| Literature DB >> 32587866 |
Masami Yoshioka1, Yoshifumi Okamoto2, Masahiro Murata2, Makoto Fukui3, Shizuko Yanagisawa3, Yasuhiko Shirayama3, Kojiro Nagai3, Daisuke Hinode3.
Abstract
Oral health status is known to be associated with lifestyle-related diseases such as diabetes and chronic kidney disease. In Japan, around 40% of hemodialysis cases are patients with diabetic nephropathy. The aim of this study was to clarify the association between oral health status and diabetic nephropathy-related indices in Japanese middle-aged men. Sixty-six men (age range: 55-64 years) with ≥20 remaining teeth and who received public medical checkups and oral examinations were enrolled. We examined correlations of age, body mass index, HbA1c, HDL-C, LDL-C, neutral fat, serum creatinine, and the estimated glomerular filtration rate (eGFR) with the number of remaining teeth or the community periodontal index (CPI) score (periodontal pocket < 4 mm: 0, 4-6 mm: 1, ≥6 mm: 2). A positive correlation between the CPI score and serum creatinine and a negative correlation between CPI score and eGFR (Spearman's rank correlation coefficient, r = 0.459, p < 0.01, and r = -0.460, p < 0.01, respectively) were observed. The mean eGFR in the CPI score 0 group was significantly higher than that in the CPI score 1/2 group (82.6 vs. 70.7, Student's t-test, p < 0.01). Logistic regression analysis using eGFR as a dependent variable and age, CPI score, body mass index, HbA1c, and neutral fat as independent variables suggested that low eGFR (<60) could be attributed to CPI score (OR = 3.169, 95% CI: 1.031-9.742, p = 0.044). These results suggest a possible association between periodontal status and renal function in Japanese middle-aged men. Periodontal condition is controlled by oral prophylaxis, and periodontal disease and chronic kidney disease have some common risk factors. Thus, periodontal management can contribute to the prevention of severe chronic kidney disease.Entities:
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Year: 2020 PMID: 32587866 PMCID: PMC7303739 DOI: 10.1155/2020/4042129
Source DB: PubMed Journal: J Diabetes Res Impact factor: 4.011
Characteristics of study subjects (n = 66).
| Min | Max | Mean | SD | |
|---|---|---|---|---|
| Age | 55 | 64 | 61.6 | 2.5 |
| Number of remaining teeth | 20 | 32 | 26.8 | 2.5 |
| Body mass index (kg/m2) | 18.4 | 38.0 | 24.1 | 3.5 |
| Serum creatinine (mg/dl) | 0.59 | 1.20 | 0.83 | 0.14 |
| HbA1c (%) | 4.9 | 9.8 | 5.84 | 0.79 |
| Estimated GFR (ml/min/1.73 m2) | 47 | 108 | 74.5 | 12.9 |
| Serum HDL cholesterol (mg/dl) | 31 | 107 | 59 | 16 |
| Serum LDL cholesterol (mg/dl) | 61 | 202 | 127 | 27 |
| Neutral fat (mg/dl) | 35 | 497 | 135 | 77 |
Distribution of study subjects by CPI score (n = 66).
|
| % | |
|---|---|---|
| CPI: 0 | 21 | 31.8 |
| CPI: 1 | 27 | 40.9 |
| CPI: 2 | 18 | 27.3 |
Distribution of study subjects by eGFR (n = 66).
|
| % | |
|---|---|---|
| 90≦ | 7 | 10.6 |
| 60-89 | 49 | 74.2 |
| ≦59 | 10 | 15.2 |
Spearman's correlation coefficients (r) between age and factors associated with metabolic syndrome and CPI score or number of teeth (n = 66).
| CPI score (0, 1, 2) | Number of remaining teeth | |
|---|---|---|
| Age | -0.005 | -0.052 |
| 0.969 | 0.681 | |
| Body mass index | -0.031 | -0.160 |
| 0.805 | 0.198 | |
| Serum creatinine |
| -0.008 |
|
| 0.949 | |
| HbA1c (%) | -0.001 |
|
| 0.993 |
| |
| Estimated GFR |
| 0.009 |
|
| 0.945 | |
| Serum HDL cholesterol | 0.090 |
|
| 0.474 |
| |
| Serum LDL cholesterol | -0.170 | 0.032 |
| 0.173 | 0.796 | |
| Neutral fat | -0.219 | -0.034 |
| 0.078 | 0.784 |
Upper: Spearman's correlation coefficients (r). Lower: p value.
Figure 1Comparison of eGFR between the CPI score 0 group and the CPI score 1/2 group (Student's t-test, ∗∗p < 0.01).
Factors associated with low eGFR (<60 ml/min/1.73m2) according to binomial logistic regression analysis (n = 66).
| Variable | OR | 95% CI |
| |
|---|---|---|---|---|
| Age | 1.493 | 0.902-2.469 | 0.119 | |
| CPI (0, 1, 2) |
|
|
| |
| Body mass index | 0.885 | 0.651-1.203 | 0.434 | |
| HbA1c | 0.423 | 0.071-2.527 | 0.346 | |
| Neutral fat | 1.005 | 0.994-1.016 | 0.350 |
Binomial logistic regression analysis was conducted using each of five variables as the dependent variable.