Stages Of A Transtheoretical Model As Predictors Of The Decline in Estimated Glomerular Filtration Rate: A Retrospective Cohort Study

ABSTRACT 

Background: The transtheoretical model (TTM) is composed of multiple stages according to the patient's consciousness and is believed to lead people to realize the importance of healthier behaviors. We examined the association of TTM stages with the decline of the estimated glomerular filtration rate (eGFR). Methods: We used the annual health checkup data and health insurance claims data of the Japan Health Insurance Association in Kyoto Prefecture between April 2012 and March 2016. TTM stages of change were obtained from questionnaires at the first health checkup and categorized into six groups. The primary outcome was defined as a more than 30% decline in eGFR from the first health checkup. We fitted a multivariable Cox proportional-hazards model for time-to-event analyses adjusting for age, sex, eGFR, body mass index, blood pressure, blood sugar, dyslipidemia, uric acid, urinary protein, and existence of kidney diseases at first health checkup. Results: We analyzed 239,755 employees and the mean follow-up was 2.9 (standard deviation, 1.2) years. As compared with the stage 1 group, the risk of eGFR decline was significantly low in the stage 3 group (hazard ratio [HR] 0.77; 95% confidence interval [CI], 0.65–0.91); stage 4 group (HR 0.80; 95% CI, 0.65–0.98); and stage 5 group (HR 0.79; 95% CI, 0.66–0.95). 

Conclusion: Compared with the pre-contemplation stage (stage 1), the preparation, action, and maintenance stages (stages 3, 4, and 5), were associated with a lower risk of eGFR decline.

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INTRODUCTION 

Chronic kidney disease (CKD) has been a global health problem for many years, and its prevalence has reached approximately 10–15% worldwide among 500 million people.1 CKD progression is caused by many pathophysiological risks such as diabetes, hypertension, and systemic immune disorders.1 Recently, dietary and lifestyle modifications were found to affect renal function, so the Kidney Disease: Improving Global Outcomes (KDIGO) guideline now recommends that CKD patients should monitor and change their behaviors, including smoking cigarettes, their healthy weights, and daily physical activity.2 In fact, the evidence that being a past smoker compared with a current smoker was associated with a decreased risk of CKD progression3 indicates that behavior change might slow the disease progression. However, an issue would be that changing such healthy behaviors does not seem easy in clinical settings. Recently, some integrative theories of psychotherapies have evolved to address this issue.

The transtheoretical model (TTM) of behavior change is one of the integrative theories that divide common people into five categories based on temporal dimensions.4 In general, people move through five stages from pre-contemplation to contemplation, and then preparation, followed by action and maintenance stages when they change their behavior. Recent studies have documented that TTM-based intervention improved adherence with lipid-lowering or antihypertensive drugs,5,6 and promoted healthy eating, exercise, and other healthy behaviors in a randomized controlled trial.7

Nevertheless, it is important to understand the precise mechanisms by which behaviors affect renal function. The first step would be to determine whether each stage is associated with CKD progression. Here, we examined whether CKD progression is associated with TTM stages using the health checkup Japanese database.

METHODS 

Database and target populations We performed a retrospective analysis using annual health checkup data and health insurance claims data of employers in companies insured by the Japan Health Insurance Association in Kyoto prefecture, Japan. Annual health checkups of employees aged more than 35 years are mandatory until they lose their eligibility (eg, change jobs, move to another area, or die).

Inclusion and exclusion criteria of participants 

We recruited employees who were between 35 and 75 years old and had two or more health checkups from April 2012 to March 2016. We excluded those who had any kidney disease or missing data in the first health checkup. Questionnaires were acquired at each health checkup and contained information on prescribed drugs, healthy behaviors, and alcohol consumption. Kidney disease was defined by The International Classification of Diseases, 10th Revision codes, such as N00-08, I70, and Q61, in the claims data.

Baseline variables 

TTM stages of a change obtained from questionnaires at the first health checkup were categorized into six groups by the question: "Do you intend to improve your lifestyle habits of diet and exercise?: do not intend to take action in the foreseeable future, regarded as stage 1; intend to change in the next 6 months, regarded as stage 2; intend to take action in the immediate future until the next month, regarded as stage 3; made specific overt modifications in their lifestyles within the past 6 months, regarded as stage 4; a prevent relapse, but they did not apply change processes as frequently as people in action, regarded as stage 5; no answer to the question (missing data), regarded as "no concern". The lifestyle behavior changes, including smoking cessation, undertaking physical activity, and achieving a healthy weight 1 year after the first health check-up, were obtained from questionnaires on the next health check-up: Those who are "quitting smoking" mean those who answered "Yes" in the previous year and answered "No" in the present year to the question, "Are you a heavy smoker? (A heavy smoker refers to those who have smoked a total of over 100 cigarettes or have smoked for 6 months and have been smoking during the past month.)". Those who are "Undertaking physical activity" mean those who answered "No" in the previous year and answered "Yes" in the present year to the question, "Are you in a habit of doing exercise to sweat lightly for over 30 minutes a time, two times weekly, for over a year?". "Decrease in the amount of drinking" is determined according to the question, "How much do you drink per day?". "Decrease in the frequency of drinking" means an answer to the question "How often do you drink? (sake, shochu, beer, wine, whisky, or brandy, etc)". The contents of the questionnaires were created by reference to "Standard medical checkup and health guidance programs" by the Japanese government: Ministry of Health, Labor, and Welfare.8

The covariates were classified into groups as follows: four groups based on age (35–45, 46–55, 56–65, and 66 or more years); four groups based on body mass index (BMI; thin: ≤18.5 kg=m2 , normal: 18.5–25 kg=m2 , pre-obesity: 25–30 kg=m2 , and obesity: >30 kg=m2 ) according to the World Health Organization; five groups based on eGFR (≤15, 30–15, 45–30, 60–45, and >60 mL=min=1.73 m2 ); three groups based urinary protein (using dipsticks: positive (≥1+), trace (±), and negative), abdominal circumference (if male ≥85 cm, female ≥90 cm); five groups based on blood pressure (systolic blood pressure [SBP] ≥180 mm Hg or diastolic blood pressure [DBP] ≥110 mm Hg without drugs, SBP ≥160 mm Hg or DBP ≥100 mm Hg without drugs, SBP ≥140 mm Hg or DBP ≥90 mm Hg without drugs, normal without drugs, and with drugs); and four groups based on dyslipidemia (triglyceride ≥150 mg=dL or high-density lipoprotein cholesterol <40 mg=dL was defined to be abnormal with hypolipidemic drugs, abnormality without hypolipidemic drugs, normal with hypolipidemic drugs, normal without hypolipidemic drugs), diabetes (fasting blood sugar ≥110 mg=dL or hemoglobin A1c ≥5.6% was defined to be as abnormality with antidiabetic drugs, abnormality without antidiabetic drugs, normal with antidiabetic drugs, normal without antidiabetic drugs), and hyperuricemia (defined uric acid ≥8 mg=dL without drugs or with use of anti-hyperuricemias). The information for each medication use was extracted from questionnaires.

Statistical analysis

The primary outcome for survival analysis was defined as a decrease of 30% or more in eGFR.9 The eGFR was calculated by the equation used by the Japanese Society of Nephrology.10 Patients were followed until the outcome or censored.

The Cox proportional-hazards model was used for time-to-event analyses to estimate the hazard ratios (HRs); a 95% confidence interval (CI) was used for the primary outcome. Follow-up period data for patients were censored on the date of the last health checkup. The analysis used two types of models: model 1 (without medication factors), adjusted for age, sex, BMI, abdominal circumference, eGFR, and urinary protein; and model 2 (with medication factors), adjusted for age, sex, BMI, abdominal circumference, eGFR, urinary protein, blood pressure, blood sugar, dyslipidemia, and uric acid. All the covariates were detected at the first health checkup. Schoenfeld residuals were used to check the proportional hazards assumption. A two-sided significance level of 0.05 was used, and all analyses were conducted using R version 3.4.1 (R Foundation for Statistical Computing, Vienna, Austria).

Subgroup analyses were performed for the model 2 condition, where the analysis population was stratified by employees 1) whose eGFR categorized as >60, 60–45, or ≤45 mL=min=1.73 m2 and 2) who did not attend hospital because of diabetes (no medication to reduce blood sugar or insulin injection), and 3) who met 1 or more criteria for Japanese metabolic syndrome.11

Sensitivity analyses were also performed for the model 2 condition, where we excluded from the analysis population 1) employees aged 60 or more years or 2) employees who had taken any medication for hypertension, diabetes, or dyslipidemia. The former employees were excluded because retirement is more likely to occur 60–65 years in Japan, which may cause healthy workers bias, and we minimized the impact of employees lost to follow-up.