Epidemiology of prediabetes mellitus among hill tribe adults in Thailand.

BACKGROUND: Prediabetes is a major silent health problem that leads to the development of diabetes within a few years, particularly among those who have a low socioeconomic status. Hill tribe people are vulnerable to prediabetes due to their unique cultural cooking methods and their hard work on farms, as well as their low economic status and educational levels. This study aimed to estimate the prevalence of prediabetes among hill tribe people in Thailand and identify the related factors.
METHODS: This cross-sectional study included participants who belong to one of the six main hill tribes: Akah, Lahu, Hmong, Yao, Karen, and Lisu. The study was conducted in 30 hill tribe villages in Chiang Rai Province, Thailand. A validated questionnaire was administered, and 5-mL blood specimens were collected. Data were collected between November 2019 and March 2020. Logistic regression was used to determine the associations between independent variables and prediabetes.
RESULTS: A total of 1,406 participants were recruited for the study; 67.8% were women, 77.2% were between 40 and 59 years old, and 82.9% were married. The majority worked in the agricultural sector (57.2%), had an annual income ≤ 50,000 baht (67.5%), and had never attended school (69.3%). The prevalence of prediabetes was 11.2%. After controlling for age and sex, five factors were found to be associated with prediabetes. Members of the Akha and Lisu tribes had 2.03 (95% CI = 1.03-3.99) and 2.20 (95% CI = 1.10-4.42) times higher odds of having prediabetes than Karen tribe members, respectively. Those with hypertension (HT) had 1.47 (95% CI = 1.03-2.08) times higher odds of having prediabetes than those with normal blood pressure. Those with a normal total cholesterol level had 2.43 (95% CI = 1.65-3.58) times higher odds of having prediabetes than those with a high total cholesterol level. Those with a high triglyceride level had 1.64 (95% CI = 1.16-2.32) times higher odds of having prediabetes than those with a normal triglyceride level. Those with a high low-density lipoprotein cholesterol (LDL-C) level had 1.96 (95% CI = 1.30-2.96) times higher odds of having prediabetes than those with a normal LDL-C level.
CONCLUSION: Appropriate dietary guidelines and exercise should be promoted among hill tribe people between 30 and 59 years old to reduce the probability of developing prediabetes.

Introduction

Prediabetes is a silent health problem worldwide [1]. With only minor signs and symptoms, a large proportion of people with prediabetes are not diagnosed or cared for properly [1]. The Centers for Disease Control and Prevention (CDC) in the United States established the criterion for prediabetes as a hemoglobin A1c (HbA1c) level of 5.7–6.4% [2]. Yip et al. [3] reported the prevalence of prediabetes, with different rates in different populations, for example, 13.5% in Caucasian populations and 18.2% in Asian populations, by using combined impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) methods. Notably, 5.0%–10.0% of people with prediabetes will develop diabetes mellitus (DM) each year [4]. This imposes a tremendous burden on the health care system, particularly due to high annual medical expenses [5], and reduces the quality of life of individuals with overt diabetes [6]. This problem will more than double among people with a poor economic status, especially in developing countries.

The Ministry of Public Health, Thailand, reported that the overall prevalence of prediabetes among Thai individuals 15 years and older was 10.7% (11.8% among men and 9.5% among women) [7]. The prevalence was high among people 30 years and older. Those who had a low socioeconomic status had low rates of proper diagnosis and care [1, 2]. The Thai government allocates a large amount of money yearly to care for patients with noncommunicable diseases, including diabetes [8]. Identifying people with prediabetes in a community, especially among those with a poor socioeconomic status, would be greatly advantageous for the design and implementation of proper interventions to reduce the rate of DM development or delay its onset.

Hill tribes comprise populations of people who have migrated from southern China to the northern region of Thailand over the past few centuries [9, 10]. There are six main groups: Akha, Lahu, Hmong, Yao, Karen, and Lisu [10]. Almost all have their own culture, language, and lifestyle, which are close to those of Chinese people, except the Karen, who originated from the Thailand-Myanmar border [10]. Today, more than 70.0% of these individuals have been granted Thai identification cards, which indicate Thai citizenship, are used to access all public services, including medical care [11, 12]. However, there is no scientific information available about prediabetes among the hill tribe people in Thailand. Thus, the objectives of this study were to examine the prevalence of prediabetes among hill tribe populations over 30 year old living in northern Thailand and identify the related factors.

Methods

Study design/study setting

A community-based cross-sectional study was performed to gather information from participants who lived in 30 hill tribe villages located in 18 districts in Chiang Rai Province, Thailand. In 2019, there were 749 hill tribe villages in Chiang Rai Province, which included 316 Lahu villages (51,339 persons), 243 Akha villages (74,403 persons), 63 Yao villages (16,227 persons), 56 Hmong villages (33,478 persons), 36 Karen villages (7,933 persons), and 35 Lisu villages (9,632 persons) [12].

Study population and eligible population

The study population comprised hill tribe people who belonged to one of the six main tribes: Akah, Lahu, Hmong, Yao, Karen, and Lisu. Those who lived in the 30 selected hill tribe villages (five villages from each tribe) and were between 30 and 59 years old met the inclusion criteria. However, those who were previously diagnosed with DM, who could not provide essential information according to the study protocol, and who did not comply with the no food or beverage (NPO) instructions for the 12 hours before blood sample collection were excluded from this study.

Sample size calculation

The sample size was calculated according to the standard formula for a cross-sectional study as a proportion [13], n = [Z2α/2*P*Q]/e2, where Z = the value of the standard normal distribution corresponding to the desired confidence level (Z = 1.96 for 95% CI), P = the expected true proportion, which was based on a previous study conducted among Thai adults, at 32.2% [14], and e = the desired precision, or percentage of the accepted deviation, which was 6.00%. Therefore, at least 1,398 participants were required for analysis.

After the sample size was calculated, five villages from each tribe were randomly selected by a computer-generated randomization method as shown in the following flowchart (Fig 1). Afterward, all people aged 30–59 years living in the selected villages were invited to participate in the study. All people were screened according to the inclusion and exclusion criteria before the initiation of data collection.

Fig 1

Flowchart of sample selection in the study.

Research instrument and its development

The questionnaire was developed and tested before use. The questionnaire was divided into three parts. In part one, twelve questions were used to collect sociodemographic information, such as age, sex, education, tribe, religion, and occupation. In part two, three questions were used to collect information about exercise, alcohol consumption and smoking. In the last part, eight open-ended questions were used to collect information about the physical examination and laboratory results, such as weight, height, blood pressure, and lipid profiles.

Item-objective congruence (IOC) was applied to improve the validity of the questionnaire. Using this method, three external experts (one medical doctor, one epidemiologist, and one public health professional) were invited to comment on the relevance of the questions in the questionnaire and the context of the study, including the objective of the study. There were three options provided to score each question. The questions were scored as -1 if they were not related to the content of the study, 0 if they required revision before use, and +1 if they reflected the content of the study. Afterward, the scores for each question assigned by the three experts were summed and divided by three. The number obtained was used to decide whether to retain the question in the questionnaire set. If the summed score was less than 0.5, the question was removed from the questionnaire. Questions scoring 0.5–0.7 were revised according to the comments before being included in the questionnaire. Questions scoring more than 0.7 were considered acceptable to include in the questionnaire.

Afterward, the questionnaire was piloted among 20 hill tribe people who had characteristics similar to those of the study population. In this step, we aimed to detect its reliability, the proper sequence of questions, and the ability of the target population to understand the questions. The pilot study was performed in a hill tribe village in Mae Chan District, Chiang Rai Province.

Operational definitions

Prediabetes was defined as an HbA1c level between 5.7% and 6.4% according to the CDC guidelines [2]. Body mass index (BMI) was classified into three categories: equal to or less than 18.50 was defined as underweight, 18.51–22.99 was defined as normal weight, and equal to or higher than 23.00 was defined as overweight [15]. Hypertension (HT) was classified as systolic blood pressure (SBP) equal to or greater than 140 mmHg, diastolic blood pressure (DBP) equal to or greater than 90 mmHg, or both [16]. Total cholesterol was classified into two categories: normal (<200 mg/dL) and high (≥200 mg/dL) [17]. High-density lipoprotein cholesterol (HDL-C) was classified into two categories for males, normal (≥40 mg/dL) and low (<40 mg/dL), and two categories for females, normal (≥50 mg/dL) and low (<50 mg/dL) [17]. LDL-C was classified into two categories: normal (<100 mg/dL) and high (≥100 mg/dL) [17]. Triglycerides were classified into two categories: normal (<150 mg/dL) and high (≥150 mg/dL) [17].

Data collection procedures

Five villages from each tribe were randomly selected from a list of the relevant hill tribe villages in Chiang Rai Province by a computer-generated method. Access to the villages was granted by district government officers. All selected village headmen were contacted 5 days prior to the date of data collection to provide essential information regarding the study, especially the target population and the inclusion and exclusion criteria. One day before the research team reached the village, the village headman informed all participants about the 12-hour NPO prior to blood specimen collection. On the date of data collection, each of the participants was provided all essential information again before providing voluntary informed consent. Those who were able to read and write in Thai completed the forms by themselves. However, those who could not understand Thai were helped by village health volunteers who were fluent in both Thai and the local language. Completion of the questionnaire and the collection of 5-mL blood specimens took approximately 25 minutes each. Data were collected between November 2019 and July 2020.

Laboratory work

All laboratory work was performed at the Mae Fah Luang Medical Laboratory Center. The latex-enhanced immunoturbidimetric method (RANDOX@) was used to measure HbA1c; this method has been certified by the National Glycohemoglobin Standardization Program and standardized to the Diabetes Control and Complication Trial reference. All lipid profiles, i.e., total cholesterol, HDL-C, and LDL-C levels, were assessed by the direct clearance method. Triglyceride levels were measured by the glycerin phosphate oxidase peroxidase method.

Statistical analysis

All completed questionnaires were coded and entered into an Excel sheet. The data were checked and managed for errors and missing data before being uploaded to the SPSS program (version 24, Chicago, IL) for analysis. Means and standard deviations are presented for continuous data with a normal distribution, while medians and interquartile ranges (IQRs) are presented for continuous data with a skewed distribution. Logistic regression was used to detect the association at a significance level of α = 0.05 in both the univariate and multivariate models. The “ENTER” mode was used to select independent variables in the model, and the final model was shown by the Hosmer Lemeshow Chi-square test to be suitable. Before interpreting the final model in the multivariate analysis, age and sex were controlled for as confounding factors.

Ethics approval

All of the study concepts and the protocol were approved by the Mae Fah Luang University Research Ethics Committee on Human Research (No. REH-6100) before project commencement. Participants were provided all essential information before providing written informed consent. For those who could not understand Thai, village health volunteers provided the information in the local language before asking these participants to voluntarily provide a fingerprint representing informed consent.

Results

General characteristics of the participants

A total of 1,406 participants were recruited for the study; 67.8% were female, 77.2% were between 40 and 59 years old (mean = 46.1, SD = 8.0), and 82.9% were married. The majority had never attended school (69.3%), worked in the agricultural sector (57.2%), and had an annual income ≤ 50,000 baht (67.5%), with a median of 30,000 baht (IQR = 44,500). Some participants reported that their parents had been diagnosed with DM: 5.3% of fathers and 7.3% of mothers (Table 1).

Table 1

General characteristics of the participants.

Factors	n	%
Total	1,406	100.0
Sex
Male	453	32.2
Female	953	67.8
Age (years)
30–39	321	22.8
40–49	538	38.3
50–59	547	38.9
Marital status
Single	86	6.1
Married	1,166	1,166
Ever married	154	11.0
Family members (people)
≤ 4	722	51.4
5–8	586	41.6
≥9	98	7.0
Tribe
Karen	225	16.0
Akha	408	29.0
Lahu	236	16.8
Hmong	198	14.1
Yao	191	13.6
Lisu	148	10.5
Religion
Buddhist	716	50.9
Christian or Muslim	690	49.1
Education
Never attended a school	975	69.3
Primary school	250	17.8
Secondary school and higher	181	12.9
Occupation
Unemployed	190	13.5
Agriculturist	776	55.2
Daily employment or trader	440	31.3
Annual income (baht)
≤ 50,000	949	67.5
50,001–100,000	338	24.0
≥ 100,001	119	8.5
Family debt
No	862	61.3
Yes	544	38.7
Paternal DM history
No	894	63.6
Yes	75	5.3
Do not know	437	31.1
Maternal DM history
No	895	63.6
Yes	102	7.3
Do not know	409	29.1
Smoking
No	1,057	72.5
Yes	349	24.8
Alcohol consumption
No	1034	73.5
Yes	372	26.5
Exercise
No	844	60.0
Sometimes	460	32.7
Regularly	102	7.3
BMI
Normal weight	434	30.9
Underweight	69	4.9
Overweight	903	64.2
Hypertension
No	995	70.8
Yes	411	29.2
Total cholesterol
Normal	771	54.8
High	635	45.2
Triglycerides
Normal	834	59.3
High	572	40.7
HDL-C
Normal	636	45.2
Low	770	54.8
LDL-C
Normal	424	30.2
High	982	69.8

One-fourth (24.8%) of the participants smoked, 26.5% used alcohol, 60.0% did not exercise, 19.9% had moderate-to-high stress, and 10.1% reported depressive symptoms. A large proportion were classified as overweight (64.2%), 45.2% had high total cholesterol, 40.7% had high triglycerides, and 69.8% had high LDL-C (Table 1).

Prevalence of prediabetes

One hundred fifty-eight (11.2%) out of 1,406 participants had an HbA1c level between 5.7% and 6.4%, indicating prediabetes (Table 2). The prevalence did not differ according to sex (p-value = 0.357), age (p-value = 0.273), or tribe (p-value = 0.066).

Table 2

Factors associated with prediabetes mellitus in the univariate and multivariate logistic regression analyses.

Factors	Prediabetes mellitus		Univariate analysis			Multivariate analysis
	Yes n (%)	No n (%)	OR	95% CI	p value	AOR	95% CI	p value
Total	158 (11.2)	1,248 (88.8)	N/A	N/A	N/A	N/A	N/A	N/A
Sex
Male	56 (35.4)	397 (31.8)	1.18	0.83–1.67	0.358	0.90	0.55–1.46	0.677
Female	102 (64.6)	851 (68.2)	1.00			1.00
Age (years)
30–39	39 (24.7)	282 (22.6)	1.00			1.00
40–49	67 (42.4)	471 (37.7)	1.03	0.68–1.57	0.896	0.99	0.62–1.60	0.989
50–59	52 (32.9)	495 (39.7)	0.76	0.49–1.18	0.221	0.79	0.47–1.30	0.355
Marital status
Single	13 (8.2)	73 (5.8)	1.00			1.00
Married	131 (82.9)	1,035 (82.9)	0.71	0.38–1.32	0.278	0.74	0.38–1.45	0.375
Ever married	14 (8.9)	140 (11.2)	0.56	0.25–1.26	0.161	0.55	0.23–1.32	0.181
Family members (people)
≤ 4	81 (51.3)	641 (51.4)	1.00			1.00
5–8	64 (40.5)	522 (41.8)	0.97	0.69–1.37	0.865	0.90	0.62–1.30	0.567
≥9	13 (8.2)	85 (6.8)	1.21	0.65–2.27	0.551	1.17	0.60–2.28	0.651
Tribe
Karen	18 (11.4)	207 (16.6)	1.00			1.00
Akha	51 (32.3)	357 (28.6)	1.64	0.94–2.89	0.084	2.03	1.03–3.99	0.041**
Lahu	19 (12.0)	217 (17.4)	1.01	0.51–1.97	0.984	0.97	0.46–2.01	0.924
Hmong	24 (15.2)	174 (13.9)	1.59	0.83–3.02	0.160	1.49	0.74–2.99	0.269
Yao	21 (13.3)	170 (13.6)	1.42	0.73–2.75	0.298	1.54	0.77–3.10	0.224
Lisu	25 (15.8)	123 (9.9)	2.34	1.23–4.46	0.010*	2.20	1.10–4.42	0.027**
Religion
Buddhist	87 (55.1)	629 (50.4)	1.21	0.87–1.68	0.270	1.50	0.97–2.31	0.070
Christian or Muslim	71 (44.9)	619 (49.6)	1.00			1.00
Education
Never attended a school	104 (65.8)	871 (69.8)	0.82	0.51–1.33	0.421	0.74	0.40–1.37	0.336
Primary school	31 (17.5)	219 (19.6)	0.97	0.55–1.73	0.924	1.01	0.53–1.90	0.985
Secondary school and higher	23 (14.6)	158 (12.7)	1.00			1.00
Occupation
Unemployed	29 (18.4)	161 (12.9)	1.59	0.95–2.64	0.075	1.62	0.94–2.80	0.082
Agriculturist	85 (53.8)	691(55.4)	1.06	0.72–1.57	0.737	1.03	0.67–1.59	0.891
Daily employment or trader	44 (27.8)	396 (31.7)	1.00			1.00
Annual income (baht)
≤ 50,000	108 (68.4)	841 (67.4)	0.72	0.42–1.24	0.234	0.72	0.41–1.39	0.271
50,001–100,000	32 (20.3)	306 (24.5)	0.58	0.32–1.10	0.092	0.54	0.28–1.04	0.066
≥ 100,001	18 (11.4)	101 (8.1)	1.00			1.00
Family debt
No	97 (61.4)	765 (61.3)	1.00			1.00
Yes	61 (38.6)	483 (38.7)	0.99	0.71–1.40	0.982	1.16	0.79–1.70	0.440
Paternal DM history
No	95 (60.1)	799 (64.0)	1.00			1.00
Yes	7 (4.4)	68 (5.4)	0.87	0.39–1.94	0.726	0.78	0.33–1.84	0.568
Do not know	56 (35.4)	381 (30.5)	1.24	0.87–1.76	0.238	1.62	0.83–3.15	0.157
Maternal DM history
No	100 (63.3)	795 (63.7)	1.00			1.00
Yes	10 (6.3)	92 (7.4)	0.86	0.44–1.71	0.676	0.75	0.36–1.55	0.432
Do not know	48 (30.4)	361 (28.9)	1.06	0.73–1.52	0.766	0.73	0.37–1.46	0.375
Smoking
No	109 (69.0)	948 (76.0)	1.00			1.00
Yes	49 (31.0)	300 (24.0)	1.42	0.99–2.04	0.057	1.65	0.98–2.61	0.053
Alcohol consumption
No	111 (70.3)	923 (74.0)	1.00			1.00
Yes	47 (29.7)	325 (26.0)	1.20	0.84–1.73	0.320	0.92	0.57–1.49	0.727
Exercise
No	99 (62.7)	745 (59.7)	2.58	1.02–6.49	0.044*	2.70	1.00–7.12	0.051
Sometimes	54 (34.2)	406 (32.5)	2.58	1.01–6.23	0.049*	2.56	0.95–6.90	0.063
Regularly	5 (3.2)	97 (7.8)	1.00			1.00
BMI
Normal weight	44 (27.8)	390 (31.2)	1.00			1.00
Underweight	9 (5.7)	60 (4.8)	1.33	0.62–2.86	0.467	1.41	0.62–3.18	0.410
Overweight	105 (66.5)	798 (63.9)	1.17	0.80–1.69	0.418	1.23	0.83–1.84	0.303
Hypertension
No	100 (63.3)	895 (71.7)	1.00			1.00
Yes	58 (36.7)	353 (28.3)	1.47	1.04–2.08	0.029*	1.47	1.03–2.12	0.036**
Total cholesterol
Normal	105 (66.5)	666 (53.4)	1.73	1.22–2.45	0.002*	2.42	1.59–3.67	<0.001**
High	53 (33.5)	582 (46.6)	1.00			1.00
Triglycerides
Normal	84 (53.2)	750 (60.1)	1.00			1.00
High	74 (46.8)	498 (39.9)	1.33	0.95–1.85	0.095	1.79	1.21–2.66	0.004**
HDL-C
Normal	67 (42.4)	569 (45.6)	1.00			1.00
Low	91 (57.6)	679 (54.4)	1.14	0.82–1.59	0.448	0.97	0.64–1.46	0.867
LDL-C
Normal	40 (25.3)	384 (30.8)	1.00			1.00
High	118 (74.7)	864 (69.2)	1.32	0.90–1.91	0.160	1.90	1.24–2.91	0.003**

N/A = Not applicable

* Significance level at α = 0.05

** Significance level at α = 0.05 after controlling for sex and age.

Factors associated with prediabetes

In the univariate analysis, five factors were found to be associated with prediabetes: tribe, exercise, total cholesterol, LDL-C, and HT (Table 2).

After controlling for age and sex in the multivariate analysis, five factors were found to be associated with prediabetes. Akha and Lisu tribe members had 2.03 (95% CI = 1.03–3.99) and 2.20 (95% CI = 1.10–4.42) times higher odds of having prediabetes than Karen members, respectively. Participants who had HT had 1.47 (95% CI = 1.03–2.08) times higher odds of having prediabetes than those who had normal blood pressure. Those who had a normal total cholesterol level had 2.43 (95% CI = 1.65–3.58) times higher odds of having prediabetes than those who had a high total cholesterol level. Those who had a high triglyceride level had 1.64 (95% CI = 1.16–2.32) times higher odds of having prediabetes than those who had a normal triglyceride level. Those who had a high LDL-C level had 1.96 (95% CI = 1.30–2.96) times higher odds of having prediabetes than those who had a normal LDL-C level (Table 2).

Discussion

The hill tribe people between 30 and 59 years old in Thailand have a low socioeconomic status, a low education level, and a low income and work in unskilled jobs. One-fourth of them consumed alcohol and smoked, while only a few people practiced regular exercise. A large proportion were overweight, had an abnormal lipid profile, and suffered from HT. The overall prevalence of prediabetes was 11.2%. Several factors were found to be associated with prediabetes: triglycerides, total cholesterol, LDL-C and HT.

The prevalence of prediabetes among hill tribe people between 30 and 59 years old was 11.2%. The rates of prediabetes among different populations and different countries vary greatly: 22.9% among Bangladeshi people 35 years and older [18], 40.9% among people between 18 and 70 years old in China [19], 35.0% among the adult Omani population [20], 52.9% among Vietnamese people between 45 and 69 years old [21], and 32.2% among Thai adults 35–65 years old [14]. The differences in the prevalence could be due to the differences in the target populations and the methods used for identifying prediabetes. Most studies used fasting blood glucose, but in our study, we used HbA1c to classify prediabetes, which is much more accurate than other methods [2].

Even though the prevalence of prediabetes was lower than that in other populations, several individual characteristics of the participants were associated with a seriously high risk of diabetes, and the next step was the development of prediabetes [1, 3]: 64.2% of the participants were overweight, 92.5% reported nonregular exercise, 24.8% smoked, 26.5% consumed alcohol, 29.2% had HT, and a large proportion had high levels of triglycerides, total cholesterol and LDL-C. These common profiles indicate that hill tribe people in Thailand are at high risk of diabetes and will require large medical expenditures in the future for care and case management [8].

Moreover, in our study, the prevalence did not differ according to sex, age, or tribe. Other studies reported that the prevalence of prediabetes was different between sexes [22], among different age categories [23], and among different tribes [24]. This indicates that the hill tribe people in Thailand share some common characteristics, particularly lifestyle and cooking practices, which include eating behaviors. The relationship between prediabetes and genetic variation among hill tribe people should be investigated.

However, Akha and Lisu people were found to have greater odds of having prediabetes than Karen people. Even though many general lifestyles and behaviors among the hill tribe people are similar, certain factors have some influence on prediabetes in these populations. For instance, alcohol use among Lisu men and women is common, but Karen women and Akha women do not use alcohol [10, 25, 26]. While smoking behavior is commonly found among Akha men and women, Lisu women and Karen women do not smoke [27]. Lisu and Akha cooking practices are similar to Chinese cooking practices, which involve oil, while Karen people use less oil in their daily cooking practice [28, 29]. Certain health-related behaviors and cooking practices can influence the development of prediabetes in these populations.

This study confirms the findings of previous studies that indicated that triglyceride and LDL-C levels were associated with prediabetes. The Canadian Diabetes Association reported that high LDL-C and triglyceride levels were risk factors for prediabetes and diabetes [30]. A study in Vietnam showed that high levels of triglycerides and LDL-C were associated with prediabetes [31]. Moreover, a study in Bangladesh clearly demonstrated that high levels of triglycerides and LDL-C were associated with prediabetes [32]. Some studies [28, 29] conducted among the hill tribes reported that a large proportion of the hill tribe people had high LDL-C levels and hypertriglyceridemia. Clearly, LDL-C and total cholesterol are related to each other [33], and the association between total cholesterol and prediabetes might be related to the impact of LDL-C. This study showed that a normal total cholesterol level was associated with prediabetes. This could be due to an interruption of LDL-C metabolism. Further investigation found that high total cholesterol levels (normally classified) had a strong correlation with increased LDL-C (r = 0.459, p value≤0.001). Therefore, the association between total cholesterol and prediabetes was a proxy association with LDL-C.

A few limitations were present in this study. First, some participants did not clearly understand Thai, which would impact their ability to answer the questions and understand the NPO instructions. However, we carefully monitored the data, while the village health volunteers helped these participants complete the questionnaire. Due to the nature of a cross-sectional study, some information was very difficult to obtain, such as a history of parental DM, because a large proportion of previous generations did not visit a hospital for health problems. Finally, only 11.2% of participants had prediabetes in the study, which might impact the ability of the statistical model to identify the potential factors associated with prediabetes.

Conclusion

Hill tribe people between 30 and 59 years old have low socioeconomic status and a high rate of prediabetes, which could progress to diabetes in the future. A large proportion had abnormal lipid profiles and exercised infrequently, which are risk factors for diabetes. More than half of the patients with prediabetes presented with HT, which is another key contributor to diabetes. The need to develop and implement public health interventions that focus on healthy food and dietary behaviors and participation in physical activity, especially regular exercise, to address prediabetes is urgent. Moreover, it is necessary to ensure that health education and essential health messages are delivered effectively to hill tribe people with a low education level. Abnormal lipid parameters relevant to health problems in this population should be further investigated.

Questionnaire used in the study (English).

(DOCX)

Click here for additional data file.

Data file for the study.

(XLSX)

Click here for additional data file.

16 Aug 2021

PONE-D-21-21401

Epidemiology of prediabetes mellitus among hill tribe adults in Thailand

PLOS ONE

Dear Dr. Apidechkul,

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.

I have received the reports from our advisors on your manuscript which you submitted to PLOS ONE.

Based on the comments received, I feel that your manuscript could be reconsidered for publication should you be prepared to incorporate major revisions.

When preparing your revised manuscript, you are asked to carefully consider the reviewer comments below and submit a list of responses to the comments.

Editor Comments: The paper should be checked by a professional speaker of English before complete acceptance.

Please submit your revised manuscript by Sep 30 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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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.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see:  http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at  https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Muhammad Sajid Hamid Akash

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.

When submitting your revision, we need you to address these additional requirements.

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2. Please include additional information regarding the survey or questionnaire used in the study and ensure that you have provided sufficient details that others could replicate the analyses. For instance, if you developed a questionnaire as part of this study and it is not under a copyright more restrictive than CC-BY, please include a copy, in both the original language and English, as Supporting Information.

Furthermore please provide additional information regarding how participants were recruited for the study, the recruitment date range (month and year) and a descriptions of where participants were recruited and where the research took place.

3. Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please move it to the Methods section and delete it from any other section. Please ensure that your ethics statement is included in your manuscript, as the ethics statement entered into the online submission form will not be published alongside your manuscript.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. 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 #1: Yes

**********

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

Reviewer #1: Yes

**********

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: 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

**********

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: add the duration time to study. add the detail of sampling technique of study, how to select study subjects. In statistical analysis, add the detail of method of multivariable logistic regression. Table2, select OR=1 at the first line of every variable, add the method of multivariable technique at the end of Table2. Check the format style and correction of the references.

**********

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

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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.

24 Sep 2021

Response to editor and reviewers’ comments

Dear Editor,

Thank you very much for the opportunity to submit the revised version. We have revised all points as editor comments in this version. The manuscript has been double checked by the American Journal Experts (AJE) with the reference No. No. 85D4-4E23-480D-5FA2-42C9.

Thank you,

TK

PONE-D-21-21401

Epidemiology of prediabetes mellitus among hill tribe adults in Thailand

PLOS ONE

Dear Dr. Apidechkul,

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.

I have received the reports from our advisors on your manuscript which you submitted to PLOS ONE.

Based on the comments received, I feel that your manuscript could be reconsidered for publication should you be prepared to incorporate major revisions.

When preparing your revised manuscript, you are asked to carefully consider the reviewer comments below and submit a list of responses to the comments.

Editor Comments: The paper should be checked by a professional speaker of English before complete acceptance.

: This manuscript has been double checked by the American Journal Experts (AJE) with No. 85D4-4E23-480D-5FA2-42C9.

Please submit your revised manuscript by Sep 30 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

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.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Muhammad Sajid Hamid Akash

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.

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

: Thank you, we have checked.

2. Please include additional information regarding the survey or questionnaire used in the study and ensure that you have provided sufficient details that others could replicate the analyses. For instance, if you developed a questionnaire as part of this study and it is not under a copyright more restrictive than CC-BY, please include a copy, in both the original language and English, as Supporting Information.

Furthermore please provide additional information regarding how participants were recruited for the study, the recruitment date range (month and year) and a descriptions of where participants were recruited and where the research took place.

: Questionnaire used has been uploaded.

3. Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please move it to the Methods section and delete it from any other section. Please ensure that your ethics statement is included in your manuscript, as the ethics statement entered into the online submission form will not be published alongside your manuscript.

: It’s moved in proper place.

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

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. 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 #1: Yes

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

Reviewer #1: Yes

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: 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

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: add the duration time to study. add the detail of sampling technique of study, how to select study subjects. In statistical analysis, add the detail of method of multivariable logistic regression. Table2, select OR=1 at the first line of every variable, add the method of multivariable technique at the end of Table2. Check the format style and correction of the references.

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

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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.

Thank you,

TK

Assistant Professor Dr.Tawatchai Apidechkul

Deputy Dean, School of Health Science, MFU

Director, Center of Excellence of the Hill tribe Health Research, WHO-CC

Former Hubert H Humphrey Fellow (2013-2014), Emory University

Global Health Delivery Intensive (Harvard School of Public Health)

Submitted filename: Response to editor and reviewersS.docx

Click here for additional data file.

19 Apr 2022

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

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.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Xi Pan

Academic Editor

PLOS ONE

[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: (No Response)

Reviewer #3: 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: Yes

Reviewer #3: Yes

**********

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

Reviewer #2: No

Reviewer #3: 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

Reviewer #3: Yes

**********

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

Reviewer #3: 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: Thank you for inviting me for this review. This is an interesting community-based cross-sectional study, and the first hand in-field data collection was valuable and provided some insights into the prevention and epidemiological understanding of prediabetes mellitus in Thailand. I hope my comments below are useful considerations.

1. Could you please clarify when the survey was conducted and time span of this study?

2. Statistical analysis section:

• Correcting “SPS program” with “SPSS program”? A typo maybe.

• Please clarify/specify your multivariable logistic model. The author mentioned that age and gender were the only two controlled confounders. Based on the results, it seems other variables were also included/controlled in the final multivariable model, while only the significant four variables were reported. Please provide a complete list of variables that you had used for this model. Based on the authors’ description of previous studies, it seems BMI, alcohol consumption and other socioeconomic factors all have impacts on the outcome to certain level. Therefore, please provide the rationale about your variable selection, in terms of model performance, data quality and clinical meanings.

• Any sampling weights applied in your data analysis since you have surveyed different geographical locations (different hill tribes)?

• In Table 2, could you please present the proportion of each factor for the patients with prediabetes mellitus and patients without prediabetes mellitus? Then we have a clear picture about how the demographical/socioeconomic and medical factors were distributed in each cohort, for instance, to see if the gender decomposition is similar in the patients with prediabetes mellitus versus the patients without prediabetes mellitus.

• It seems the two cohorts (the patients with prediabetes mellitus versus the patients without prediabetes mellitus) were not propensity score matched/ approximately matched cohorts. Please include this as a limitation/address the possible impacts for your model.

3. Please check the English writing grammar.

4. I noticed the author published a similar paper in 2018:

"Apidechkul T. Prevalence and factors associated with type 2 diabetes mellitus and hypertension among the hill tribe elderly populations in northern Thailand. BMC Public Health. 2018 Jun 5;18(1):694. doi: 10.1186/s12889-018-5607-2. PMID: 29871598; PMCID: PMC5989444."

Could please also provide participants’ selection flowchart in current study to see how the weighting sampling method could be applied if that is possible? The reason to do this is that I am wondering if any geographical impacts applied/pre-exists for the prevalence of prediabetes mellitus. After reading this similar work, I gained some background information in your study location and it seems the different hill tribe had various prevalence of type 2 diabetes mellitus and hypertension, it made me pondering if that case was also applied for prevalence of prediabetes mellitus.

Reviewer #3: This interesting study investigated the prevalence of prediabetes among subjects from six hill tribes in Thailand, which answered an important research question. The manuscript is generally well written and clearly presented. I only have a few comments for the authors to consider.

1. This study found that people having a normal total cholesterol level are more likely to have prediabetes than those having a high cholesterol level. This is controversial in the literature. Although the authors provided some discussion around this finding, I would like to recommend the authors add more discussion around the potential confounding and the relation between LDL-C, HDL-C, and total cholesterol. Please also consider adding a few references here.

2. I am wondering why only age and sex were adjusted as covariates in the multivariable analysis. Since there are six tribes included in this study and the authors mentioned each of the tribes has its own culture, it seems tribe is one of the confounders. Please consider adding some explanations or discussions.

3. In table 2, please consider providing all the odds ratios from multivariable analyses, even if some are not statistically significant.

**********

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

Reviewer #3: Yes: Junjie Ma

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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.

9 Jun 2022

Response to reviewers’ comments

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: Thank you for inviting me for this review. This is an interesting community-based cross-sectional study, and the first hand in-field data collection was valuable and provided some insights into the prevention and epidemiological understanding of prediabetes mellitus in Thailand. I hope my comments below are useful considerations.

1. Could you please clarify when the survey was conducted and time span of this study?

: Data were collected between November 2019 and March 2020; please see the abstract and methods sections (page 6, lines 16-17).

2. Statistical analysis section:

• Correcting “SPS program” with “SPSS program”? A typo maybe.

: Thank you, it has been corrected.

• Please clarify/specify your multivariable logistic model. The author mentioned that age and gender were the only two controlled confounders. Based on the results, it seems other variables were also included/controlled in the final multivariable model, while only the significant four variables were reported. Please provide a complete list of variables that you had used for this model. Based on the authors’ description of previous studies, it seems BMI, alcohol consumption and other socioeconomic factors all have impacts on the outcome to certain level. Therefore, please provide the rationale about your variable selection, in terms of model performance, data quality and clinical meanings.

: We started with the univariate analysis by having one independent variable and dependent variable and set the significance threshold at �  =0.05, and any variable with a p value equal to or less than 0.05 was considered significant. This step of the analysis was repeated until all independent variables were completed. Afterward, all independent variables were added to the model with the dependent variable, and the significance was assessed. The least significant variable (with the greatest p value) was removed from the model, and the Hosmer Lemeshow Chi-square test was used to assess the goodness of fit (nonsignificant). The process of testing the model was repeated by removing all nonsignificant variables in the model and showing that the Hosmer Lemeshow Chi-square test result was nonsignificant, which was the final model. However, before interpretation, age and sex were adjusted in the model to control their effects as confounding factors.

Of course, with the conditions (both exposures (independent variables) and outcomes (disease) examined at the same time) of the cross-sectional study used in this project, which is intended to assess the prevalence and predict the factors associated with the outcome, the association detected might not be fully accurate, similar to other stronger study designs that focus only on testing the association.

During the analysis, we examined all independent variables with the outcome (pre-DM) and retained some variables as the best predictors in the model before making the interpretation.

We have added the essential information to the statistical analysis section, page 6 line 32; and page 7 lines 1-3.

• Any sampling weights applied in your data analysis since you have surveyed different geographical locations (different hill tribes)?

: Thank you for the great comment. We used five villages from each tribe to select the participants for the study. All people who met the criteria and lived in one of the five selected villages for each tribe were invited to participate in the study. Finally, with the number of people living in each village, the proportion of participants from each tribe was still reflected in the study population:

Karen 16%, Hmong 14%, Yao 13%, Akha 29%, Lahu 16.8%, and Lisu 10.5%.

Even the actual population in the six different tribes are a bit different: in 2019, there were 749 hill tribe villages in Chiang Rai Province, Thailand, which included 316 Lahu villages (51,339 persons (26.5%)), 243 Akha villages (74,403 persons (38.5%)), 63 Yao villages (16,227 persons (8.4%)), 56 Hmong villages (33,478 persons (17.0%)), 36 Karen villages (7,933 persons (4%)), and 35 Lisu villages (9,632 persons (4.9%)) [12]. Page 3, lines 20-23.

: We have carefully considered this excellent point and found that the statistics shown in Table 2, particularly the CIs, had high power, which means that the sample size was large enough for testing the hypothesis and that the proportions could well reflect the different sizes of the populations of each tribe. However, during sample size calculation, we did not consider this idea, and it will be used in our next project to ensure that the final model can accurately reflect the hill tribe population. Thank you so much.

• In Table 2, could you please present the proportion of each factor for the patients with prediabetes mellitus and patients without prediabetes mellitus? Then we have a clear picture about how the demographical/socioeconomic and medical factors were distributed in each cohort, for instance, to see if the gender decomposition is similar in the patients with prediabetes mellitus versus the patients without prediabetes mellitus.

: Yes, please see columns 2 and 3, which have been changed to the % column. Then, we can see the proportion distribution in each factor between those who had pre-DM and those who did not.

• It seems the two cohorts (the patients with prediabetes mellitus versus the patients without prediabetes mellitus) were not propensity score matched/ approximately matched cohorts. Please include this as a limitation/address the possible impacts for your model.

: Thank you. We completely agree with you and have included this as one of the key limitations of the study.

3. Please check the English writing grammar.

: Thank you. The English has been checked by American Journal Experts (AJE) with reference no. 85D4-4E23-480D-5FA2-42C9 .

4. I noticed the author published a similar paper in 2018:

"Apidechkul T. Prevalence and factors associated with type 2 diabetes mellitus and hypertension among the hill tribe elderly populations in northern Thailand. BMC Public Health. 2018 Jun 5;18(1):694. doi: 10.1186/s12889-018-5607-2. PMID: 29871598; PMCID: PMC5989444."

: The two projects are different. The first project you mentioned, which was published previously in BMC Public Health, was designed to identify factors that contributed to DM and HT in the elderly population. In current project, however, we focused on people between 30 and 59 years old. The current project was performed after our first project was completed, and we extended our ideas in the second project. The first project was supported by the National Research Council of Thailand, while the second project was supported by the Health System Research Institute, Thailand (Grant No 61-027). Therefore, the two papers are different.

Could please also provide participants’ selection flowchart in current study to see how the weighting sampling method could be applied if that is possible? The reason to do this is that I am wondering if any geographical impacts applied/pre-exists for the prevalence of prediabetes mellitus. After reading this similar work, I gained some background information in your study location and it seems the different hill tribe had various prevalence of type 2 diabetes mellitus and hypertension, it made me pondering if that case was also applied for prevalence of prediabetes mellitus.

: Thank you for the comment. Please see Figure 1 on page 4.

Reviewer #3: This interesting study investigated the prevalence of prediabetes among subjects from six hill tribes in Thailand, which answered an important research question. The manuscript is generally well written and clearly presented. I only have a few comments for the authors to consider.

1. This study found that people having a normal total cholesterol level are more likely to have prediabetes than those having a high cholesterol level. This is controversial in the literature. Although the authors provided some discussion around this finding, I would like to recommend the authors add more discussion around the potential confounding and the relation between LDL-C, HDL-C, and total cholesterol. Please also consider adding a few references here.

: Thank you for the great comment. We have added information including references in this section; please see page 16, lines 12-16.

2. I am wondering why only age and sex were adjusted as covariates in the multivariable analysis. Since there are six tribes included in this study and the authors mentioned each of the tribes has its own culture, it seems tribe is one of the confounders. Please consider adding some explanations or discussions.

: Thank you for the great comment. From our literature review, we found that age and sex were major confounding factors in predicting diabetes and prediabetes.

: We also reanalyzed entire steps and used the Karen tribe as the reference (because Karen had the lowest prevalence) and found that two tribes, namely, Akha and Lisu, had significantly greater odds of developing pre-DM. Thank you so much for noting this. We have added this information to the discussion section as well.

3. In table 2, please consider providing all the odds ratios from multivariable analyses, even if some are not statistically significant.

: We have added all ORs in the multivariate model; please see table 2.

Thank you so much.

TK

Assist Prof. Dr. Tawatchai Apidechkul, MSc (Infectious Epidemiology), Dr. P. H (Epidemiology)

Dean, School of Health Science, Mae Fah Luang University

Director, Center of Excellence of Hill Tribe Health Research, WHO-CC

Former Hubert H Humphrey Fellow (2013-2014), Emory University

Global Health Delivery Intensive (Harvard School of Public Health)

Submitted filename: Response to reviewers .docx

Click here for additional data file.

11 Jul 2022

Epidemiology of prediabetes mellitus among hill tribe adults in Thailand

PONE-D-21-21401R2

Dear Dr. Apidechkul,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Xi Pan

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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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 #2: Yes

Reviewer #3: Yes

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

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #2: Yes

Reviewer #3: Yes

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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: I would like to thank the authors for addressing my initial comments. The authors have provided a nicely detailed and thorough response to the comments from the previous review and have addressed my major concerns regarding the survey administration and statistical analysis plan. Following the revision to the article, the paper had been sufficiently improved.

Reviewer #3: All my comments have been addressed in this version. This interesting study will contribute the research area of diabetes.

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Reviewer #2: No

Reviewer #3: Yes: Junjie Ma

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14 Jul 2022

PONE-D-21-21401R2

Epidemiology of prediabetes mellitus among hill tribe adults in Thailand

Dear Dr. Apidechkul:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Xi Pan

Academic Editor

PLOS ONE