MetS 1,206 1.00    1.00   
  No longer have MetS 210 0.97 (0.36–1.59) 0.952 0.80 (0.32–1.98) 0.629
  Developed MetS 378 4.11 (2.23–5.58) <0.001 3.10 (1.77–5.43) <0.001
  Remained with MetS 577 2.27 (1.20–4.30) 0.012 2.00 (1.13–3.54) 0.017
TC, mg/dL      
  Never elevated 1,331 1.00     1.00   
  No longer elevated 221 0.96 (0.41–2.28) 0.931 0.81 (0.37–1.78) 0.601
  Became elevated 297 1.72 (0.84–3.53) 0.138 1.46 (0.76–2.78) 0.252
  Remained elevated 593 1.29 (0.60–2.76) 0.508 0.96 (0.48–1.93) 0.917
TG, mg/dL        
  Never elevated 2,045 1.00    1.00   
  No longer elevated 125 2.35 (0.98–5.61) 0.054 2.03 (0.89–4.66) 0.094
  Became elevated 165 3.95 (1.86–8.36) <0.001 3.93 (1.96–7.87) <0.001
  Remained elevated 108 5.01 (1.85–9.54) 0.001 5.27 (2.22–8.51) <0.001
HDL-C, mg/dL      
  Remained elevated 1,993 0.50 (0.23–1.08) 0.076 0.92 (0.45–1.87) 0.812
  Became elevated 116 0.48 (0.15–1.56) 0.221 0.45 (0.14–1.45) 0.179
  No longer elevated 127 1.89 (0.76–4.68) 0.171 2.12 (0.86–5.18) 0.101
Never elevated 207 1.00    1.00 
Calculated LDL-C, mg/dL          
  Never elevated 1,751 1.00   1.00   
  No longer elevated 142 2.10 (0.91–4.86) 0.082 2.00 (0.91–4.41) 0.085
  Became elevated 189 1.19 (0.53–2.67) 0.668 1.21 (0.57–2.57) 0.625
  Remained elevated 171 1.32 (0.54–3.25) 0.541 1.46 (0.63–3.41) 0.376
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aPre-DM was defined as a fasting plasma glucose level between 100 and 125 mg/dL

bT2DM was defined as a fasting plasma glucose level ≥ 126 mg/ dL, and/or was diagnosed with diabetes.

cNormal blood glucose levels were defined as fasting plasma glucose concentrations <100 mg/dL.

dP-value < 0.05 for test of null hypothesis that the relative risk ratios are equal to the relative risk ratios in the reference category. Sex, age, WC, TC, BMI, and BP were adjusted.

RRR was relative risk ratio with 95% confidence intervals (CI).

SIH, Siriraj Health; pre-DM, pre-diabetes; T2DM, type 2 diabetes mellitus; BMI, body mass index; WC, waist circumference; BP, blood pressure; MetS, metabolic syndrome; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol

The average predicted probabilities of maintaining normal blood sugar levels, developing pre-DM, or T2DM during follow-up among participants without T2DM at baseline were analyzed by using the Stata margins command (Fig 3). The lowest probabilities of maintaining normal blood glucose levels and the highest probabilities of progressing to pre-DM or T2DM were observed among individuals who either maintained MetS (0.29, 95% CI: 0.18–0.35), developed MetS (0.19, 95% CI: 0.12–0.28), maintained increases in TG concentrations (0.30, 95% CI: 0.26–0.45), or occurred increases in TG concentrations (0.21, 95% CI: 0.15–0.32).

Fig 3. Average predicted probabilities of remaining with normal blood glucose levels, developing pre-diabetes (Pre-DM), or developing type 2 diabetes (T2DM) in follow-up among participants with normal blood glucose levels (n = 2,477) at baseline.

Fig 3

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BMI, body mass index; MetS, metabolic syndrome; WC, waist circumference; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; BP, blood pressure.

The distribution of genotypes and allelic frequencies for these TCF7L2 gene polymorphisms was presented in Table 5 and S2 Table in S2 File. Notably, the minor T allele of both rs7903146 (C/T) and rs4506565 (A/T) SNPs showed a trend toward increased risk for developing pre-DM, with odds ratios 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). Five SNPs (rs12243326, rs12255372, rs4132670, rs4506565, and rs7903146) did not exhibit significant associations with pre-DM and T2DM (S3 Table in S2 File). Additionally, after adjusting for age, it was found that men with the TT genotype of rs7917983 exhibited a 2.81-fold increased risk for T2DM compared to women with the same genotype. Moreover, women with the TT genotype of rs290487 showed a 2.27-fold increased risk for diabetes compared to men with the TT genotype as shown in S3 Table in S2 File.

Table 5. Genotypic variants of type 2 diabetes related single nucleotide polymorphisms association comparing pre-diabetes and type 2 diabetes mellitus to those without diabetes (control).

SNPs Risk allele % of Genotype Risk factors for Pre-DMa Risk factors for T2DMb
Control (n = 2,671) Pre-DMa (n = 844) T2DMb (n = 217) OR (95%CI) P-valuec OR (95%CI) P-valuec
rs7903146 TT 0.30 0.47 0.46 2.74 (0.32–23.3) 0.411 1.05 (0.12–9.51) 0.972
rs12255372 GT + TT 6.36 6.64 8.76 1.33 (0.77–2.30) 0.317 1.29 (0.77–2.17) 0.342
rs7917983 TT 16.74 15.17 14.75 0.84 (0.54–1.30) 0.307 0.96 (0.61–1.52) 0.804
rs4506565 TT 0.30 0.47 0.46 2.71 (0.32–23.07) 0.411 1.04 (0.11–9.47) 0.971
rs4132670 GT + TT 6.93 6.87 8.29 1.14 (0.67–1.93) 0.629 1.18 (0.67–2.08) 0.560
rs12243326 GT + TT 6.36 6.40 8.29 1.24 (0.73–2.10) 0.438 1.30 (0.74–2.28) 0.366
rs290487 TT 21.30 23.34 22.12 1.18 (0.78–1.79) 0.715 0.96 (0.62–1.47) 0.673
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aPre-DM was defined as a HbA1c level between 5.7 and 6.4%.

bT2DM was defined as a self-reported medical history of diabetes, current use of hypoglycemic medication, and/or a HbA1c level > 6.4%.

cP-value adjusted for age and sex; OR was an odd ratio with 95% confidence intervals (CI).

SNPs, single-nucleotide polymorphisms; pre-DM, pre-diabetes; T2DM, type 2 diabetes mellitus

Discussion

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 sex 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 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 [30]. Moreover, the observed T2DM prevalence in our study is similar to the 3.8% prevalence documented among 2,790 university hospital employees in Bangkok [31] and the 2.7% prevalence reported among 3,360 employees of the EGAT in Nonthaburi, an adjacent province of Bangkok [32]. 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 indicated a significant association between T2DM and factors such as being overweight, obesity, smoking, and alcohol consumption, consistent with observations in heterogenous populations [33, 34]. 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 [35, 36].

Our multivariate analysis revealed several pivotal risk factors associated with T2DM. Age, MetS, HTN, and DLP prominently featured in this array. Notably, the strength of association for these predictor variables varied depending on diabetes status. For instance, while being elevated TG levels, HTN, DLP, and smoking 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 [37, 38], attributed to elevated glycated hemoglobin levels and changes in insulin sensitivity [39, 40]. 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 [41], 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 indicated 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, 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 type) and TT (mutant) of rs7917983 and rs290487 across all groups. The mutant genotype was found in all groups, 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 [42, 43]. Similar results were obtained in studies where the T allele had no impact on the association with T2DM [44, 45]. 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 [46, 47]. 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 pre-DM, 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.

Conclusions

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 is in alignment with national and regional epidemiological data. Importantly, a substantial portion of the population remains unaware of their diabetes status, underscoring the need for proactive health promotion. Key contributors to onset of T2DM encompass advancing age, the presence of MetS, HTN, DLP, and the increases in TG concentrations 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.

Supporting information

S1 Table. Methods for measuring laboratory parameters.

(DOCX)

pone.0303085.s001.docx (37.6KB, docx)
S1 File. Genotyping analysis.

(DOCX)

pone.0303085.s002.docx (400KB, docx)
S2 File

(DOCX)

pone.0303085.s003.docx (50.6KB, docx)
S1 Fig. Diabetes classification by self-report and HbA1c among the participants (SIH1-2).

(DOCX)

pone.0303085.s004.docx (57.8KB, docx)

Acknowledgments

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.

Data Availability

The study is a part of the Siriraj Health (SIH) Cohort Study. The authors do not have the legal authority to distribute the data. However, all interested researchers can follow the respective rules and protocols of data sharing and scientific collaboration, just as the current authors have. The data are accessible upon request from the Siriraj Health study committee via email: sihealthstudy@mahidol.edu.

Funding Statement

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.

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Chikezie Hart Onwukwe

2 Jan 2024

PONE-D-23-40874Metabolic 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 StudyPLOS ONE

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: I Don't Know

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: There is no new massage in this study. However, there is severe concern about the cohort sample and it seems there is sampling bias with two incoherent . A large portion of sample (4,518 of 5011) aged 18-55 and a small portion (493 of 5011) aged more than 55 years and this affect the results. These two age groups have different characteristics including increased rate of diabetes and metabolic syndrome (which is the target of this study) in the second group but all of them have been analyzed with each other that biased the results. This bias also affects the result of genotyping study because many individuals in first age group (younger group) will develop diabetes in the next ages above 50 and belong to diabetes group but currently are in normal group.

The data should be re-analyzed after stratification of two samples and results should be arranged and presented after new analysis.

Reviewer #2: Dear Authors,

This manuscript addresses a very relevant subject, considering the increasing prevalence of type 2 diabetes and its complications, besides the evidence on the role of the TCF7L2 gene on the liability of developing type 2 diabetes related to some specific polymorphism, which may have differences among different populations that need clarifications. This manuscript deserves some grammar and punctuation revision. As pointed out by the authors, the lack of data related especially to HbA1C in the follow-up may have interfered with the final results of diabetes diagnosis. It reinforces the need for a more extensive and detailed study.

Yours Sincerely,

Reviewer #3: Pichanun Mongkolsucharitkul et al has submitted the research study “Metabolic and genetic risk factors associated with pre-diabetes and type 2 diabetes in Thai healthcare 2 employees: a long-term study from the Siriraj Health (SIH) Cohort Study” for review.

The study aims to explore NCD risk factors, biomarker relationships, and to develop a T2DM risk prediction model while 88 investigating the association between T2DM and genetic variants of the TCF7L2 gene.

My review comments on the manuscript as below:

1) The chosen research question is very much relevant to the ever-increasing prevalence of Diabetes in the world.

2) I request the author to proofread the manuscript for grammatic errors and language (e.g line 32, 33)

3) Methods:

a) Exclusion criteria and inclusion criteria may be defined more specifically.

b) S1 Fig: Why the ‘self-reported uncertain DM’ is excluded?

4) What are the criteria for metabolic syndrome, please define (line 198) how it has been coded as Yes and No

5) Research tools used, for example, the questionnaire used for participant interviews, Food frequency questionnaire is not submitted for review

6) Line 203- please explain the limitations of follow up data collection more explicitly. For example, if the participants were physically available or not,

7) Line 223 - T2DM with concurrent HT - data not given. Moreover, HT data could be given as systolic, diastolic range that is specifically coexist with DM.

8) Line 225 – Micro and macroalbuminuria, by itself are not modifiable. Only the progression to renal complications can be reduced with strict glycemic and HTN control. Please explain

9) Line 251 - 253: this observation may be explained more elaborately using the results of the data analysis.

10) Line 303 – repeated word- microalbuminuria

11) Genetic analysis was not reviewed

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

**********

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PLoS One. 2024 Jun 28;19(6):e0303085. doi: 10.1371/journal.pone.0303085.r002

Author response to Decision Letter 0

18 Feb 2024

February 19, 2024

Dear Editor,

We thank you for the opportunity to re-submit our revised manuscript entitled “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” (Manuscript ID: PONE-D-23-40874)

The reviewers’ comments were insightful. We have responded specifically to each suggestion below. To make changes easier to be identified, I have highlighted them in yellow in the text.

The manuscript has been reviewed and approved by all of the authors. Data in this manuscript have not been previously reported/published nor is it being considered for publication elsewhere. All correspondence, editorial communications, and reprint requests should be sent to the above address.

Our responses to the editor and reviewers’ comments are below.

We strongly hope you find our manuscript suitable for publication and look forward to hearing from you.

Sincerely,

Associate Professor Korapat Mayurasakorn, MD, FRCFPT

Family Physician

Lecturer

Email: korapat.may@mahidol.ac.th

Response to Editor

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Response 1: Thank you for your comment. We will ensure that our manuscript meets PLOS ONE's style requirements.

Point 2: For studies involving third-party data, we encourage authors to share any data specific to their analyses that they can legally distribute. PLOS recognizes, however, that authors may be using third-party data they do not have the rights to share. When third-party data cannot be publicly shared, authors must provide all information necessary for interested researchers to apply to gain access to the data. (https://journals.plos.org/plosone/s/data-availability#loc-acceptable-data-access-restrictions)

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Response 2: Thank you for your comment. We have addressed the data availability statement as follows. The study is a part of the Siriraj Health (SIH) Cohort Study. The authors do not have the legal authority to distribute the data. However, all interested researchers can follow the respective rules and protocols of data sharing and scientific collaboration, just as the current authors have. The data are accessible upon request from the Siriraj Health study committee via email: sihealthstudy@mahidol.edu.

Point 3: One of the noted authors is a group or consortium "Siriraj Population Health and Nutrition Research (SPHERE) group and SIH study group". In addition to naming the author group, please list the individual authors and affiliations within this group in the acknowledgments section of your manuscript. Please also indicate clearly a lead author for this group along with a contact email address.’

Response 3: Thank you for your comment. We have decided to remove the “Siriraj Population Health and Nutrition Research (SPHERE) group and SIH study group" as authors, as most of members of these groups are already included as authors, and the SPHERE group is also listed in the author’s affiliations. Instead, we will acknowledge the individual members of these groups.

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Response 4: Thank you for your concern. The map images in Figure 1 were created by Apinya Surawit, who is one of the authors, to use as the original images for illustrative purposes in our study only. We also uploaded the illustration file (final5.ai) that was the original file of the map images as the attached file in the other document. All of the figure files were already uploaded to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool. And Ms. Apinya Surawit has provided a signed content permission form.

 

Response to Reviewers

Reviewer #1

Point 1: There is no new massage in this study. However, there is severe concern about the cohort sample and it seems there is sampling bias with two incoherent. A large portion of sample (4,518 of 5011) aged 18-55 and a small portion (493 of 5011) aged more than 55 years and this affect the results. These two age groups have different characteristics including increased rate of diabetes and metabolic syndrome (which is the target of this study) in the second group but all of them have been analyzed with each other that biased the results. This bias also affects the result of genotyping study because many individuals in first age group (younger group) will develop diabetes in the next ages above 50 and belong to diabetes group but currently are in normal group. The data should be re-analyzed after stratification of two samples and results should be arranged and presented after new analysis.

Response 1: Thank you for your feedback. We have carefully reviewed your concerns regarding potential sampling bias in our study cohort and acknowledge the importance of addressing this issue to ensure the validity of our results. We also would like to clarify that in our first enrollment, known as "Siriraj Health Study 1 (SIH-1)," we had 4,528 participants aged 18-55 years, while the second enrollment, which began in January 2020, consisted of 493 participants aged over 18 years old.

We have rephrased in Line 108-110, “Beginning in January 2020, the second phase of the Siriraj Health (SIH2) study included participants aged over 18 years with no upper age limit. However, due to the COVID-19 pandemic from 2020 to 2022, only 493 participants were enrolled.” It is worth noting that participants in both SIH1 and SIH2 did not exhibit significantly different age-group distributions, as illustrated in the following unpublished table (for your information only).

Age, years SIH1 (n=4,518) SIH2 (n=493) Total (n=5,011)

n % n % n %

< 30 1,258 27.84 157 31.85 1,415 28.24

30 - 39 1,899 42.03 174 35.29 2,073 41.37

40 - 49 1,038 22.97 91 18.46 1,129 22.53

50 - 55 322 7.13 28 5.68 350 6.98

≥ 56 1 0.02 43 8.72 44 0.88

We also reanalyzed the progression of diabetes development among individuals who were not diagnosed with diabetes to evaluate the relative risk of developing pre-diabetes or diabetes at follow-up as shown in Table 4 and Fig 2.

 

Reviewer #2

Point 1: This manuscript addresses a very relevant subject, considering the increasing prevalence of type 2 diabetes and its complications, besides the evidence on the role of the TCF7L2 gene on the liability of developing type 2 diabetes related to some specific polymorphism, which may have differences among different populations that need clarifications. This manuscript deserves some grammar and punctuation revision. As pointed out by the authors, the lack of data related especially to HbA1C in the follow-up may have interfered with the final results of diabetes diagnosis. It reinforces the need for a more extensive and detailed study.

Response 1: Thank you for your feedback. We appreciate your acknowledgment of the relevance of our study topic and the importance of addressing potential genetic influences on type 2 diabetes (T2DM) susceptibility, particularly related to the TCF7L2 gene. We have explained more about TCF7L2 gene in Line 86-90, “In Asian populations, including Japanese [22], Thai [23], and Chinese [24], variants of the TCF7L2 gene, such as rs7903146, rs11196205, and rs12255372, have been identified as significant genetic risk factors for T2DM. These findings highlight the genetic heterogeneity of T2DM across different ethnic groups and underscore the importance of understanding population-specific genetic determinants of the disease.”

We acknowledge your suggestion for grammar and punctuation revision, and we will ensure to thoroughly review and enhance these aspects to improve the clarity and readability of the manuscript. Additionally, we acknowledge the importance of addressing the highlighted limitations regarding the lack of data related to HbA1C in the follow-up period due to the nature of a routine annual health examination policy, which may have influenced the final results of diabetes diagnosis, as indicated in Line 224-228, 423-424. However, we would like to clarify that although we lack HbA1C data, we do possess information related to fasting blood glucose collected during the follow-up period. This enables us to monitor the progression of diabetes development among individuals who were not diagnosed with diabetes at baseline, as illustrated in Table 4 and Fig 2.

 

Reviewer #3

Point 1: Pichanun Mongkolsucharitkul et al has submitted the research study.

“Metabolic and genetic risk factors associated with pre-diabetes and type 2 diabetes in Thai healthcare 2 employees: a long-term study from the Siriraj Health (SIH) Cohort Study” for review. The study aims to explore NCD risk factors, biomarker relationships, and to develop a T2DM risk prediction model while investigating the association between T2DM and genetic variants of the TCF7L2 gene.

My review comments on the manuscript as below:

1) The chosen research question is very much relevant to the ever-increasing prevalence of Diabetes in the world.

Response 1: Thank you for recognizing the relevance of our chosen research question. We agree that the prevalence of diabetes globally is a critical issue, and we believe that addressing this research question will contribute to a better understanding of the challenges and potential solutions in managing this health concern. We are committed to conducting thorough and insightful research that can offer valuable insights into addressing the complexities surrounding diabetes. And we will continue to monitor the progress of NCD for this cohort.

Point 2: I request the author to proofread the manuscript for grammatic errors and language (e.g line 32, 33)

Response 2: Thank you for your comment. We acknowledge your suggestion for grammar and language revision, and we will ensure to thoroughly review and enhance these aspects to improve the clarity and readability of the manuscript (highlighted in yellow).

Point 3: Methods: Exclusion criteria and inclusion criteria may be defined more specifically.

Response 3: Thank you for your comment. We have added the inclusion and exclusion criteria in Line 100-104, “The inclusion criteria of the study were the Siriraj Hospital personnel who attended an annual health screening surveillance program. The exclusion criteria of the study were individuals who withheld treatment information, unable to consistently follow-up, unable to participate in this cohort in the next 2 years, or presence of contraindications for blood sampling, such as blood clotting.”

Point 4: S1 Fig: Why the ‘self-reported uncertain DM’ is excluded?

Response 4: Thank you for your comment. The exclusion of individuals who self-reported uncertainty about whether they have diabetes, along with those with other missing data, was a decision made due to the limitations inherent in the survey data collection process. When participants are unsure about their diabetes status or when there are missing data points, it can introduce ambiguity and potential inaccuracies into the analysis. To maintain the reliability and validity of the study results, the research team opted to exclude these individuals from the analysis in this instance.

Point 5: What are the criteria for metabolic syndrome, please define (line 198) how it has been coded as Yes and No

Response 5: Thank you for your comment. We have added the criteria for metabolic syndrome in Line 158-162, “We used the criteria of metabolic syndrome (MetS) in adults by the IDF definition with South Asian e

Attachment

Submitted filename: Response to Reviewers.docx

pone.0303085.s005.docx (54.2KB, docx)

Decision Letter 1

Chikezie Hart Onwukwe

10 Mar 2024

PONE-D-23-40874R1Metabolic 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 StudyPLOS ONE

Dear Dr. Mayurasakorn,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

==============================

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

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We look forward to receiving your revised manuscript.

Kind regards,

Chikezie Hart Onwukwe

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: Dear Authors,

Considering Siriraj Hospital personnel are involved in an annual health screening surveillance program, and about half the population is overweight and obese, it is expected that they receive diet and exercise orientations. As you didn't check behavioral changes, fasting blood glucose may not be enough to diagnose all participants and mislead the conclusions.

You spent much of your manuscript discussing the well-known risk factors for developing diabetes. I expected some comments on the possible influence of such metabolic factors on the outcomes you may have found in your genetic findings and comparisons of different age groups.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

**********

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2024 Jun 28;19(6):e0303085. doi: 10.1371/journal.pone.0303085.r004

Author response to Decision Letter 1

27 Mar 2024

Dear Editor,

We thank you for the opportunity to re-submit our revised manuscript entitled “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” (Manuscript ID: PONE-D-23-40874R1)

The reviewers’ comments were insightful. We have responded specifically to each suggestion below. To make changes easier to be identified, I have highlighted them in yellow in the text.

The manuscript has been reviewed and approved by all of the authors. Data in this manuscript have not been previously reported/published nor is it being considered for publication elsewhere. All correspondence, editorial communications, and reprint requests should be sent to the above address.

Our responses to the editor and reviewers’ comments are below.

We strongly hope you find our manuscript suitable for publication and look forward to hearing from you.

Sincerely,

Associate Professor Korapat Mayurasakorn, MD, FRCFPT

Family Physician

Lecturer

Email: korapat.may@mahidol.ac.th

Response to Editor

Point 1: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Response 1: Thank you for your comment. We acknowledge your suggestion for reference revision, and we will ensure to thoroughly recheck and enhance these aspects to improve the correct template of the reference list (highlighted in yellow). We did not cite any retracted articles in this manuscript.

Response to Reviewers

Reviewer #2

Point 1: Dear Authors,

Considering Siriraj Hospital personnel are involved in an annual health screening surveillance program, and about half the population is overweight and obese, it is expected that they receive diet and exercise orientations. As you didn't check behavioral changes, fasting blood glucose may not be enough to diagnose all participants and mislead the conclusions.

You spent much of your manuscript discussing the well-known risk factors for developing diabetes. I expected some comments on the possible influence of such metabolic factors on the outcomes you may have found in your genetic findings and comparisons of different age groups.

Response 1: Thank you for your insightful comments and suggestions regarding our manuscript. We appreciate your observation regarding the importance of assessing behavioral changes alongside metabolic markers in our study population. We agree that incorporating such assessments would provide a more comprehensive understanding of the outcomes.

Regarding the involvement of Siriraj Hospital personnel in an annual health screening surveillance program, it's pertinent to note that while the hospital does provide health check-up reports to its workers, there's no mandatory provision of dietary and exercise programs. Consequently, behavioral changes primarily occur on a voluntary basis. We will certainly consider including measures of behavioral changes in future studies to address this limitation and provide a more robust analysis.

We acknowledge that relying solely on fasting blood glucose (FBG) levels may not capture all participants at risk for diabetes. In Thailand, FBG levels serve as a standard early health screening measure, offering insights into glucose trends over time. While we did not utilize HbA1c in screening, recognizing its higher accuracy in defining diabetes, implementing more complicated tests could have resulted in increased missing data due to the logistical challenges associated with obtaining blood samples from the population.

However, it's essential to acknowledge the fundamental differences in the pathophysiology between HbA1c and fasting blood glucose. HbA1c reflects the average blood glucose levels over the past 2-3 months, providing a more stable indicator of long-term glycemic control, whereas fasting blood glucose offers a snapshot of blood sugar levels at a specific point in time. Despite these differences, in our pursuit of precise predictions, we attempted to compare HbA1c values with a limited number of samples, contrasting them with the FBG values that were tracked. Although the pathophysiology underlying HbA1c and FBG levels may differ, it was observed that the trends of both values move in the same direction, as illustrated in the following unpublished figure (for your information only).

Furthermore, we understand your expectation for a discussion on the influence of metabolic factors on our genetic findings and comparisons across different age groups. Indeed, the observed association between the TCF7L2 gene (all 7 SNPs) and the decreased risk of type 2 diabetes mellitus (T2DM) in individuals aged 40 and above could potentially be influenced by statistical fluctuations, especially in studies with small sample sizes. This concern is particularly relevant when studying complex traits like T2DM, where genetic associations may be subtle and influenced by various environmental and genetic factors. Additionally, the researchers did not conduct a subgroup study to analyze genetic risk factors in relation to other metabolic risk factors, given the small size of the genetic sample.

As illustrated in the following unpublished table (for your information only), we observed significant associations between TCF7L2 SNPs and both pre-DM and T2DM across different age strata. Notably, the rs12243326, rs12255372, rs290487, rs4132670, rs4506565, rs7903146, and rs7917983 variants exhibited varying degrees of association with pre-DM and T2DM risk, with some associations being more pronounced in certain age groups.

Regarding the influence of metabolic factors on our genetic findings, we conducted adjusted analyses, accounting for sex, body mass index (BMI), waist circumference (WC), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL). These adjustments allowed us to assess the independent effects of TCF7L2 SNPs on pre-DM and T2DM risk while controlling for potential confounders related to metabolic health.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0303085.s006.docx (68.3KB, docx)

Decision Letter 2

Chikezie Hart Onwukwe

19 Apr 2024

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

PONE-D-23-40874R2

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Acceptance letter

Chikezie Hart Onwukwe

26 Apr 2024

PONE-D-23-40874R2

PLOS ONE

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Methods for measuring laboratory parameters.

    (DOCX)

    pone.0303085.s001.docx (37.6KB, docx)
    S1 File. Genotyping analysis.

    (DOCX)

    pone.0303085.s002.docx (400KB, docx)
    S2 File

    (DOCX)

    pone.0303085.s003.docx (50.6KB, docx)
    S1 Fig. Diabetes classification by self-report and HbA1c among the participants (SIH1-2).

    (DOCX)

    pone.0303085.s004.docx (57.8KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0303085.s005.docx (54.2KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0303085.s006.docx (68.3KB, docx)

    Data Availability Statement

    The study is a part of the Siriraj Health (SIH) Cohort Study. The authors do not have the legal authority to distribute the data. However, all interested researchers can follow the respective rules and protocols of data sharing and scientific collaboration, just as the current authors have. The data are accessible upon request from the Siriraj Health study committee via email: sihealthstudy@mahidol.edu.

    Articles from PLOS ONE are provided here courtesy of PLOS

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