| Literature DB >> 30943956 |
Hsing-Yu Chen1,2,3,4, Heng-Chih Pan5,6, Yung-Chang Chen5,6, Yu-Chun Chen7, Yi-Hsuan Lin2,3,4, Sien-Hung Yang3,4, Jiun-Liang Chen3,4, Hau-Tieng Wu8,9,10.
Abstract
BACKGROUND: Diabetic nephropathy (DN) is a common complication of diabetes mellitus (DM) that imposes an enormous burden on the healthcare system. Although some studies show that traditional Chinese medicine (TCM) treatments confer a protective effect on DN, the long-term impact remains unclear. This study aims to examine end-stage renal disease (ESRD) and mortality rates among TCM users with DN.Entities:
Keywords: Chronic kidney disease; Diabetes mellitus; Diabetic nephropathy; End-stage renal disease; Mortality; Traditional Chinese medicine
Mesh:
Substances:
Year: 2019 PMID: 30943956 PMCID: PMC6448220 DOI: 10.1186/s12906-019-2491-y
Source DB: PubMed Journal: BMC Complement Altern Med ISSN: 1472-6882 Impact factor: 3.659
Fig. 1Flow chart of this study. (Abbreviations: DM: diabetes mellitus, DN: diabetic nephropathy, ESRD: End-stage renal disease, TCM: traditional Chinese medicine)
Comparable demographic features among TCM users and non-TCM users after 1:1 propensity score matching
| TCM users | TCM nonusers | Standardized mean difference | |||
|---|---|---|---|---|---|
| Gender | 0.068 | ||||
| Female | 14,455 | (42.0%) | 15,623 | (45.4%) | |
| Male | 19,986 | (58.0%) | 18,818 | (54.6%) | |
| Age (years) | −0.017 | ||||
| -20 | 24 | (0.1%) | 49 | (0.1%) | |
| 21–40 | 716 | (2.1%) | 1072 | (3.1%) | |
| 41–60 | 8429 | (24.5%) | 10,140 | (29.4%) | |
| 61- | 25,272 | (73.4%) | 23,180 | (67.3%) | |
| Insured level (NTD/month) | −0.068 | ||||
| 0–20,000 | 30,012 | (87.1%) | 28,832 | (83.7%) | |
| 20,001–40,000 | 2330 | (6.8%) | 3343 | (9.7%) | |
| 40,001– | 2099 | (6.1%) | 2266 | (6.6%) | |
| Geolocation | 0.030 | ||||
| 1 (more urban) | 8495 | (24.7%) | 8648 | (25.1%) | |
| 2 | 9400 | (27.3%) | 9630 | (28.0%) | |
| 3 | 5173 | (15.0%) | 5311 | (15.4%) | |
| 4 | 6273 | (18.2%) | 6058 | (17.6%) | |
| 5 | 1063 | (3.1%) | 1020 | (3.0%) | |
| 6 | 2157 | (6.3%) | 2028 | (5.9%) | |
| 7 (more rural) | 1880 | (5.5%) | 1746 | (5.1%) | |
| Previous TCM users | 868 | (2.5%) | 2962 | (8.6%) | −0.181 |
| Comorbidities | |||||
| Hypertension | 22,578 | (65.6%) | 21,575 | (62.6%) | 0.060 |
| Hyperlipidemia | 10,659 | (30.9%) | 10,979 | (31.9%) | −0.020 |
| Heart failure | 2147 | (6.2%) | 2030 | (5.9%) | 0.015 |
| IHD | 7123 | (20.7%) | 6867 | (19.9%) | 0.019 |
| CVD | 3143 | (9.1%) | 3016 | (8.8%) | 0.013 |
| Hyperuricemia | 4242 | (12.3%) | 4060 | (11.8%) | 0.017 |
| COPD | 4207 | (12.2%) | 4180 | (12.1%) | 0.002 |
| CCI | 4.2 | (1.9) | 4.0 | (2.0) | 0.088 |
| Modified DCSI score | 1.5 | (1.3) | 1.4 | (1.3) | 0.035 |
| Confounding drugs | |||||
| Diabetic drugs | |||||
| Insulin analogs | 3716 | (10.8%) | 3534 | (10.3%) | 0.018 |
| Biguanides | 19,275 | (56.0%) | 19,019 | (55.2%) | 0.015 |
| SU | 22,281 | (64.7%) | 21,990 | (63.8%) | 0.018 |
| Alpha-glucosidase inhibitors | 3597 | (10.4%) | 3577 | (10.4%) | 0.002 |
| TZD | 4560 | (13.2%) | 4597 | (13.3%) | −0.003 |
| Others | 2612 | (7.6%) | 2523 | (7.3%) | 0.010 |
| Lipid-lowering agent | |||||
| Statin | 8342 | (23.3%) | 8291 | (23.2%) | 0.005 |
| Fibrate | 4003 | (11.2%) | 4000 | (11.2%) | 0.000 |
| Others | 104 | (0.3%) | 90 | (0.3%) | 0.008 |
| Anti-hypertensives | |||||
| ACEi | 9369 | (27.2%) | 8976 | (26.1%) | 0.026 |
| ARB | 9511 | (27.6%) | 9029 | (26.2%) | 0.032 |
| α-blocker | 2653 | (7.7%) | 2452 | (7.1%) | 0.023 |
| β-blocker | 10,484 | (30.4%) | 10,181 | (29.6%) | 0.019 |
| CCB | 16,008 | (46.5%) | 15,137 | (44.0%) | 0.051 |
| Diuretics | 10,402 | (30.2%) | 9816 | (28.5%) | 0.038 |
| Vasodilator | 4640 | (13.5%) | 4464 | (13.0%) | 0.015 |
| Central-acting agent | 2653 | (7.7%) | 2452 | (7.1%) | 0.027 |
| Analgesics | |||||
| NSAID | 10,429 | (30.3%) | 10,839 | (31.5%) | −0.025 |
| COX-2 inhibitors | 1785 | (5.2%) | 1685 | (4.9%) | 0.014 |
| Acetaminophen | 8672 | (25.2%) | 9103 | (26.4%) | −0.028 |
| Aspirin | 11,949 | (34.7%) | 11,574 | (33.6%) | 0.023 |
Abbreviations: ACEi angiotensin converting enzyme inhibitor, ARB angiotensin II receptor blocker, CCB calcium channel blocker, CCI Charlson’s comorbidity index, COPD chronic obstructive pulmonary disease, COX-2 cyclooxygenase-2 inhibitor, DCSI Diabetes Complications Severity Index, NSAID nonsteroidal anti-inflammatory drug, NTD new Taiwan dollar, SU Sulfonylureas, TCM traditional Chinese medicine, TZD Thiazolidinediones
Incidence rates and risks of ESRD and mortality among TCM users and TCM nonusers
| Overall | TCM user | TCM nonuser | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Case | PY | Ia | Case | PY | Ia | Case | PY | Ia | aHR/aCSHRb (95% CI) | |
| Before matching | ||||||||||
| All-cause mortality | 32,201 | 703,192.6 | 47.4 | 14,048 | 458,457 | 30.6 | 18,153 | 244,735.7 | 74.1 | 0.48 (0.47–0.49)* |
| ESRD | 13,538 | 669,671.5 | 20.2 | 7363 | 439,654.1 | 16.7 | 6175 | 230,017.3 | 26.8 | 0.74 (0.72–0.77)* |
| After matching | ||||||||||
| All-cause mortality | 23,761 | 437,362.7 | 54.3 | 9173 | 232,106.2 | 39.5 | 14,588 | 205,256.5 | 71.1 | 0.48 (0.47–0.50)* |
| ESRD | 9407 | 414,628.6 | 22.7 | 4252 | 221,755.2 | 19.2 | 5155 | 192,873.3 | 26.7 | 0.81 (0.78–0.84)* |
Abbreviations: aCSHR adjusted cause-specific hazard ratio, aHR adjusted hazard ratio, ESRD end-stage renal disease, TCM traditional Chinese medicine;
*p-value <.001
aIncidence is presented as 1000 person-year (PY)
bAge, gender, geolocation, insurance level, comorbidities, medications, and previous TCM experience, were adjusted in the Cox regression model to evaluate the adjusted hazard ratio (aHR) for all-cause mortality. Age, gender, geolocation, insured level, comorbidities, medications, previous experience with TCM were fitted in the competing-risk regression to evaluate the adjusted cause-specific hazard ratio (aCSHR) for ESRD
Fig. 2Competing-risk analysis of the ESRD rate in the matched cohort, by TCM users and nonusers
Fig. 3Survival analysis of mortality rate in the matched cohort, by TCM users and nonusers
Mortality associated with the TCM users, by duration of TCM use and TCM use status
| Death | PY | Ia | Before matched | After matched | |||
|---|---|---|---|---|---|---|---|
| aHR$ (95% CI) | Sig. | aHR$ (95% CI) | Sig. | ||||
| Duration of TCM use (days) | |||||||
| TCM nonuser | 14,588 | 170,815.5 | 85.4 | 1 (reference) | 1 (reference) | ||
| ≤ 60 | 6144 | 114,886.8 | 53.5 | 0.55 (0.54–0.57) | *** | 0.56 (0.54–0.57) | *** |
| 61–120 | 1236 | 27,672.5 | 44.7 | 0.47 (0.44–0.49) | *** | 0.47 (0.44–0.50) | *** |
| 121–180 | 566 | 13,663.0 | 41.4 | 0.40 (0.37–0.43) | *** | 0.43 (0.39–0.46) | *** |
| 181–240 | 311 | 8969.5 | 34.7 | 0.36 (0.33–0.40) | *** | 0.36 (0.32–0.40) | *** |
| 241– | 916 | 32,473.4 | 28.2 | 0.29 (0.27–0.30) | *** | 0.29 (0.28–0.31) | *** |
| TCM use status | |||||||
| Never user | 10,699 | 127,149.0 | 84.1 | 1 (reference) | 1 (reference) | ||
| Former user | 3889 | 43,666.6 | 89.1 | 1.01 (0.98–1.05) | 1.02 (0.98–1.06) | ||
| Current user | 5530 | 113,624.6 | 48.7 | 0.49 (0.47–0.50) | *** | 0.49 (0.48–0.51) | *** |
| New user | 3643 | 84,040.59 | 43.3 | 0.47 (0.45–0.48) | *** | 0.47 (0.46–0.49) | *** |
*Significance: *p-value <.05; **p-value <.01; ***p-value <.001
aIncidence is presented as 1000 person-year (PY)
$Age, gender, geolocation, insured level, comorbidities, medications, previous TCM experience, were adjusted in the Cox regression model to evaluate the hazard ratio for all-cause mortality
Fig. 4Multivariate subgroup analysis for the impact of TCM use on all-cause mortality. Abbreviations as in Table 1. *Significance: *p-value <.05; **p-value <.01; ***p-value <.001. $Cox regression model with adjusted covariates, including age, gender, geolocation, insured level, comorbidities, medications, and previous TCM experience. Each covariate listed above was excluded from the subgroup analysis itself but included in the subgroup analysis with other covariates
Fig. 5Cumulative incidence of mortality stratified by TCM use and ESRD occurrence
Sensitivity analyses on risks of mortality among TCM usersa
| Model | aHR (95% CI)a | Sig. |
|---|---|---|
| Full cohort ( | 0.49 (0.48–0.50) | *** |
| Same study cohort with inverse probability weighting ( | 0.51 (0.49–0.52) | *** |
| Redefine TCM usersc | ||
| Excluding late TCM users ( | 0.48 (0.47–0.49) | *** |
| Excluding former TCM users | ||
| Cumulative duration ≥30 days ( | 0.40 (0.39–0.42) | *** |
| Cumulative duration ≥60 days ( | 0.37 (0.36–0.39) | *** |
| Cumulative duration ≥90 days ( | 0.35 (0.33–0.36) | *** |
*Significance: *p-value <.05; **p-value <.01; ***p-value <.001
aThe same covariates, including age, gender, geolocation, insured level, comorbidities, medications, and previous experience with TCM were adjusted in every model
bonly excluding renal transplantation patients and missing values
cTCM users were redefined as followings: all TCM users except patients who initiated TCM treatment 6 months before death/end of follow-up (late TCM users), or patients who used TCM longer than 30, 60, or 90 days. PSM was used to select baseline characteristics-matched cohorts