Methods pertaining to present risk surveys, including genetic factors, have been previously described [10,11,12].
This study took place in the city of Goto, which is on a remote island in western Japan. According to 2013 estimates from the National Institute of Population and Social Security Research in 2013, there were 16,264 residents aged 60–89 years in 2015 and 15,807 residents in that age group in 2020 .
The study population comprised 1388 individuals (501 men and 887 women) aged 60–89 years in Goto who participated in an annual health checkup in 2017. The local government conducted this annual checkup program. The program was directed by the Ministry of Health, Labour, and Welfare of Japan. Based on this annual health checkup, we performed an rs17081935-related survey to clarify the mechanism of aging, including the decrease in handgrip strength. The details of the present survey are described elsewhere .
Participants without serum data (n = 11) and SNP data (n = 3) were excluded from the analysis. Finally, 1374 older Japanese individuals (498 men and 876 women), with a mean age of 72.8 ± standard deviation (SD) of 7.1 years, were enrolled in the study.
The experimental protocols were reviewed by medical staff in meetings before the study to reduce inter-observer variability in the information collected from medical interviews and measurements. Trained interviewers obtained the medical history of the participants and information on drinking and smoking habits. Body weight and height with bare feet and while wearing light clothes were measured using an automatic body composition analyzer (BF-220; Tanita, Tokyo, Japan). Body mass index (BMI) was calculated as weight (kg)/height (m)2. BMI was categorized as low (< 18.0 kg/m2), normal (18.0–24.9 kg/m2), or high (≥ 25.0 kg/m2).
After at least 5 min of rest, systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured in the sitting position using a blood pressure measuring device (HEM-907; Omron, Kyoto, Japan). If the participants had high blood pressure measurements (SBP ≥ 140 mmHg or DBP ≥ 90 mmHg), we measured blood pressure again, and the lower blood pressure values were used. Hypertension was defined as SBP ≥ 140 mmHg, DBP ≥ 90 mmHg, or use of antihypertensive medication.
Fasting blood samples collected in siliconized tubes were used to measure low-density lipoprotein cholesterol (LDLc), high-density lipoprotein cholesterol (HDLc), and triglycerides (TG). Blood samples in sodium fluoride tubes were used to measure glycated hemoglobin (HbA1c). Measurements were obtained following standard laboratory procedures at SRL, Inc. (Tokyo, Japan). Dyslipidemia was defined as LDLc ≥ 140 mg/dL, HDLc < 40 mg/dL, TG ≥ 150 mg/dL, or lipid-lowering medication use. Diabetes was defined as HbA1 ≥ 6.5% or use of medication to lower glucose levels.
Blood samples in siliconized tubes were also used to measure creatinine. Glomerular filtration rate (GFR) was estimated using a recently adapted established method introduced by a working group of the Japanese Chronic Kidney Disease Initiative : GFR (mL/min/1.73 m2) = 194 × (serum creatinine [enzyme method]) −1.094 × (age) −0.287 × (0.739 for women). Mild reduced renal function was defined as GFR of 60–89 mL/min/1.73 m2. Chronic kidney disease was defined as GFR < 60 mL/min/1.73 m2.
Samples from the EDTA-2 K tube were used to measure erythrocyte count using an automated procedure at SRL, Inc. (Tokyo, Japan).
Genomic DNA was extracted from 2 mL of whole peripheral blood using Gene Prep Star NA-480 (Kurabo Industries Ltd., Osaka, Japan). Participants were genotyped for the rs17081935 SNP using the TaqMan SNP Genotyping Assay (C_33131398, Thermo Fisher Scientific, Tokyo, Japan) and the LightCycler 480 thermal cycling platform (Roche Diagnostics, Basel, Switzerland). In detail, genomic DNA was amplified using polymerase chain reaction (PCR) (first step, 95 degrees for 30 s; second step, 40 cycles between 95 degree for 5 s and 60 degrees for 30 s; third step, 50 degrees for 30 s) with two fluorogenic hydrolysis probes (VIC/FAM). Endpoint genotyping analysis was performed. No increments in fluorescence intensity were detected during PCR from the negative control wells, which did not contain any genomic templates.
Carotid intima-media thickness (CIMT) was measured based on ultrasonography of the left and right carotid arteries by experienced vascular technicians using LOGIQ Book XP with a 10-MHz transducer (GE Healthcare, Milwaukee, WI, USA). Maximum values for left and right common carotid CIMT were calculated with semi-automated digital edge-detection software (Intimascope; MediaCross, Tokyo, Japan); the protocol is described in detail elsewhere . Atherosclerosis was defined as CIMT ≥ 1.1 mm, as in our previous studies [17, 18].
Handgrip strength was determined with a handgrip dynamometer (Smedley; Matsumiya Ika Seiki Seisakujo, Tokyo, Japan) as grip strength from two measurements obtained for each hand; the maximum value for each hand was used. Reduced handgrip strength was defined as being in the lowest quintile of sex-specific handgrip strength values, < 25.6 kg for men and < 16.1 kg for women.
The characteristics of the study patients in relation to rs17081935 genotype are expressed as means ± SD for continuous values (age and height) and percentages for prevalence data. Significant differences involving the rs17081935 genotype were evaluated using analysis of variance.
By using analysis of covariance (ANCOVA), sex- and age-adjusted values for height by rs17081935 genotype were calculated and expressed as least mean square values (standard error).
Logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to determine the association between reduced handgrip strength and rs17081935 genotype. We also performed these analyses stratified by atherosclerosis status. Logistic regression was also used to determine the association between reduced handgrip strength and atherosclerosis.
Four adjusted models were used. Model 1 adjusted for sex and age. Model 2 adjusted for sex, age, and cardiovascular risk factors as potential confounders, namely, drinking status (none, often, daily), smoking status (never, former, current), BMI category (< 18.0 kg/m2, 18.0–24.9 kg/m2, ≥ 25.0 kg/m2), hypertension (no, yes), dyslipidemia (no, yes), and diabetes (no, yes). Model 3 adjusted for variables in model 2 plus height (cm). Drinking status, smoking status, BMI category, hypertension, dyslipidemia, and diabetes are known to affect the endothelium, but the condition of the endothelium could not be evaluated based on these factors in this study. Since atherosclerosis evaluated by CIMT and renal function are factors that directly indicate endothelium condition [19, 20] and endothelial repair activity might play a crucial role in maintaining muscle strength among older individuals , we also generated a model (model 4) that adjusted only for sex, age, height, and renal function evaluated by GFR category (< 60 mL/min/1.73 m2, 60–89 mL/min/1.73m2, and ≥ 90 mL/min/1.73 m2).
To validate the study population in the present study, the Hosmer–Lemeshow test for goodness of fit were performed. To evaluate the sex-specific correlation between BMI and height, simple correlation analysis was performed.
All statistical analyses were performed using SAS for Windows, version 9.4 (SAS Inc., Cary, NC, USA). Values of p < 0.05 were considered statistically significant.