# data[].attributes.body
Working in your groups:

1. Search PubMed for an article of your interest that uses a basic statistical test or method

2. Create a post with:

The name of the statistical test/method in the title

A description of how it was used in the research

Link to or screenshot of the article

3. Each group should choose a different statistical test/method

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Name of the Test: Linear Regression (Poisson)

Description: Regression models were fitted for therapeutic duplication (primary outcome which was binary), and combination status (exposure which was fixed dose combinations vs free dose components). This produced RR estimates for therapeutic duplication.

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Frank Moriarty, Kathleen Bennet, and Tom Fahey

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Fixed-dose combination of antihypertensives and risk of medication errors

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Abstract

Objective

While fixed-dose combinations (FDC) can improve adherence, they may add complexity to the prescribing/dispensing process, potentially increasing risk of medication errors. This study aimed to determine if prescriptions for antihypertensive FDCs increase the risk of therapeutic duplication and drug-drug interactions (DDI).

Methods

This retrospective observational study used administrative pharmacy claims data from the Irish Primary Care Reimbursement Service. Prescriptions dispensed to adults in 2015 were included if they contained an antihypertensive FDC, or the same drugs prescribed separately. The outcomes were therapeutic duplication and potentially serious DDI involving FDC drugs. Relative risk (RR) of these outcomes, adjusted for prescription and patient factors, was determined using generalised linear models with Poisson distributions and propensity score matching.

Results

This study included 307 833 FDC prescriptions (67.0%) and 151 632 separate component prescriptions. Half of patients prescribed FDCs were female with a mean age of 67.1 (SD 12.5) years and, compared with separate component prescriptions, FDCs were less often coprescribed with other cardiovascular medications. Therapeutic duplication occurred in 0.8% of prescriptions, most often involving calcium channel blockers, and 10.6% contained a DDI (most often amlodipine and simvastatin). The RR of therapeutic duplication on FDC prescriptions compared with separate component prescriptions was 1.46 (95% CI 1.17 to 1.83) and the adjusted RR was 2.06 (95% CI 1.64 to 2.60). For DDIs, there was no significant difference between FDC and separate component prescriptions after confounder adjustment.

Conclusions

This study found FDCs were associated with increased risk of duplication. When considering prescribing FDCs, this safety consideration should be weighed against potential benefits.

Keywords: epidemiology, drug interactions, quality and outcomes of care, hypertension

Introduction

Fixed-dose combinations (FDC), single medications or dosage forms that contain a combination of two or more active ingredients, are becoming increasingly common.1 2 They are most often used where multiple drugs may be required to treat/control a condition, such as HIV/AIDS, diabetes, or hypertension, and polypills combining more diverse drug combinations (eg, for cardiovascular prevention) also exist.² By replacing multiple treatments, they can reduce pill burden for patients and simplify medication regimens. For this reason, FDCs can increase adherence compared with patients taking the equivalent drugs separately,2 3 and improve objective measures such as blood pressure.⁴ Combination therapy using multiple drugs acting on different therapeutic targets can produce synergistic effects and provide superior efficacy compared with monotherapy. Using multiple agents can also benefit tolerability where a combination allows for each drug to be used at lower doses than would be required if either drug was used alone, reducing the risk of adverse effects.⁵ Hypertension is one of the conditions where FDCs are used most commonly.3 5 Most patients require multiple antihypertensive drugs to meet blood pressure targets, and hence the greatest number of FDCs is in this therapeutic area.⁵ Hypertension is highly prevalent, particularly among older people, and is often comorbid with other conditions in this age group.⁶ Hence, FDCs may be particularly beneficial for such patients with high pill burdens for whom adherence to treatment may be challenging.⁷ Studies have demonstrated the advantages both in efficacy and safety of utilising multiple antihypertensives instead of a higher dose of a single agent,⁵ and there is evidence from a recent Cochrane review that initiating combination antihypertensive therapy rather than monotherapy may be advantageous.⁸

A disadvantage to FDCs is the 'fixed' nature of the dosing combinations may make dosage adjustment more difficult, leading to potential underdosing or overdosing.¹ FDCs may reduce the ability to identify the cause of an adverse drug event relating to one of the drug ingredients. FDCs could also contribute to medication errors. They are frequently prescribed by brand name,⁹⁻¹¹ and presence of multiple ingredients available in varying strengths within a single product adds complexity to the prescribing and dispensing process.¹² Medication errors cause a substantial burden of patient harm, morbidity and mortality¹³; however, the impact of FDCs on medication safety and prescribing quality has not been extensively evaluated to date. Therefore, the aim of this study is to determine if prescriptions for antihypertensive FDCs increase the risk of prescribing errors, namely therapeutic duplications and potentially serious drug-drug interactions (DDI), compared with the free combination (FC) ingredients prescribed separately.

Methods

Study design, setting and participants

The Strengthening the Reporting of Observational Studies in Epidemiology statement has been used in the planning and reporting of this research.¹⁴ This is a retrospective cohort study using the Health Service Executive-Primary Care Reimbursement Service (HSE-PCRS) administrative pharmacy claims database.¹⁵ The analysis includes prescriptions dispensed to adults (aged 18 years and over) under the General Medical Service (GMS) scheme in Ireland in 2015. The GMS scheme is public health cover that provides free medical care and prescribed medications to about one-third of the general Irish population whose household income is below the eligibility threshold. It also covers the vast majority (approximately 95%) of people aged 70 years and over, where a higher income threshold applies.

The unit of analysis was each prescription dispensing claim, and these were included if there was a dispensing for either of the following:

All such prescriptions dispensed to an adult on the GMS scheme during 2015 were included in the analysis. The relevant combinations available on the market, all of which were included in this study, are listed in online supplementary table S1. Permission was obtained from the HSE-PCRS to conduct this analysis. As the data were anonymised and results presented at group level, ethical approval was not required.

heartjnl-2018-313492supp001.docx (3.2MB, docx)

Outcomes

The primary outcome was therapeutic duplication involving a component of an included antihypertensive FDC, defined as two drugs from a therapeutic class (ACE inhibitors, angiotensin II receptor blockers (ARB), calcium channel blockers or beta blockers) on the same pharmacy claim (ie, dispensed from the one prescription). Products containing the same drug were not considered therapeutic duplication. The secondary outcome was the presence of a potentially serious DDI involving a component of an included antihypertensive FDC and another coprescribed medication. Interactions where the sole adverse effect relates to hypotension were excluded, as prescribers may have accounted for this interaction in the dose they prescribe and thus these are less likely to constitute medication errors. Interactions were considered potentially serious if they carry a 'black dot' in the British National Formulary (online supplementary table S2).

Exposure and potential confounders

The exposure of interest was prescribing of antihypertensives as an FDC compared with prescribing of individual antihypertensives separately.

To adjust for potential differences between prescriptions containing FDCs and separate components, the following potential confounders were included: the age and sex of the patient, and the type of antihypertensive (ACE inhibitor, ARB or beta blocker) and total antihypertensive dosage dispensed (expressed using the WHO classification of defined daily dosages or DDD). We categorised based on drug class for adjustment due to the large number of individual drug combinations. To account for potential differences in cardiovascular comorbidities, the analysis adjusted for the total numbers of items on the same prescription claim from each of the following classes (defined by WHO Anatomical Therapeutic Classification codes):

Other antihypertensives (C02, C03, C07, C08, C09).

Lipid-lowering agents (C10).

Anticoagulants/antiplatelets (B01).

Antidiabetic agents (A10).

Other cardiovascular agents (C01, C04, C05).

Lastly, the number of other items (excluding the above categories) on the prescription claim was also adjusted for. Previous research on the relationship between FDCs and adherence has similarly adjusted for coprescribing of other medicines.16 17

Statistical methods

Analyses were all conducted at the level of the pharmacy claim (ie, prescription). Descriptive statistics are presented for included dispensed prescriptions and separately for FDC and FC prescriptions. Differences between the FDC and FC groups were assessed by calculating standardised mean differences (SMD) for each variable, which are independent of sample size.

Regression models were fitted for therapeutic duplication as the binary outcome variable, and combination status (FDC vs FC) as the exposure variable of interest. This analytical approach was then repeated for the secondary outcome of presence of a potentially serious DDI. The non-independence of repeated dispensings to individuals was accounted for by estimating robust standard errors. Generalised linear models using the Poisson distribution were fitted to produce unadjusted and adjusted estimates of the relative risk (RR) of therapeutic duplication, with 95% CIs. Age, sex, antihypertensive type and DDDs in the FDC/FC, and the numbers of other prescription items mentioned above were adjusted for.

A propensity score (representing the probability of an FDC being prescribed) was generated by fitting a logistic regression model including all of the prescription and patient characteristics as covariates. This was used to match FDC and FC prescriptions using a calliper of 0.2 of the SD of the logit of the propensity score.¹⁸ This allowed for the absolute risk difference (ARD) between the FDC and FC prescriptions for therapeutic duplication and DDI to be calculated in both crude and propensity score-matched analyses. By taking the reciprocal of these, we also determined the numbers needed to treat to cause harm (NNTH; ie, the number of patients who need to be prescribed an FDC rather than an FC to cause one additional case of therapeutic duplication/DDI). To assess the impact of residual covariate imbalance, the propensity score-matched regression was repeated with double adjustment for covariates with an SMD of >0.10 after matching.¹⁹ Different approaches to propensity score adjustment were also conducted as a sensitivity analysis, that is, adjusting for propensity score (crude, trimmed, quintiles) in the regression alone or with other covariates.

To assess the robustness of the results and impact of potential residual confounding, sensitivity analysis was conducted to assess the magnitude of effect of residual confounding that would fully explain the observed results.²⁰

Results

Descriptive statistics

A total of 459 465 prescriptions, issued to 49 283 patients, were included in this study, 307 833 (67%) containing an FDC and the remaining 151 632 (33%) containing an equivalent FC. Descriptive statistics for included prescriptions are provided in table 1. Fifty per cent of FDC prescriptions were for women, with a mean age of 67.1 (SD 12.5) years, while for FCs, 48.1% of prescriptions were for women and the mean age was 70.1 (12) years. Prescriptions for FDCs involved an ARB more frequently and an ACE inhibitor or beta blocker less frequently compared with FC prescriptions. FDCs had higher doses of antihypertensives than FCs, although these prescriptions were less likely to contain another coprescribed medication to treat cardiovascular disease and contained fewer other prescription items.

Table 1.

Therapeutic duplication and DDIs

Of all included prescriptions, 0.8% had an instance of therapeutic duplication on the same prescription claim, most often relating to calcium channel blockers, and 10.6% contained a potentially serious DDI relating to a combination ingredient (see tables 2 and 3). Calcium channel blockers were the most commonly implicated of the antihypertensive agents in both duplications and interactions, with amlodipine and simvastatin being the most frequently occurring individual interaction (in 40.5% of all prescriptions with an interaction, online supplementary table S3).

Table 2.

FC
(n=151 632)

FDC
(n=307 833)

Table 3.

FC
(n=151 632)

FDC
(n=307 833)

Propensity score-matched regression†

1.09 (1.03 to 1.15)

0.005

The crude RR of drug duplication on FDC prescriptions compared with FC prescription was 1.46 (95% CI 1.17 to 1.83, ARD 0.28%; NNTH 358), and the RR was higher after adjusting for prescription and patient characteristics ( table 2). A propensity score match was successful for 97% of FC prescriptions (online supplementary figure S1 and table S4) and in propensity score-matched regression, the RR of duplication was 2.29 (95% CI 1.81 to 2.90; ARD 0.75%; NNTH 133). The magnitude did not vary substantially regardless of how the propensity score was used for adjustment (see online supplementary table S5).

For DDIs ( table 3), the risk was lower for FDC prescriptions relative to FC prescriptions in unadjusted analysis (RR 0.69, 95% CI 0.66 to 0.73, ARD −4.07%; NNTH −25). Adjusting for covariates, the RR was 1.00 (95% CI 0.94 to 1.05). Propensity score matching yielded an RR of 1.09 (95% CI 1.03 to 1.15; ARD 0.98%; NNTH 101); however, most other methods of propensity score adjustment produced RR estimates that were not statistically significant (online supplementary table S5).

Sensitivity analysis

Considering the largest effect an unmeasured confounder would need to explain the observed association between FDC prescriptions and therapeutic duplication, a factor occurring in 5% of FDC prescription would need to have an RR of therapeutic duplication of 7.6, 4.2 or 2.5 if it was present in 30%, 50% or 95% of FC prescriptions, respectively (see figure 1).

Figure 1.

Effect of an unmeasured confounder present in 0.05 of fixed-dose combination (FDC) prescription claims required to explain observed association between FDC prescriptions and duplication.

Discussion

This study found that prescriptions for antihypertensive FDCs carry a twofold increased risk of therapeutic duplication compared with the FC ingredients prescribed separately; however, the absolute risk of duplication associated with FDCs was less than 1%. Although the risk of DDIs was higher at approximately 10%, there was no apparent difference between FDC and FC prescriptions.

Although some FDC medications have been flagged as medications which increase the risk of prescribing or dispensing errors,²¹ there appears to be little empirical evidence of this. Much of the literature has focused on assessing the most common types of medication errors, and the drug classes most frequently involved.12 22 By contrast, alternate medications or formulations and their risk of medication errors have received less attention. For instance, oral dosage forms via a monitored dosage system result in fewer administration errors among care home residents.²³ There are several potential ways that FDCs may increase risk of medication errors. Greater than two strengths of two ingredients in an FDC will result in more products listed in an electronic record system or on a pharmacy shelf, increasing the risk of selection errors.¹² Dosing errors may result from FDCs containing two ingredients of the same strengths, for example, combinations of amlodipine 5 or 10 mg and perindopril arginine 5 or 10 mg. These action-based errors, that is, discordance between the intended product to be prescribed/dispensed and the actual action, could not be identified using our data source.²² Instead, these pharmacy claims were used to evaluate the knowledge-based errors of duplication and DDIs, for which there are a number of plausible mechanisms. As marketing of FDCs often results in patent extension of off-patent drugs, brand names are more likely to be used for FDCs.²⁴ Brand names are more often confused with another at the prescribing/dispensing stage than generic or international non-proprietary names (INN).9 25 Moreover, the information provided by the INN suffix on a drug's therapeutic class (ie, -sartan indicating an ARB) is lost when prescribing a brand name.⁹ Hence, healthcare professionals may be less likely to identify a drug interaction or duplication if they do not recognise the type of drug an FDC contains.⁹⁻¹¹

Knowledge-based errors such as therapeutic duplication or drug interactions may be mitigated through the use of computer-based clinical decision support systems (CDSS).²⁶ The majority of prescribing/dispensing systems incorporate CDSS capable of detecting instances of therapeutic duplication and DDIs.²⁷ It is possible that implementation of duplication/interaction checking may be less comprehensive for combination products if software is only capable of cross-referencing one active ingredient per medication. A lack of reference to the calcium channel blocker component of combination products in the software drug product file may explain the increased risk of therapeutic duplication. However, due to the proprietary nature of the prescribing/dispensing software systems used in Ireland, we are unable to determine their capability for cross-referencing products with multiple ingredients. Even when detection is effective, CDSS alerts ought to be implemented appropriately to minimise alert fatigue and ensure such medication issues are acted on where necessary. This is an important consideration in the development and implementation of electronic health records, particularly given the increasing use of combination products across therapeutic domains. In addition, increased generic prescribing and dispensing (ie, using INN) may also reduce medication errors. In a simulated before and after study of generic labelling of medications, FDCs were one of just two circumstances where errors decreased.²⁸ When patients and doctors are considering an FDC, it is important to assess the potential risks and benefits. Although use of FDCs can reduce pill burden,¹⁷ the potential impact on medication adherence is complex. A lower pill burden can reduce regimen complexity; however, this is only one of over 700 potential factors found to be associated with non-adherence,²⁹ and so any potential benefit of FDCs is dependent on the factors contributing to non-adherence in each individual patient's case. FDCs could have a negative impact on some aspects of adherence, for example, unnecessarily high doses due to inflexible combinations could result in treatment discontinuation (or non-persistence) due to adverse effects. FDCs can also provide economic benefits.16 24 These benefits should however be weighed against the potential for iatrogenic harm due to medication errors. Even if the risk is low, medication errors can cause a substantial burden of patient harm, morbidity and mortality.¹³

This study has a number of strengths and limitations. This is a large analysis which used the PCRS database, the most comprehensive source of medication prescribing and dispensing information in Ireland, capturing approximately 40% of the population.¹⁵ As this is an observational study, there may be residual confounding due to unmeasured factors which predict both whether antihypertensive medications are prescribed in combination and therapeutic duplication/drug interactions, such as whether a prescriber uses CDSS. Due to the retrospective nature of this study we could not collect data on these additional factors. However, the results were robust to various methods of adjustment for measured confounders, and we quantified the magnitude of confounding due to unmeasured factors that would alter the findings. Other potential action-based medication errors (ie, selecting a different product than intended) could not be evaluated. In addition, these data include only medications dispensed on the GMS scheme.¹⁵ The scheme population is not representative of the general population (tending to be older and more socioeconomically deprived), and so the observed association may differ in other populations. We were unable to adjust for clustering of prescriptions at the prescriber level, and so the width of the CIs may be underestimated. However, some of the prescriber-level effects are likely to be accounted for in our adjustment for patient-level clustering. There may be clinical awareness and rationale for the duplications and interactions detected in this study, but regardless of whether these were intentional, FDCs appear to be associated with marginally higher therapeutic duplication. The definition of therapeutic duplication used did not include cases where the same active ingredient was prescribed in two different medications as this has the potential to be appropriate and intentional, thus our results represent a conservative estimate of the extent of therapeutic duplication. Similarly, by excluding DDIs that relate solely to hypotension where a prescriber may have titrated the antihypertensive doses to account for such an interaction, we have aimed to reduce misclassification of medication errors.

Compared with prescribing antihypertensive drugs as separate medications, prescriptions for FDC medications were associated with greater risk of therapeutic duplication but not DDIs. The absolute risk is small and should be considered in light of potential benefits of FDCs to patients. If FDCs and polypills are to be implemented more widely as has been proposed,³⁰ it is important to consider how their use may influence prescribing quality and risk of medication errors, and thus medication safety. Systems to reduce medication errors, such as CDSS, should be evaluated to ensure they are functional for both FDC and single ingredient medications.

Key questions.

What is already known on this subject?

What might this study add?

How might this impact on clinical practice?

Acknowledgments

We acknowledge the Primary Care Reimbursement Service for providing the administrative data used in this study.

Footnotes

Twitter: @FrankMoriarty @HRBPrimaryCare

Contributors: FM conceived and designed the study. KB acquired the data and FM conducted the analysis. FM, KB and TF interpreted the data. FM drafted the manuscript and all authors revised this critically and approved the final version.

Funding: This research was supported by the Health Research Board (HRB) in Ireland through the HRB Centre for Primary Care Research (grant number HRC/2014/1). KB was supported by the HRB (grant number RL/15/1579).

Competing interests: None declared.

Patient consent: Not required.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data sharing statement: There are no additional data available from this study.

References

1. European Medicines Agency Committee for Medicinal Products for Human Use (2017) Guideline on Clinical Development of Fixed Combination Medicinal Products. London, UK. [Google Scholar]

21. Irish Medication Safety Network. Briefing Document on Sound-Alike Look-Alike Drugs (SALADs), 2010. [Google Scholar]

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

Supplementary Materials

heartjnl-2018-313492supp001.docx (3.2MB, docx)

Articles from Heart are provided here courtesy of BMJ Publishing Group

# data[].attributes.attachment_link.title
Fixed-dose combination antihypertensives and risk of medication errors

# data[].attributes.attachment_link.description
While fixed-dose combinations (FDC) can improve adherence, they may add complexity to the prescribing/dispensing process, potentially increasing risk of medication errors. This study aimed to determine if prescriptions for antihypertensive FDCs ...

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Analysis of variance was used to compare the means of 4 grps (placebo, tirzepatide 5, 10, 15 mg) to determine if there are any statistically significant differences at baseline.

# data[].attributes.custom_properties.9w5v_5ok
 Julio Rosenstock, Carol Wysham, Juan P Frías, Shizuka Kaneko, Clare J Lee, Laura Fernández Landó, Huzhang Mao, Xuewei Cui, 
Chrisanthi A Karanikas, Vivian T Thieu

# data[].attributes.custom_properties.PmT6ZPYI
Efficacy and safety of a novel dual GIP and GLP-1 receptor 
agonist tirzepatide in patients with type 2 diabetes 
(SURPASS-1): a double-blind, randomised, phase 3 trial

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The test was used to check if the proportion of each cancer subtype is statistically different between WES and WGS cohorts.

Article link: https://www.nature.com/articles/s41467-020-18151-y

# data[].attributes.custom_properties.PmT6ZPYI
Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

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The test was used to compare participants outcomes (FBS,HbA1c, Body weight) before and after the lifestyle program within the same individuals.

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Shin SW, Jung SJ, Jung ES, Hwang JH, Kim WR, So BO, Park BH, Lee SO, Cho BH, Park TS, Kim YG, Chae SW

# data[].attributes.custom_properties.PmT6ZPYI
Effects of a Lifestyle-Modification Program on Blood-Glucose Regulation and Health Promotion in Diabetic Patients: A Randomized Controlled Trial

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Abstract

Background

We aimed to investigate the efficacy of the lifestyle intervention (LSI) program in controlling blood glucose regulation and health promotion in type 2 diabetic (T2D) patients.

Methods

Thirty adults with a diagnosed with diabetes were randomly assigned to LSI and control groups. The LSI group maintained their daily routines after participating twice in the LSI program, while control group maintained 4 weeks of daily life without participating in an intervention.

Results

HbA1c levels in the LSI group decreased significantly after participation (p = 0.025) compared with levels before the study, but there was no significant difference between the groups. The weight and body mass index (BMI) of the LSI group tended to decrease significantly compared with the control group (p = 0.054 and p = 0.055, respectively), and the waist circumference (WC) of the LSI group decreased significantly compared with that of the control group (p = 0.048). In the effects of the LSI program according to the polymorphism of GCKR genes, changes in glycated albumin (GA) (%), HbA1c, WC, BMI, and weight showed a significant decrease in the non-risk (TT genotype) GCKR group compared with the risk group (CC and TC genotype).

Conclusion

Application of the four-week LSI program to diabetics revealed positive effects on blood-glucose control and improvement in obesity indicators. In particular, the risk group with variations in the GCKR gene was associated with more genetic effects on indicators such as blood glucose and obesity than was the non-risk group.

Keywords: Diabetic patients, Lifestyle modification, Personalized recommendation, Gene-based, Blood glucose, Direct-to-consumer test, Genotype, Health promotion

INTRODUCTION

According to a recent World Health Organization (WHO) report, the global prevalence of diabetes among adults over 18 years of age rose from 4.7% in 1980 to 8.5% in 2014. In Korea, the number of cases of diabetes mellitus (DM) has increased due to westernization of lifestyles and diets. According to a report published in 2016, the prevalence rate for DM was 13.7% among adults 30 or older [ 1], with one out of every seven adults 30 or older in Korea found to be diabetic. Moreover, the prevalence rate for people aged 65 or older was greater than 30%, and the prevalence rate of pre-diabetic impaired-fasting glucose (IFG) among the population was 24.8%. If the diagnosis includes the prevalence rate of IFG, which is not commonly applied, it can be assumed that the actual prevalence rate is higher. Therefore, without diagnosis of potential impaired-glucose tolerance (IGT) and proper blood-glucose control measures, prevalence rates for DM are expected to surge in the coming years, becoming an economic and social burden not only for individuals but at the national level. Effective management of diabetes and prevention of associated complications require correcting problematic eating and physical activity habits, as well as drug choices. Intervention methods for metabolic syndromes, including DM, in Korea and elsewhere, can be divided into drug and non-drug treatments. Effective non-drug and behavioral approaches focus on reducing smoking and drinking, regulating weight, increasing physical activity levels, and controlling meals [ 2- 10]. While conventional methods of managing metabolic diseases focus on diet, physical activity, weight, stress management, and smoking cessation, precision nutrition can prevent and manage chronic diseases by optimizing interventions and taking into account individual genetic backgrounds, metabolic profiles, intestinal microbiology, environments, and lifestyles. Instead of traditional diabetes treatments, the development of personal clinical approaches, as well as genomic, transcriptomic, epigenomic, and metabolomic information and intestinal microbial cluster technology provide opportunities to apply precision nutrition to the prevention and management of type 2 DM [ 11]. In recent years, the application of precision medicine to chronic diseases including diabetes has spread throughout the world, followed by research according to demand for and interest in personalized precision medicine [ 12- 17]. However, evidence supporting the application of precision medical treatments that use genetic tests for diabetes in Korean medical sites is lacking, and whether lifestyle intervention (LSI) can help prevent and treat diabetes remains unclear. Prior studies on the effects of LSI diabetes programs in Korea have reported reduced risk factors for metabolic syndrome, but no randomized controlled trials (RCTs) have been conducted to evaluate the effectiveness of such programs. In addition to providing personalized genetic risk information, it is also necessary to determine whether LSI programs, which can be effective in changing individual expressive risk factors and behaviors, benefit from a synergy effect. In other words, it is necessary to determine whether diabetes can be suppressed and controlled through improvement of lifestyles even if the genetic risks to an individual are high. We therefore conducted an RCT to determine whether an LSI program that uses effectively manage diabetes.

MATERIALS AND METHODS

1. Subjects

The subjects in this study were recruited through Clinical Trial Center for Functional Foods (CTCF2) advertising (homepage, brochure, and poster) at Chonbuk National University Hospital (CBUH) from May 17 to May 30, 2018. This study was approved by the CBUH Institutional Review Board (IRB No. CUH 2018-05-011). This clinical trial protocol was registered at Clinical Research Information Service of Republic of Korea ( https://cris.nih.go.kr/cris/en/: board approval number:KCT0005313). All procedures were carried out in accordance with the Helsinki Declaration and Korean Good Clinical Practice protocols. Subjects were registered after signing a consent form for participation in a human study, with 30 subjects deemed suitable for the study through a screening process that included assessments, physical examinations, and laboratory tests conducted within three weeks of participating in the LSI program.

The criteria for selection were: adults aged 19 or older at the time of screening; a diagnosis of diabetes at the time of screening (6.5% ≤ HbA1c < 9.0%); and those who heard and fully understood a detailed description of this study and agreed in writing to participate and comply with the precautions.

Exclusion criteria were: diagnosis of type 1 and type 2 DM with or under insulin medication within the last three months; diagnosis of a clinically significant acute or chronic disease of the cardiovascular, endocrine, immune, respiratory, hepatobiliary system, kidney and urinary, neuropsychiatric, or musculoskeletal system or an inflammatory and hemato-oncological condition; a history of gastrointestinal diseases (e.g., Crohn's disease, etc.) or gastrointestinal surgery (except simple appendectomy or hernia surgery) that may affect the absorption of nutrients; an inability to participate in an LSI, such as a food intake and exercise prescriptions, according to dietary prescriptions; taking antipsychotic drugs or narcotic analgesics within six months prior to the screening tests; suspicion of alcoholism or drug abuse or a similar history; serum alanine aminotransferase (ALT) or aspartate aminotransferase (AST) levels > three times the upper limit of the reference range or serum creatinine > 2.0 mg/dL; and participation in a human study within two months before the screening tests. Subjects were also excluded if a screening tests showed that a pregnant, breast-feeding or possibly-pregnant patients was not using appropriate contraception methods such as hormone transplants, intrauterine devices, and oral contraceptives. Some potential subjects were deemed inappropriate for participation by the tester for other reasons, including the results of diagnostic and medical examinations.

2. Study design

The study was a prospective comparison of patient and control groups conducted on subjects diagnosed with diabetes. A total of 30 subjects were assigned to the either the test group (participating in the LSI program) or control group (maintenance of daily lifestyle), with 15 randomly assigned to each group ( Fig. 1). Members of the LSI group maintained their daily routines after participating in two LSI sessions, while the control group members maintained four weeks of daily life without participating in an intervention program. The researcher visited the CTCF2 at CBUH and implemented screening tests for applicants who had completed the written consent form. The subjects visited the CTCF2 for the first time within three weeks of screening, were reviewed for selection and exclusion criteria, and registered for the study. Members of the LSI and control groups completed a baseline examination by the day of the first session. Four weeks after participating in the study, 28 days after the baseline examination date, all subjects visited the center to submit to an inspection specified in the plan.

Fig. 1.

Flow chart of the lifestyle intervention program.

3. Lifestyle interventions

1) LSI group (participating in the LSI program)

The education and counseling provided to the LSI group focused on improving overall behavior with respect to desirable lifestyle habits, including antidiabetic diets, physical activity, non-smoking, moderate drinking, and stress management. Customized counseling based on genetic information was also provided as an experience-oriented program ( Fig. 1). The LSI program was administered by CBUH medical staff specializing in endocrine metabolism, biochemist, gastroenterology, nephrology, and urology. Nutritionists, nurses, an exercise specialist, forest-walk commentator, and a counseling therapist also participated in the educational programming. In addition to providing personalized genetic risk information to the subjects of the LSI group, the researchers delivered LSI programming with genetic counseling as part of its efforts to change the expressive risk factors and behavior of individuals. The educational content of the study was developed by referring to and reconstructing studies on the effects of the LSI program [ 9, 18- 21]. The LSI group visited the research center twice (spending two nights and three days, for a total of 28 hours in the first session and 5 hours in the second session) over four weeks ( Table 1). The educational content of the first session included lectures, practice exercises, and individual counseling. Three self-help groups of four subjects each were formed to provide an environment in which feedback could be supplied to the researchers. In the second session, subjects visited the CTCF2 of CBUH to review diet management, tasting sessions, and walking exercises introduced during the first session. Content for the LSI program was based on a booklet titled "Diabetes Control through the Improvement and Management of Lifestyles" and participants filled out a diet and exercise diary. Lifestyle improvement methods including nutrition education and exercise methods to be practiced at home. The contents and materials of the LSI program are shown in ( Supplmentary Table 1).

Table 1.

LSI group
(n = 15)

Control group
(n = 15)

Total
(N = 30)

p-value*

Sex (M/F)

8/7

4/11

12/18

0.136†

BMI (kg/m ²)

24.00 ± 4.19

24.79 ± 2.84

24.40 ± 0.65

0.549

Smoking (Y/N)

1/14

0/15

1/29

> 0.999‡

2) Nutrition, drinking, and smoking education programs

Diet-control education was conducted in parallel with lectures and individual counseling. The educational content included the principles of an antidiabetic diet, diet planning, healthy table preparation, self-diet evaluation (comparing differences before and after participation), maintenance of a diet and exercise diary, explanation of dietary-intake analysis results, dietary principles for healthy blood-glucose control, how to determine the amount of one-time intake per person, calculation of individual caloric requirements, nutritional functions of dietary fiber, how to follow a blood-glucose control diet with Korean food, responsible drinking habits, how to dine out and consume snacks, and how to buy food to help control blood glucose. The dietary guidelines were designed to allow individuals to consume sufficient energy for their individual daily physical activity level.

3)Exercise, and mental and physical management programs

Exercise education was based on prior studies, and individual exercise prescriptions and guidance were provided in combination with lectures, and individual counseling. The first session involved walking a forest road, brain yoga, foot massages to promote blood circulation, upper-body muscle strengthening and muscle strength improvements, lower-body muscle strengthening exercises, and full-body circuit exercises. The second session involved improving mental and physical activities through communication and forest-road walks.

4)Individual counseling of personal genetic information

Members of the LSI group took part in one-on-one counseling with a medical doctor to discuss the results of genetic tests. Drawing on the results of examinations of eight tag-single-nucleotide-polymorphisms (SNPs) related to blood-glucose metabolism, on the first day of the session we provided information on the genetic risk of diabetes, personal genetic backgrounds, metabolic profiles, environmental variables, and lifestyles, as well as recommending participants adopt healthier lifestyles. Customized counseling was conducted according to the genetic polymorphism of the GCKR gene, and subjects considered at risk were given intensive dietary control in the form of a two-thirds reduction in carbohydrate intake and encouraged to increase their physical activity levels.

4. Outcome measurements

Metabolic data were obtained using standard protocols of the CBUH clinical laboratory. All subjects who visited CTCF2 underwent a safety assessment at the first visit (baseline, week 0) and fourth visit (week 4). Data on demographics, smoking, physical activity, alcohol drinking, medical history, dietary intake, anthropometric and biochemical parameters, and vital signs were obtained from each individual in both groups. Hematology examinations, blood biochemical tests, and urine tests were conducted after subjects had fasted for 12 hours.

1) Efficacy assessments

Efficacy assessments included glucose (fasting prandial glucose [FPG], during an oral glucose tolerance test [OGTT]), postprandial plasma glucose (PPG), glucose incremental area under the curve (iAUC), insulin (fasting plasma insulin [FPI]), homeostatic model assessment of insulin resistance (HOMA-IR), glycated albumin (GA), and HbA1c. The glucose and insulin iAUCs during OGTTs were determined using the trapezoidal method.

2) Anthropometric parameters

Anthropometric data were obtained for height, weight, and waist and hip circumferences, and body mass index (BMI) in kg/m ² was obtained from the height and weight measurements obtained with the GL150 system (G-Tech Co., Uijeongbu, Korea) in light clothing. Waist circumference (WC) was measured using a tape measure parallel to the lower rib and the middle of the pelvis when the subject was standing and breathing comfortably. Body fat, body fat rate, and muscle mass were measured using an Inbody 720 body analyzer (Biospace Co., Seoul, Korea).

3) Blood lipid markers and inflammation

Total cholesterol (TC), neutral blood lipid levels, and high-density lipoprotein cholesterol (HDL-C) levels were analyzed with a Hitachi 7600-100 analyzer (Hitachi High Technologies Corporation, Tokyo, Japan), and low-density lipoprotein cholesterol (LDL-C) content was calculated with the Friedewald formula [ 22]. Lipid metabolic indexes of apolipoprotein A1 and apolipoprotein B along with liver enzyme indexes for gamma-glutamyl transferase, ALT, AST and total bilirubin were analyzed with an ADVIA 2400 chemistry system (SIEMENS, Munich, Germany). Inflammatory indesxs were measeured using a serum high sensitivity C-reactive protein (hs-CRP) latex immunoassay method and erythrocyte sedimentation rate(ESR) for Westergren methods.

4) Blood pressure measurement

Blood pressure was measured with an HBP-9020 (Omron Healthcare Co., Ltd, Kyoto, Japan) analyzer after the subject arrived at the research site and had rested comfortably for at least 10 min. Three measurements of systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse rate were recorded at intervals of approximately 2 min while the subject was seated, and an average was calculated. A medical team carried out the examination through interviews, ocular inspections, auscultation, percussion, and palpation.

5) DNA isolation and genotyping

Genetic information was analyzed by selecting eight tag-SNPs related to blood-glucose metabolism that can predict type 2 DM occurrence. Two genes relate to blood-glucose metabolism (CDKN2A and CDKN2B). GLIS3 involves both the growth of beta cells and the production of insulin in the pancreas. Three genes (MTNR1B, DGKB, and SLC30A8) are functionally associated with the secretion of insulin, and three (GCK, GCKR, and G6PC2) were selected and tested for a relationship with cellular or mitochondrial metabolic function [ 23- 28]. Analysis of genetic tests was performed using a peripheral blood sample after obtaining consent from the subjects. Genotype analysis was performed by Therazen Co., Ltd.(Seoul, Republic of Korea) on all 30 subjects according to manufacturer guidelines using the QuantStudio 12K the analysis system (Flex Accefill, Life Technologies, Carisbad, CA, USA) and applying the Taqman assay method.

6)Calculation of genetic risk scores related to blood-glucose metabolism

Polymorphisms were analyzed using the eight gene markers related to blood-glucose metabolism, and the genetic risk was evaluated based on the results. A genetic risk score (GRS) was calculated by assigning 0 points for two homozygous non-risk alleles, 1 point for two heterozygous alleles, and 2 points for two homozygous risk alleles.

7) Evaluation of diet and physical activity

To investigate changes in dietary habits, a nutritionist trained in dietary records management explained to subjects how to prepare a dietary diary and collected data after face-to-face interviews when retrieving each subjects' dietary diary. During the first visit (baseline, week 0) and the third visit (week 4), the dietary diary from the previous day was examined using CAN-Pro 4.0, a computer-aided nutritional analysis program from the Korean Nutrition Society Forum, Seoul, Korea, and average values were calculated. To investigate changes in physical activity, the global physical activity questionnaire was administered on the first visit (baseline, week 0) and the third visit (week 4), and a metabolic equivalent task (MET) value was calculated.

8) Safety outcome measurements

Subject clinical conditions, including adverse reactions, were evaluated and recorded in case report lists. All subjects underwent safety evaluations at baseline (week 0) and after completing the four-week study. Safety assessments included electrocardiograms and laboratory tests. Hematological examinations included counts of white blood cells (WBCs), red blood cells, and basophils, and levels of hemoglobin, hematocrit, platelets, neutrophils, and lymphocytes. Liver and kidney functions, including total bilirubin, total protein, alkaline phosphatase (ALP), ALT, AST, blood urea nitrogen, and creatinine assessments, were measured by staff of the clinical pathology department of our hospital.

9) Statistical analyses

All statistical processing used SAS 9.2 (SAS Institute, Cary, NC, USA). All data were presented as a mean ± standard deviation for continuous variables and as a frequency for categorical variables. Per protocol analyses were performed. Categorical variables were compared using the chi-square test (Fisher's exact test). For average comparisons between the two groups, an independent-sample t-test was used for independent samples, and a paired-sample t-test was used for paired samples. p < 0.05 were considered statistically significant.

RESULTS

1. Participant demographics

A total of 48 volunteers were screened and 30 subjects (12 males and 18 females) were selected, with 15 subjects each randomly assigned to the test and control groups. Twenty-eight of the 30 subjects completed the study in compliance with the protocols, and two subjects withdrew consent. The results of the statistical analysis were taken from the data obtained from the 28 subjects who completed the study. General characteristics of the subjects are presented in Table 1. The average age of the subjects was 64.5 ± 6.0 years, and there was no significant difference in age, sex, BMI, vital signs, smoking rate or quantity, drinking rate, and alcohol consumption between the two groups (p > 0.05). Among the key validation items, the HDL-C baseline of the LSI group was significantly higher than that of the control group (p = 0.029), even though the FPG, insulin, HbA1c, and HOMA-IR values did not differ significantly between two groups. In this study, therefore, statistical results were presented after correcting the baseline (HDL-C) ( Table 1).

2. Genotype frequency of the subjects

Results of the analysis of the eight gene polymorphisms associated with subject blood glucose are presented in ( Supplementary Table 2). The frequency of risk alleles for blood glucose related to gene markers showed a distribution of more than 60% of risk alleles in seven gene markers (rs10811661, rs560887, rs780094, rs7034200, rs10830963, rs2191349, and rs13266634) out of a total of eight genes. Compared with the LSI group, the control group displayed significant differences between groups due to a higher distribution of risk alleles in the gene markers for CDKN2A/B, GCK, and DGKB (p < 0.05). In particular, the gene marker rs560887 was associated with the highest frequency of risk alleles, at 100%.

3. Dietary intake and physical activities

Results of an investigation of dietary intake and physical activity before the study (week 0) and after participating in this study (weeks 1, 2, and 4) are presented in Table 2. Comparing changes in diet before and after participation, intake of calories, carbohydrates, proteins, fats, and fibers showed no statistically significant differences within or between test groups (p > 0.05). In addition, no significant difference within or between groups in the change of the MET of the subjects was found (p > 0.05).

Table 2.

Nutrients

LSI group (n = 13)

Control group (n = 15)

p-value†

wk 0

wk 1

wk 2

wk 4

p-value*

wk 0

wk 1

wk 2

wk 4

p-value*

β-carotene ( µ g)

6517.4 ± 3683.9

6585.4 ± 2961.1

0.940

6034.0 ± 4617.1

4813.7 ± 2934.3

0.122

0.251

Vitamin B ₁(mg)

1.19 ± 0.4

1.43 ± 0.4

0.006

1.04 ± 0.3

1.08 ± 0.5

0.758

0.026

Vitamin B ₂(mg)

1.17 ± 0.5

1.21 ± 0.3

0.725

1.01 ± 0.4

0.90 ± 0.5

0.388

0.039

Vitamin B ₆(mg)

1.67 ± 0.6

1.94 ± 0.5

0.048

1.47 ± 0.4

1.54 ± 0.6

0.644

0.040

Folic acid ( µ g)

568.8 ± 236.6

573.5 ± 129.3

0.926

440.5 ± 112.7

456.0 ± 215.2

0.731

0.114

4. Changes in glucose indexes

Changes in blood-glucose indexes of the LSI and control groups before and after participating in the study are presented in Table 3. In a comparison of the changes in FPG, PPG, and fasting insulin before and after participating in the study, no significant difference within or between groups was found (p > 0.05). In a comparison of the changes in GA, HbA1c, and HOMA-IR before and after participating in the study, members of the LSI group showed a tendency for significantly lower GA levels after participation compared with before participation (p = 0.085). Although HbA1c levels showed a statistically significant decrease after participation (p = 0.025), there was no statistically significant difference within groups (p > 0.05). In addition, no statistically significant difference between these two groups was found with respect to the other items (p > 0.05).

Table 3.

LSI group (n = 13)

Control group (n = 15)

p-value†

Baseline

Wk 4

Change value

p-value*

Baseline

Wk 4

Change value

p-value*

PPG (mg/dL)

Glucose iAUC 0-2 h (hr*mg/dL)

199.6 ± 72.7

221.6 ± 59.8

22.0 ± 35.4

0.045

228.1 ± 53.6

249.6 ± 69.0

21.5 ± 53.2

0.140

0.977

Insulin (2.6-24.9 μ U/mL)

10.2 ± 7.2

9.6 ± 6.1

-0.6 ± 4.7

0.643

10.9 ± 9.8

10.2 ± 11.1

-0.8 ± 2.9

0.339

0.929

5. Changes in anthropometric parameters

Results of changes in anthropometric parameters in the LSI and control groups before and after participating in the study are shown in Table 4. In a comparison of changes in anthropometric parameters before and after participating in this study, weight and BMI in the LSI group showed statistically significant decreases after participation (p = 0.024 and p = 0.024, respectively), and a significant difference was found in a comparison with the control group (p = 0.050 and p = 0.054, respectively). Members of the LSI group showed a statistically significant decrease in WC after participating in the study (p = 0.012). A statistically significant difference was found in a comparison with the control group (p = 0.048). The LSI group was associated with a statistically significant decrease in hip circumference before participation in the study (p = 0.033) and represented a tendency of a significant difference between two groups compared to the control group (p = 0.097). There was no statistically significant difference between these groups with respect to the other items (p > 0.05).

Table 4.

LSI group (n = 13)

Control group (n = 15)

p-value†

Baseline

Wk 4

Change value

p-value*

Baseline

Wk 4

Change value

p-value*

Body mass index (kg/m ²)

23.98 ± 4.43

23.71 ± 4.36

-0.28 ± 0.39

0.025

24.79 ± 2.84

24.79 ± 2.89

-0.01 ± 0.32

0.937

0.055

6. Changes in lipid profiles

The changes in lipid profiles between the LSI and control groups before and after participating in the study are presented in Table 4. In a comparison of levels of TC, total glycerides, LDL-C, HDL-C, Apo A1, and Apo B before and after participating in the study, the LSI group showed a statistically significant decrease in HDL-C four weeks after participation (p = 0.001) and a statistically significant difference was found in comparison with the control group (p = 0.022), although the difference disappeared after correcting the baseline value (p > 0.05). In addition, although the LSI group showed a significant decrease in Apo A1 levels four weeks after participation compared with before participation (p = 0.004), no statistically significant difference was evident in a comparison with the control group (p > 0.05). No statistically significant difference within LSI groups within and between the two groups with respect to the other items was observed (p > 0.05).

7. Changes in inflammatory indexes and GGT

Changes in the inflammatory indexes (hs-CRP, erythrocyte sedimentation rate [ESR]), and GGT in the LSI and control groups before and after participation are shown in Table 4. In a comparison of hs-CRP, ESR, and GGT before and after four weeks of participation, no statistically significant difference in the values within or between test groups was found (p > 0.05).

8.Correlation between genetic risk scores and parameters

Results of an analysis of the correlations among the changed levels of genotyping and glycemic control factor, anthropometric genotyping, blood-glucose index, anthropometric index, inflammatory index, and GGT concentrations are presented in Supplementary Table 3. In the CDKN2A/B genes, FPG, GA, and fat-free mass (FFM) were negatively correlated, but ESR and body fat mass (BFM) showed a positive correlation (p < 0.01). The G6PC2 gene was positively correlated with insulin, HOMA-IR, GGT, weight, BMI, BFM, FFM, and WC (p < 0.01). The GCKR gene showed a positive correlation with insulin, HOMA-IR, and HbA1c, and the GSR gene showed a positive correlation for ESR and WC. In addition, the DGKB gene exhibited a positive correlation for FMM, but the MTNR1B gene showed a negative correlation for FMM. In particular, WC was positively correlated with FPG (p < 0.05), insulin, HOMA-IR, HbA1c, hs-CRP, ESR, GGT, weight, BMI, BFM, percent body fat (PBF), FFM, and waist-to-hip ratio (WHR) (p < 0.001). GGT was positively correlated with FPG (p < 0.05), insulin, HOMA-IR, hs-CRP, weight, BMI, BFM, FFM, and WC (p < 0.001), but negatively corrected with GA (p < 0.05).

9.Effect of LSI intervention on validity indicators according to GCKR gene polymorphisms

The effects of participating for four weeks on blood glucose, anthropometric events, and lipid metabolism according to GCKR gene polymorphisms are presented in Fig. 2. No significant difference in blood-glucose indexes (FPG and PPG) was found between groups, genes, and treatment durations. Although HbA1c and GA levels were not associated with significant differences between groups, genes, and treatment durations, a significant difference between groups was seen in the genes (p > 0.05). In the case of the HbAlc, the TT (non-risk) subjects in the LSI group decreased from 7.5 ± 0.5 before participating in the study to 7.2 ± 0.5 after four weeks (p < 0.1). In the case of the GA, the TT (non-risk) subjects in the LSI group decreased significantly from 18.1 ± 0.9 before participating in the study to 16.2 ± 0.4 after four weeks (p < 0.02). HbA1c and GA levels showed large decreases compared with the other groups. No significant difference in the changes in anthropometric indexes according to GCKR gene polymorphism was found between groups in the correlation between groups, genes, and treatment durations. However, in the case of weight, BMI, and WC, the TT (non-risk) group in the LSI group exhibited a larger tendency for a decrease after participating than before participating (p < 0.1).

Fig. 2.

Glycated albumin, HbA1c, and Anthropometric index according to variants of GCKR. HbA1C: glycated haemoglobin A1c, GCKR: glucokinase (hexokinase 4) regulator.

10. Safety and adverse events

No significant changes or differences were observed among subjects with respect to safety indicators (laboratory tests and electrocardiogram results) (p > 0.05). A comparison of laboratory test items in the two test groups showed no significant clinical changes within the reference range even though there was a statistically significant difference between the two groups in the form of a decrease in WBC counts in the LSI group after four weeks of participation (p = 0.002) compared with values before participation. No statistically significant difference between the two groups was found for the other items (p > 0.05) ( Supplementary Table 4).

DISCUSSION

The purpose of this study was to explore the effects of blood-glucose control and health-promotion factors in terms of a genetic polymorphism when conducting customized counseling based on genetic information and an LSI program for management of diabetes. An RCT was conducted to provide guidelines and basic data for an optimal LSI program for DM in the future. In all subjects, more than 60% of seven genes ( CDKN2A/B, G6PC2, GCKR, GLIS3, MTNR1B, DGKB,and SLC30A8) of a total of eight blood glucose-related genetic markers showed variations. Correlation between diabetes and genes appeared to be high. In particular, the GCKR gene was most relevant to a blood glucose-related index. According to several prior studies [ 29- 33], GCKRvariability (CC and TC types) is known to increase the risk of type 2 DM and chronic kidney disease, and affect β-cell functions, as well as reduce energy metabolism rates in glucose, which can increase blood-glucose levels. Analysis and evaluation of GCKRgenotypes may help identify patients at high risk of metabolic disorders and may be valuable in developing personalized treatment plans for coping with a prognosis of metabolic risks [ 29]. The GRS for GCKR investigated in this study was closely correlated with blood glucose-related indexes (insulin, HOMA-IR, HbA1C, and GA), which are believed to help manage the metabolic risks of DM. In a study by Cheng et al. [ 34], the SNP in GCKRwas less relevant to fasting blood glucose but highly relevant to the indexes for insulin, HOMA-IR, and HbA1c in type 2 DM, a finding that was similar to ours. GGT and WC were closely correlated with blood glucose and obesity indicators. GGT was positively correlated with FPG, insulin, HOMA-IR, GA, weight, BMI, body fat, and WC (p < 0.05), and WC showed a positive correlation with insulin, HOMA-IR, HbA1c, weight, BMI, body fat, and FFM. Changes in GGT in the LSI group tended to decrease after participation compared with before participation. Serum GGT in a general population group was recently reported to exhibit a dose-response relationship with several risk factors for cardiovascular disease, although all were within normal ranges [ 35- 37]. Serum GGT concentration in particular is considered an independent predictor of chronic diseases and requires early management because it is closely related to the risk of type 2 DM and the mechanism of diabetes development [ 37- 39]. The increase in WC and GGT in patients with diabetes observed in this study is therefore relevant to cardiovascular disease risk factors, suggesting that control of blood glucose and weight are important. In fact, members of the LSI group showed significant reduction in WC, weight, and BMI compared with the control group, despite the lack of continuous feedback and maintaining daily life after participation in LSI program only twice, indicating that decreases in GA and HbA1c levels, which reflect the degree of blood glucose control, were achieved. Recently, Oh et al. [ 40] reported that Koreans have a much lower BMI level compared with Westerners, yet the DM prevalence rate in Korea is similar to that of Western countries because Koreans have less muscle and more visceral fat than Westerners. In particular, WHR can be a useful marker for predicting IFG and IGT because the visceral fat leads to liver or whole-body insulin resistance and is a major source of inflammatory cytokine and metabolic products [ 40]. The effects of differences in blood-glucose control and weight loss varied depending on the risk type of the GCKRgene variation. In other words, changes in GA and HbA1c levels, WC, BMI, and weight in the LSI group were associated with larger decreases in the non-risk (TT type) group compared with the risk group. It has been suggested that the GCKRgene variation risk group may find it more difficult to control blood glucose and weight compared with the normal (non-risk) group. In the future, therefore, it will be necessary to differentiate among subjects in LSI programs according to their genetic characteristics, and not only distinguish among different education methods, periods, and frequency, to identify those at high risk of diabetes at an early stage. Such distinctions may help establish active prevention and management plans through customized treatment and diagnosis of metabolic prognosis. In a meta-analysis on the intervention effects of weight control through lifestyle improvements in type 2 diabetes performed by Norris et al. [ 41], weight control was not effective as a pooled data analysis failed to distinguish among intervention methods, including education, counseling, diet, and exercise. When the intervention methods were analyzed separately, a combination of behavioral correction and exercise therapy through education showed effective results in both weight control and HbA1c levels. However, the effect was minimal when low-calorie diet control was performed alone. Significant differences in dietary intake, physical activity, and nutrient consumption among subjects who participated in the intervention could not be identified between groups. Therefore, participating twice in the LSI programs did not significantly change the factors involved in improving lifestyle, such as diet control and increased physical activity. Song et al. [ 42] reported a combination of appropriate intervention methods (education, counseling, exercise, diet, education and counseling, exercise and counseling, and education + diet + counseling) must be applied to increase the expected effectiveness of the LSI in diabetics because different effects can occur depending on the duration, frequency, and validity measurement method of the intervention.

There are several limitations to this study. First, due to the characteristics of the LSI program, the frequency of the application of the program twice only to a small number of diabetics in a short period of time, elements that were chosen to enable simultaneous recruitment and close management of the subjects did not contribute to significant changes in behavior, such as improved lifestyle habits. Second, the distribution ratio of the genetic polymorphism related to blood glucose in the allocation of the subjects represented significant differences between the test groups, which led to bias in the proportion of genetic variants (risk) in the control group. Third, while customized counseling using genetic information was applied to blood-glucose control, it failed not only to provide continuous feedback but also to comprehensively reflect the information of intestinal microorganisms in addition to diet and exercise. Fourth, GCKR is a well-known gene for type 2 DM and blood-glucose control, but because it targets a small number of diabetics, caution should be used when interpreting the results. However, despite these limitations, this study represents the first RCT in Korea of a personalized lifestyle modification program based on genetic information of the diabetics.

In the future research programs, effective management of diabetes will require increasing the numbers of applicants and participants by developing a standardized program and training of professionals. A machine-learning technique that was recently applied to the development of a technology that predicts glycemic index reactions by integrating and analyzing blood indexes, dietary habits, physical measurements, physical activity, and intestinal microbes suggests postprandial blood-glucose levels can be controlled through a diet tailored to the individual [ 43]. It will therefore be necessary to develop and differentiate an LSI program that applies a well-designed method to comprehensively explain all relationships between genetic-variation characteristics and environments (diet, physical activity, intestinal microbes, sleep, etc.) in individuals.

CONCLUSION

A four-week LSI program for diabetics successfully explore the positive effects on blood-glucose control and improvements in obesity indicators. In particular, the risk group with GCKR genetic variations was found to be more genetically affected, as measured by blood glucose and obesity indicators, compared with the non-risk (TT type) group. This suggests that variations due to the genetic polymorphism should be considered in future applications of LSI programs to diabetics.

Supplemental Materials

ACKOWLEDGEMENTS

We would like to thank all participating researchers and our study staff.

Footnotes

CONFLICTS OF INTERESTS
None to declare.

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Supplementary Materials

Articles from Journal of Lifestyle Medicine are provided here courtesy of Yonsei University Wonju College of Medicine

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