Insulin resistance measured by High Homeostasis Model Assessment (HOMA) as a Predictor for new onset Diabetes mellitus and diabetic Complications in Non-Diabetic Adults: A KoGES prospective Cohort Study

doi:10.21203/rs.3.rs-2220340/v1

Aims: Homeostasis model assessment for insulin resistance (HOMA-IR) is a biomarker for Type 2 diabetes mellitus(T2DM). However, the role of HOMA-IR in the non-diabetic is unclear.

Methods: From a prospective community-based cohort of 10,030 individuals, 4,314 individuals younger than 65 years and without diabetes were enrolled and divided into three groups by baseline HOMA-IR tertiles: high (n=1244), moderate (n=1248), and low (n=1822). The primary outcome was new onset T2DM. Secondary outcomes were chronic kidney disease (CKD) and a composite of coronary artery disease, myocardial infarction, and stroke as macrovascular events.

Results:The mean age was 51 years. The prevalence of hypertension and cholesterol and HbA1c were higher in the high HOMA-IR group. New onset T2DM (6.3%) and CKD (12.2%) incidence in the high HOMA-IR group was higher than in the others. The prevalence of macrovascular events did not differ among groups. High-HOMA-IR was an independent risk factor for new onset T2DM (odds ratio 2.07 [1.32-3.25]; p =0.001) and CKD (1.42 [1.08-1.87]; p =0.01).

Conclusions:High HOMA-IR was an early predictor of new onset T2DM and CKD regardless of the HbA1c in non-diabetic individuals. Further research on the specific cut off value will be needed.

insulin resistance

diabetes mellitus

chronic kidney disease

HOMA-IR

Diabetes mellitus (DM) and its complications are increasing, and the related medical cost is becoming a socio-economic burden worldwide [1]. DM is also closely related to other metabolic diseases, such as dyslipidemia and fatty liver [2]. Current treatments target individual metabolic diseases, such as hypertension, dyslipidemia, and diabetes, and are focused on maintenance therapy that prevents disease deterioration rather than preventing and managing the root of the disease. Therefore, despite the high medical costs, the prevalence of metabolic diseases and the associated mortality rates are increasing. To prevent diabetes and its complications, it is important to detect high risk population and prevent the disease onset in the early and reversible phase.

Insulin resistance (IR) is a state of impaired biological response to normal circulating levels of insulin that represents an early pathophysiology of diabetes progression and is associated with micro and macrovascular diseases, as reported by cross-sectional epidemiologic studies [3–6]. However, it is not yet clear whether IR is a consequence of diabetes and diabetic complication or a factor leading to it. In this study, we investigated whether IR measured using the homeostatic model assessment for insulin resistance (HOMA-IR) value is associated with new onset diabetes as well as vascular disease and can be used as a early predictor for diabetes and vascular diseases in non-diabetic participants from a large prospective community-based cohort.

Study populations

Data were collected from the Ansan (urban) and Ansung (rural) prospective community-based cohort studies. These studies are part of the Korean Health and Genome Study (KoGES), which is conducted by the Korea Centers for Disease Control and Prevention (Republic of Korea) as a government-funded epidemiological survey to investigate trends in chronic non-communicable diseases and their associated risk factors. From June 2001 to January 2003, adults aged 40 to 69 years residing in Ansan and Ansung were enrolled. The cohort included 10,030 adults (5,018 from Ansung and 5,020 from Ansan) in total who underwent health examination at the Korea University Ansan Hospital and Ajou University Medical Center [7]. The distributions of age and gender were similar to those in the general population. Surveys were conducted every two years through clinical examinations and questionnaires, and a total of six follow-ups were conducted until 2014.

Our study only included participants from the cohort who were under the age of 65 and did not have diabetes. The following were exclusion criteria: age over 65 years at first visit, HbA1c test performed only once at first visit, unavailable HOMA-IR value or diagnosis of diabetes at baseline as follows: 1) HbA1c ≥ 6.5% [8], 2) taking DM medication at the time of the first visit.

Covariates

All covariates were based on the time of the first visit and included clinical data and biochemical data. Clinical data, such as age, gender, smoking status, hypertension, dyslipidemia, previous myocardial infarction (MI), previous heart failure, and previous chronic kidney disease (CKD) data, were obtained using standardized questionnaires by a trained interviewer. Biochemical data, including data regarding HbA1c, fasting blood glucose and insulin, lipid profile, and biomarkers reflecting systemic inflammatory status (high sensitivity C-reactive protein), were also obtained, as previously described [9]. Blood samples were obtained after an overnight fast of at least 8 h, and HbA1c levels were measured using high-performance liquid chromatography (Variant II; BioRad Laboratories, Hercules, CA, USA). The glomerular filtration rate (GFR) was calculated using the Modification of Diet in Renal Disease equation at each visit [10]. Body mass index (BMI) was calculated as weight divided by height squared (kg/m²).

The fasting insulin and glucose values were used to calculate the values for HOMA-IR, homeostasis model of assessment–β-cell (HOMA–β-cell), and Quantitative insulin sensitivity check index (QUICKI) [11, 12]. The subjects were divided into three groups by HOMA-IR value tertiles; the first tertile was 1.365, and the second tertile was 1.834.

Outcome definition

The primary outcome was new onset DM. New onset DM was diagnosed based on A1C criteria (HbA1C ≥ 6.5%) during follow up. Secondary outcomes were defined as CKD and macrovascular events. CKD was defined as a creatine clearance rate of < 60 mL/min/1.73 m². Subjects with CKD at baseline were excluded from the survival analysis. A macrovascular event was defined as a composite of coronary artery disease, MI, and ischemic stroke reported through a questionnaire [3].

Statistical analysis

All statistical analyses were performed using the SPSS (version 24.0; IBM Corp., Armonk, NY, USA) and R (version 3.1.10; the R Foundation for Statistical Computing, Vienna, Austria) software. Categorical variables are presented as frequencies with percentages and were compared between groups using the chi-square test or Fisher’s exact test. Continuous variables are presented as either mean (± standard deviation) and were compared between groups using one-way analysis of variance.

The cumulative incidences of primary outcomes were compared between groups using the Kaplan-Meier method with the log rank test. The odds ratios (ORs) and confidence intervals (CIs) for primary and secondary outcomes according to HOMA-IR groups were estimated using multivariable logistic regression analysis after adjustment of variables. Model 1 adjusted clinical factors such as age, sex, current smoking, hypertension, dyslipidemia, MI, heart failure, CKD, and HbA1c. Model 2 additionally adjusted laboratory variables such as high-density lipoprotein (HDL)-cholesterol, Low-density lipoprotein (LDL)-cholesterol, C-reactive protein, HOMA-β-cell, GFR, and BMI. Statistical significance was considered at p-value < 0.05.

Ethical considerations

The Institutional Review Board of Gwangju Institute of Science and Technology (South Korea) approved the study protocol (IRB No. 20200414-EX-01-02). All research procedures were performed in accordance with the relevant guidelines and regulations. All participants volunteered for the Ansan and Ansung studies and provided written informed consent.

Baseline characteristics

Among the 10,030 individuals in the study cohort, 1,784 individuals who only tested for HbA1c once were excluded. In addition, participants were excluded if they were > 65 years old at baseline (n = 983) or diagnosed with DM at baseline (n = 801) and without an HOMA-IR value (n = 2148). Finally, 4,314 individuals were enrolled in this study (Fig. 1). The participants were divided into low (n = 1822), moderate (n = 1248), and high (n = 1244) HOMA-IR groups based on HOMA-IR value tertiles.

The subjects were followed for a median interval of 9.9 years (interquartile range: 9.1–10.0 years). Clinical and biochemical baseline characteristics of the HOMA-IR groups are presented in Table 1. The mean age was 51 years in all HOMA-IR group, and the proportions of men in the low, moderate, and high HOMA-IR groups were 54.5%, 47.4%, and 45.4%, respectively. The prevalence of hypertension was higher in the high HOMA-IR group (17.4%) than those in the moderate (11.8%) and low (8.7%) HOMA-IR groups, but the prevalence of dyslipidemia, previous MI, previous heart failure, and CKD were not significantly different among the HOMA-IR groups.

Table 1

Clinical and laboratory characteristics of the study cohort.
HOMA-IR group
	Low (n = 1822)	Moderate (n = 1248)	High (n = 1244)	p	p’	p’’
Age (years)	51.3 ± 7.8	50.8 ± 7.6	50.5 ± 7.6	0.03	0.03	1.00
Men	993 (54.5)	591 (47.4)	565 (45.4)	< 0.001	< 0.001	0.34
Current smoking	551 (30.7)	285 (23.1)	266 (21.7)	< 0.001	< 0.001	0.41
Hypertension	158 (8.7)	147 (11.8)	216 (17.4)	< 0.001	< 0.001	< 0.001
Dyslipidemia	30 (1.6)	21 (1.7)	33 (2.7)	0.10	0.07	0.10
Previous MI	8 (0.4)	14 (1.1)	8 (0.6)	0.08	0.45	0.28
Previous Heart failure	3 (0.2)	3 (0.2)	1 (0.1)	0.61	0.65	0.63
Previous CKD	34 (1.9)	38 (3.0)	38 (3.1)	0.05	0.04	0.99
Laboratory variables
Total cholesterol (mg/dL)	194 ± 34	197 ± 35	203 ± 36	< 0.001	< 0.001	< 0.001
Triglyceride (mg/dL)	129 ± 86	142 ± 93	169 ± 118	< 0.001	< 0.001	< 0.001
HDL-cholesterol (mg/dL)	52 ± 12	50 ± 12	47 ± 10	< 0.001	< 0.001	< 0.001
LDL-cholesterol (mg/dL)^a	116 ± 33	119 ± 32	122 ± 34	< 0.001	< 0.001	0.03
HbA1c (%)	5.5 ± 0.3	5.5 ± 0.4	5.6 ± 0.4	< 0.001	< 0.001	< 0.001
HbA1c (mmol/mol)	37 ± 1.5	37 ± 1.6	38 ± 1.6	< 0.001	< 0.001	< 0.001
CRP (mg/L)	0.21 ± 0.49	0.24 ± 0.89	0.22 ± 0.26	0.39	1.00	1.00
Fasting glucose (mg/dL)	85 ± 8	87 ± 8	91 ± 9	< 0.001	< 0.001	< 0.001
Fasting insulin (uU/mL)	5.1 ± 2.0	7.4 ± 2.1	11.2 ± 6.9	< 0.001	< 0.001	< 0.001
HOMA-IR^c	1.09 ± 0.44	1.61 ± 0.46	2.53 ± 1.53	< 0.001	< 0.001	< 0.001
HOMA-β-cell	91 ± 32	106 ± 34	124 ± 49	< 0.001	< 0.001	< 0.001
QUICKI	0.37 ± 0.01	0.35 ± 0.01	0.34 ± 0.01	< 0.001	< 0.001	< 0.001
GFR (mL/min/1.73 m²)	96 ± 20	95 ± 21	94 ± 21	0.16	0.18	1.00
BMI (kg/m²)	23.4 ± 2.8	24.5 ± 2.8	25.9 ± 2.9	< 0.001	< 0.001	< 0.001
† Values are presented as number (%) or mean ± SD. p’ is for the comparison between low and high HOMA-IR groups. p’’ is for the comparison between moderate and high HOMA-IR groups.
‡ Abbreviations: BMI, body mass index; CAD, coronary artery disease; CKD, chronic kidney disease; CRP, C-reactive protein; GFR, glomerular filtration rate; HbA1c, glycated hemoglobin; HDL, high-density lipoprotein, HOMA-IR, homeostasis model assessment for insulin resistance; MI, myocardial infarction; LDL, low-density lipoprotein; QUICKI, quantitative insulin sensitivity check index.

The mean values of HOMA-IR were 1.09, 1.61, and 2.53 and those of HOMA-β-cell were 91, 106, and 124, respectively, in the low, moderate, and high groups. The high HOMA-IR group had a higher mean HbA1c, total cholesterol, triglyceride, and LDL-cholesterol and lower HDL-cholesterol than the other groups, although all values were within normal ranges. In contrast, C-reactive protein and baseline GFR values were not different among the three groups.

Primary outcomes for the HOMA-IR groups

The primary outcome was defined as new onset DM, and the secondary outcomes were defined as CKD and macrovascular events. New onset DM was observed in a total of 168 participants, and more participants were newly diagnosed with diabetes in the high HOMA-IR group (n = 78, 6.3%) than in the other groups (p-value < 0.001). CKD was observed in a total of 425 participants, and more participants were diagnosed in the high HOMA-IR group (n = 149, 12.2%) than in the other groups (p-value = 0.004). On the contrary, macrovascular events, including coronary artery disease, MI, and ischemic stroke, were observed in a total of 103 participants, and there was no significant difference in the incidence of macrovascular events among the HOMA-IR groups (Table 2, Fig. 2).

Table 2

Primary and secondary outcomes according to HOMA-IR groups.
HOMA-IR groups
	Low (n = 1822)	Moderate (n = 1248)	High (n = 1244)	p-value	p’	p’’
New onset Diabetes	54 (3.0)	32 (2.6)	78 (6.3)	< 0.001	< 0.001	< 0.001
Chronic kidney disease	158 (8.8)	118 (9.6)	149 (12.2)	0.004	0.001	0.02
Macrovascular events	49 (2.7)	21 (1.7)	33 (2.7)	0.14	0.94	0.09
Coronary artery disease or MI	31 (1.7)	14 (1.1)	19 (1.5)	0.27	0.47	0.49
Ischemic stroke	17 (0.9)	7 (0.6)	14 (1.1)	0.32	0.74	0.14
† Values are presented as number (%), p-values were calculated using Kaplan-Meier survival analysis with the log rank test. p’ is for the comparison between low and high HOMA-IR groups. p’’ is for the comparison between moderate and high HOMA-IR groups.

HOMA-IR as an independent predictor of new onset diabetes and chronic kidney disease

Table 3 shows the odds ratio (OR) for primary and secondary outcomes in the HOMA-IR groups. After adjusting for clinical risk factors in model 1, high HOMA-IR was found to be a significant risk factor for new onset DM (OR: 1.55, 95% CI: 1.05–2.27, p-value = 0.03). Additionally, after adjusting for baseline laboratory variables and BMI in model 2, high HOMA-IR was still found to be a significant risk factor for the development of DM (OR: 2.07, 95% CI: 1.32–3.25, p-value = 0.001). The baseline HbA1c level was also a significant risk factor for new onset diabetes. The results for CKD were similar to those for new onset diabetes. In model 1, the OR for newly diagnosed CKD in the high HOMA-IR group was 1.36 (95% CI: 1.06–1.74, p-value = 0.02) and in model 2, the OR for newly diagnosed CKD in the high HOMA-IR group was 1.42 (95% CI: 1.08–1.87, p-value = 0.01). In contrast, the HbA1c level was not a risk factor for the development of CKD. There was no significant difference in ORs for high HOMA-IR and HbA1c values for macrovascular events in both models. BMI, as an indicator of metabolic disease, is also a significant risk factor for new onset DM but not for vascular events (Supplement Table S1).

Table 3

Multivariate analysis of primary and secondary outcomes in the HOMA-IR groups.
	Model 1		Model 2
	Odds ratio (95% CI)	p-value	Odds ratio (95% CI)	p-value
New onset DM
HbA1c (%)	29.8 (18.2–48.9)	< 0.001	28.9 (16.9–49.8)	< 0.001
Low HOMA-IR	Reference		Reference
Moderate HOMA-IR	0.81 (0.51–1.29)	0.37	1.02 (0.62–1.68)	0.93
High HOMA-IR	1.55 (1.05–2.27)	0.03	2.07 (1.32–3.25)	0.001
Chronic kidney disease
HbA1c (%)	1.15 (0.86–1.56)	0.35	1.09 (0.79–1.51)	0.59
Moderate HOMA-IR	1.04 (0.79–1.35)	0.79	0.97 (0.73–1.30)	0.85
High HOMA-IR	1.36 (1.06–1.74)	0.02	1.42 (1.08–1.87)	0.01
Macrovascular event
HbA1c (%)	1.40 (0.79–2.49)	0.25	1.27 (0.68–2.37)	0.45
Moderate HOMA-IR	0.63 (0.38–1.07)	0.09	0.64 (0.36–1.12)	0.11
High HOMA-IR	0.97 (0.61–1.55)	0.90	1.09 (0.64–1.86)	0.74
Model 1, adjusted for HbA1c, age, sex, current smoking, hypertension, dyslipidemia, myocardial infarction, heart failure, chronic kidney disease; Model 2, adjusted for factors in model 1 as well as high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, C-reactive protein, homeostasis model assessment of β-cell function, glomerular filtration rate, and body mass index.

Changes in HOMA-IR and HOMA-β-cell values between the baseline and last visit were calculated ((Supplement Figure S1). The HOMA-IR value was significantly increased in the low and moderate HOMA-IR groups compared to that in the high HOMA-IR group. HOMA-β-cell decreased over time, regardless of the group. The decrease in HOMA-β-cell was significantly higher in the high HOMA-IR group than in the low and moderate HOMA-IR groups.

Analysis of a non-diabetic middle-aged population from a large prospective community-based cohort with a long term follow up revealed a significantly higher prevalence of new onset diabetes and CKD in the high HOMA-IR group than in the other groups. Baseline high HOMA-IR was an independent risk factor for both new onset diabetes and CKD regardless of the HbA1c level. However, there was no association between high HOMA-IR and macrovascular events.

Many previous studies have shown the relationship between IR and diabetes [3, 4, 6]. IR refers to reduced responsiveness to insulin in tissues that take up glucose, such as liver, skeletal muscle, and adipose tissue [13, 14]. In compensation for IR, the synthesis of insulin in β cells increases and hyperinsulinemia occurs, leading to impaired glucose disposal [14]. Type 2 diabetes mellitus (T2DM) is induced when there is a combination of insufficiencies in β cell mass and function to meet the demands of IR. A high serum glucose level inhibits the proliferation and de-differentiation of β cells through a process called “glucotoxicity,” which gradually leads to reduced insulin secretion [15]. In the high HOMA-IR group, the HOMA-β-cell value for β cell function decreased more than that in the other groups in our study. Given that individuals with T2DM have a β cell mass and function occasionally preserved within normal range in the early period of T2DM progression, β cell mass and function insufficiencies were relative rather than absolute. Although an insufficient β cell mass is essential for the development of T2DM [16], it is difficult to accurately measure β cell mass in living people, and insulin secretion capacities have a wide range; therefore, β cell mass has limited use as a biomarker for new onset diabetes.

In contrast, IR is commonly observed in most T2DM patients and in individuals with impaired glucose tolerance. IR is reportedly the strongest predictor of T2DM, and diabetes can be prevented by improving IR [5]. IR begins from the very early stage of diabetes and can thus be used as an early biomarker to estimate the risk of new onset diabetes. A recent Saku study [4] assessed IR and diabetes in 2,209 non-diabetic patients. Changes in HOMA-IR were measured in the non-diabetic patients and showed that the incidence of T2DM was high when the changes in HOMA-IR were moderate or high. The Saku study also showed that IR had a strong impact on the development of diabetes. This finding is line with our study. Our study showed a significant relationship between high HOMA-IR and new onset diabetes even in non-diabetic patients from a larger prospective community-based cohort with ten years follow up.

BMI is a metabolic disease parameter and is associated with IR and diabetes [17]. In our study, BMI was found to be a significant risk factor for new onset DM but not for vascular events. Because BMI is calculated based only on height and weight, it does not seem to represent metabolic status more sensitively than other parameters, such as visceral fat and waist-to-hip ratio [18–20]. Vascular disease is also directly affected by factors other than metabolic disease, such as high blood pressure.

Another important finding of the present study was that high HOMA-IR value was found to be an independent risk factor for CKD among non-diabetic individuals, whereas HbA1c was not. Various studies have been conducted on the relationship between CKD and IR [21–24]. CKD, itself characterized by a low-grade inflammatory state, can cause IR and vice versa. CKD and IR adversely affect each other, accelerating the deterioration of renal function [22]. There are several mechanisms that have been suggested to underly the relationship between CKD and IR, one of which is hyperinsulinemia, which increases oxidative stress, protein glycosylation oxidation, and lipid peroxidation [5]. Hyperinsulinemia causes glomerular hyperfiltration, endothelial dysfunction, and increased vascular permeability. IR with oxidative stress and inflammation, is thought to play roles in microalbuminuria development and kidney function impairment [5]. In addition to hyperinsulinemia, inappropriate activation of the renin-angiotensin-aldosterone system may cause renal insufficiency [25]. Eventually, IR can lead to glomerulosclerosis and tubulointerstitial injury. A 3-year prospective cohort study with 7,200 patients showed that the incidence of CKD was and rate of decrease of eGFR were higher in the high HOMA-IR group with metabolic syndrome [26]. Our study also showed that high HOMA-IR was an independent risk factor for CKD after adjusting for multiple risk factors, including HbA1c and baseline GFR. HbA1c was not a risk factor for CKD in our study. This might be due to the HbA1c value within the normal range in our study, unlike the 7% HbA1c standard value for predicting microvascular complications in the UKPDS study [25].

The incidence of macrovascular events did not differ between HOMA-IR groups. Some studies have reported a relationship between IR and cardiovascular events. However, it is difficult to directly compare these with our study, since most of these previous studies have a cross-sectional design and involved a few participants or participants who already had atherosclerosis identified as a high-risk factor [21, 26–28]. The clinical significance of IR for cardiovascular disease may more likely be as a factor accelerating disease progression in patients with certain risk factors, such as CKD, rather than as an independent risk factor [21, 28]. Another issue with our study was that it focused on a population that was relatively young and healthy; very low risk of cardiovascular events. Major vascular complications begin to develop about 10 years after diabetes diagnosis. The cohort in present study is still ongoing; thus, we hope to see very long-term cardiovascular events rate and influence of baseline high HOMA-IR value.

There are several limitations to this study. First, IR was evaluated using only HOMA-IR. The gold standard for evaluating IR is the hyperinsulinemia-euglycemic glucose clamp technique [11], but it is clinically difficult to implement and even more difficult to apply in large-scale cohort studies. In contrast, HOMA-IR is widely used to measure IR and has yielded reliable results in many studies [27, 28]. Second, clinical data were obtained through standardized questionnaires by a trained interviewer. However, the incidence of macrovascular events in this relatively healthy cohort was lower than that among people with diabetes. Large cohort studies routinely use standardized questionnaires, and the incidence of macrovascular events in our Korean cohort was similar to that in other ethnic groups without diabetes [29]. Third, the absence of data regarding other microvascular events, such as retinopathy, could be a limitation, although the expected incidences of end-stage DM-related microvascular events in our cohort are very low, as the participants did not have diabetes at baseline. It is difficult to infer the effect of specific medication on clinical events, because of the lack of medication data. As mentioned above, participants included in this analysis had a very low incidence of risk factors; thus, the effect of drugs in our cohort is expected to be insignificant.

In conclusion, our findings indicate that baseline high HOMA-IR has a significant relationship with the development of T2DM and CKD and is an independent risk factor for both new onset T2DM and CKD, regardless of the HbA1c level in a healthy middle-age population. However, we found no association between high HOMA-IR and macrovascular events. This suggests HOMA-IR measurement can be used as a biomarker to identify people at risk for T2DM development. Further studies are needed to define a HOMA-IR cut off value as a new onset T2DM prediction marker and determine its association with macrovascular disease through long term follow up.

BMI, body mass index; CAD, coronary artery disease; CKD, chronic kidney disease; CRP, C-reactive protein; GFR, glomerular filtration rate; HbA1c, glycated hemoglobin; HDL, high-density lipoprotein, HOMA-IR, homeostasis model assessment for insulin resistance; MI, myocardial infarction; LDL, low-density lipoprotein; QUICKI, quantitative insulin sensitivity check index; T2DM, type 2 diabetes mellitus; UKPDS, the UK Prospective Diabetes Study

Ethical Approval and Consent to participate

All participants volunteered for the Ansan and Ansung studies and provided written informed consent. The study protocol was approved by the institutional review board of the Gwangju Institute of Science and Technology (IRB No. 20200414-EX-01-02). All the research procedures were performed in accordance with guidelines and regulations.

Consent for publication

I, the undersigned, give my consent for the publication of identifiable details within the text to be published in the Clinical Diabetes and endocrinology

Availability of supporting data

The corresponding author, Ji-Young Jang, may provide the data that back up the conclusions of this study upon request.

Competing interests

The authors declare no conflict of interest.

Funding

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), which is funded by the Ministry of Education (2016R1A6A3A04010466, 2020R1C1C1004999 to C.M.O.). No funder/sponsor had any role in the design and conduct of the study.

Authors’ contributions

Ji-Yong Jang, MD had full access to all data in the study and take responsibility for data integrity and data analysis accuracy. Jibeom Lee and MH Kim were equally responsible for drafting of the manuscript. All authors have participated in editing the manuscript and approved the final manuscript.

Acknowledgements

Not applicable

Whiting DR, Guariguata L, Weil C, Shaw J. IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract; 2011; 94: 311-321.
Calanna S, Scicali R, Di Pino A, Knop FK, Piro S, Rabuazzo AM, Purrello F. Lipid and liver abnormalities in haemoglobin A1c-defined prediabetes and type 2 diabetes. Nutr Metab Cardiovasc Dis; 2014; 24: 670-676.
Tang Q, Li X, Song P, Xu L. Optimal cut-off values for the homeostasis model assessment of insulin resistance (HOMA-IR) and pre-diabetes screening: Developments in research and prospects for the future. Drug Discov Ther; 2015; 9: 380-385.
Morimoto A, Tatsumi Y, Soyano F, Miyamatsu N, Sonoda N, Godai K, Ohno Y, Noda M, Deura K. Increase in homeostasis model assessment of insulin resistance (HOMA-IR) had a strong impact on the development of type 2 diabetes in Japanese individuals with impaired insulin secretion: the Saku study. PLoS One; 2014; 9: e105827.
Chen J, Muntner P, Hamm LL, Fonseca V, Batuman V, Whelton PK, He J. Insulin resistance and risk of chronic kidney disease in nondiabetic US adults. J Am Soc Nephrol; 2003; 14: 469-477.
Baek JH, Kim H, Kim KY, Jung J. Insulin Resistance and the Risk of Diabetes and Dysglycemia in Korean General Adult Population. Diabetes Metab J; 2018; 42: 296-307.
Kim Y, Han BG, Ko GESg. Cohort Profile: The Korean Genome and Epidemiology Study (KoGES) Consortium. Int J Epidemiol; 2017; 46: e20.
Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes. Diabetes Care 42(Suppl. 1): S13-28
Lim S, Jang HC, Lee HK, Kim KC, Park C, Cho NH. A rural-urban comparison of the characteristics of the metabolic syndrome by gender in Korea: the Korean Health and Genome Study (KHGS). J. Endocrinol. Invest; 2016; 29: 313-319.
The relationship of glycemic exposure (HbA1c) to the risk of development and progression of retinopathy in the diabetes control and complications trial. Diabetes; 1995; 44(8): 968-983.
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia; 1985; 28: 412-419.
Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, Quon MJ. Quantitative Insulin Sensitivity Check Index: A Simple, Accurate Method for Assessing Insulin Sensitivity In Humans. The Journal of Clinical Endocrinology & Metabolism; 2000; 85(7): 2402-2410.
Kosmas CE, Silverio D, Tsomidou C, Salcedo MD, Montan PD, Guzman E. The Impact of Insulin Resistance and Chronic Kidney Disease on Inflammation and Cardiovascular Disease. Clin Med Insights Endocrinol Diabetes; 2018; 11: 1179551418792257.
Freeman AM, Pennings N. Insulin Resistance. StatPearls Treasure Island (FL), 2019.
Weir GC, Bonner Weir S. Five stages of evolving beta-cell dysfunction during progression to diabetes. Diabetes; 2004; 53(suppl 3): S16-21.
Weir GC, Gaglia J, Bonner-Weir S. Inadequate β-cell mass is essential for the pathogenesis of type 2 diabetes. The Lancet Diabetes & Endocrinology; 2020; 8: 249-256.
Diniz M, Beleigoli AMR, Schmidt MI, Duncan BB, Ribeiro ALP, Vidigal PG, Bensenor IM, Lotufo PA, Santos IS, Griep RH, Barreto SM. Homeostasis model assessment of insulin resistance (HOMA-IR) and metabolic syndrome at baseline of a multicentric Brazilian cohort: ELSA-Brasil study. Cad Saude Publica; 2020; 36: e00072120.
Scicali R, Rosenbaum D, Di Pino A, Giral P, Cluzel P, Redheuil A, Piro S, Rabuazzo AM, Purrello F, Bruckert E, Gallo A. An increased waist-to-hip ratio is a key determinant of atherosclerotic burden in overweight subjects. Acta Diabetol; 2018; 55: 741-749.
Fernstrom M, Fernberg U, Hurtig-Wennlof A. Insulin resistance (HOMA-IR) and body fat (%) are associated to low intake of fruit and vegetables in Swedish, young adults: the cross-sectional lifestyle, biomarkers and atherosclerosis study. BMC Nutr; 2019; 5: 15.
Klöting N, Fasshauer M, Dietrich A, Kovacs P, Schon MR, Kern M, Stumvoll M, Bluher M. Insulin-sensitive obesity. Am J Physiol Endocrinol Metab; 2010; 299: E506–E515.
Schrauben SJ, Jepson C, Hsu JY, Wilson FP, Zhang X, Lash JP, Robinson BM, Townsend RR, Chen J, Fogelfeld L, Kao P, Landis JR, Rader DJ, Hamm LL, Anderson AH, Feldman HI. Insulin resistance and chronic kidney disease progression, cardiovascular events, and death: findings from the chronic renal insufficiency cohort study. BMC Nephrol; 2019; 20: 60.
Spoto B, Pisano A, Zoccali C. Insulin resistance in chronic kidney disease: a systematic review. Am J Physiol Renal Physiol; 2016; 311(6): F1087-F1108.
Liao MT, Sung CC, Hung KC, Wu CC, Lo L, Lu KC. Insulin resistance in patients with chronic kidney disease. J Biomed Biotechnol; 2012; 2012: 691369.
Hu Y, Shi LX, Zhang Q, Peng NC. Increased Risk of Chronic Kidney Diseases in Patients with Metabolic Syndrome: A 3-year Prospective Cohort Study. Curr Med Sci; 2019; 39: 204-210.
Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). The Lancet; 1998; 352(9131): 837-853.
Scicali R, Giral P, Gallo A, Di Pino A, Rabuazzo AM, Purrello F, Cluzel P, Redheuil A, Bruckert E, Rosenbaum D. HbA1c increase is associated with higher coronary and peripheral atherosclerotic burden in non diabetic patients. Atherosclerosis; 2016; 255: 102-108.
Mossmann M, Wainstein MV, Goncalves SC, Wainstein RV, Gravina GL, Sangalli M, Veadrigo F, Matte R, Reich R, Costa FG, Bertoluci MC. HOMA-IR is associated with significant angiographic coronary artery disease in non-diabetic, non-obese individuals: a cross-sectional study. Diabetol Metab Syndr; 2015; 7: 100.
Bonora E, Kiechl S, Willeit J, Oberhollenzer F, Egger G, Meigs JB, Bonadonna RC, Muggeo M. Insulin resistance as estimated by homeostasis model assessment predicts incident symptomatic cardiovascular disease in caucasian subjects from the general population: the Bruneck study. Diabetes Care; 2007; 30: 318-324.
Barengo NC, Teuschl Y, Moltchanov V, Laatikainen T, Jousilahti P, Tuomilehto J. Coronary heart disease incidence and mortality, and all-cause mortality among diabetic and non-diabetic people according to their smoking behavior in Finland. Tob Induc Dis; 2017; 15

FigureS1..tif
Figure S1. Progressive change of HOMA-IR and HOMA-beta cell function during follow-up (* indicated p <0.001)
suppleIJO1.docx
Table S1. Odds ratios for body mass index in multivariate analysis of primary and secondary outcomes.

Insulin resistance measured by High Homeostasis Model Assessment (HOMA) as a Predictor for new onset Diabetes mellitus and diabetic Complications in Non-Diabetic Adults: A KoGES prospective Cohort Study

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Participants And Methods

Study populations

Covariates

Outcome definition

Statistical analysis

Ethical considerations

Results

Baseline characteristics

Primary outcomes for the HOMA-IR groups

HOMA-IR as an independent predictor of new onset diabetes and chronic kidney disease

Discussion

Abbreviations

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1