Background/Aims: Prescription opioid use is increasing despite concerns about drug safety. We examined relationships between use of analgesics with biomarkers of chronic kidney disease (CKD) in a representative sample of adults in the United States (US). Methods: Participants (n=3980) were from the National Health and Nutrition Examination Survey (NHANES) 2009-2010. Use of any analgesic, prescription opioids, and NSAIDs were compared to referent groups with use of non-analgesic prescription medication or use of no prescription medication. CKD biomarkers including urine albumin-to-creatinine ratio (UACR) and serum-creatinine-based estimated glomerular filtration rate (eGFR; CKD Epidemiology Collaboration: CKD-EPI equation) were analyzed as continuous and binary variables (UACR ≥30 mg/g or eGFR <60 mL/min per 1.73m2; median splits). Results: Frequencies of use were: any prescription analgesic 12.7% (507/3980); prescription opioids 5.1% (204/3980); NSAIDs 5.6% (224/3980); non-analgesic medication 38.7% (1540/3980); no medication 48.6% (1933/3980). Prescription analgesic use (β=0.17, p=0.021) and opioid use (β=0.19, p=0.002) were associated with higher UACR values, while NSAID use was not (β=0.17, p=0.105). Prescription analgesic use was related to UACR ≥5.98 mg/g (median), (OR=1.34, 95%CI=1.01-7.79, p=0.045). No type of analgesic use was related to CKD-EPI eGFR. Conclusion: In a representative US population, prescription opioid use associated with higher albuminuria levels compared to non-opioid-users.

Twenty-six million adults in the United States (US), approximately 12 %, have chronic kidney disease (CKD) and many more are at an increased risk [1]. Analgesic use has been linked to higher risks of developing CKD [2,3,4,5] as well as progression to end-stage renal disease (ESRD) [6,7,8]. A form of progressive CKD, analgesic nephropathy, results from the habitual consumption of analgesics and has been recognized since the 1950's [9]. The specific agents involved are unclear, because many analgesics are composed of drug combinations. Opioids and non-steroidal anti-inflammatory drugs (NSAIDs) are common analgesics ingredients either alone or in combinations.

NSAIDs have been associated with acute kidney injury (AKI), with increasing evidence of their role in development and progression of CKD and ESRD [5,6,10,11,12]. Daily consumption of an analgesic drug for at least a month was reported by 14% of US participants 20 years of age and older in the 1999-2000 National Health and Nutrition Examination Survey (NHANES) [13]. However, in this NHANES sample, habitual non-opioid analgesic (aspirin, acetaminophen, and ibuprofen) consumption was not previously linked to albuminuria or reduced estimated glomerular filtration rate (eGFR) [14], contrasting other evidence suggesting increased risks of CKD development and/or progression with long-term NSAID use [5,6,10,11,12].

Use of analgesics is commonplace and increasing in parallel with the frequency of CKD. Notably, prescription opioid use is dramatically increasing in the US, with a rise in total opioid consumption from 50.7 to 126.5 million grams from 1997 to 2007 [15], and a per capita opioid use morphine equivalence increase of more than 7 times, from 96 mg of morphine per person in 1997 to 700 mg per person in 2007 [16]. Milligram per person use of prescription opioid analgesics increased 402% over the same timeframe [15]. Additionally, there was a 60% increase in the number of prescriptions for opioids dispensed by retail pharmacies from 2000 (131 million prescriptions) to 2010 (210 million prescriptions) [17]. Many concerns have been raised about trends in, and safety of, opioid use but the potential link to analgesic nephropathy or CKD has not previously been evaluated. Therefore, the purpose of this study was to determine relationships between use of prescription analgesics, including opioids and NSAIDs, with biomarkers of CKD in a representative sample of US adults.

Study design and population

NHANES 2009-2010 survey and examination data were collected by the Centers for Disease Control and Prevention (CDC) National Center for Health Statistics (NCHS) [18]. NHANES consisted of questionnaires given to participants' in their homes followed by a standard health examination. Data are drawn from a nationally representative sample of the US population [19]. NHANES data is released to the public every 2 years. While 2-year datasets can be combined to improve the stability of estimates, the 2009-2010 dataset [20] was chosen to map survey responses and laboratory values to current rates of prescription opioid use, a key consideration given the recent dramatic increase in use of these medications [17]. A detailed description of the four-stage sampling design, calculation of sampling rates for ethnic race, sex, age, and income status, and sample selection have been published elsewhere [19].

Study participants were asked about prescription medications ("In the past 30 days, have you used or taken medication for which a prescription is needed?"). Medication information (type, generic name, therapeutic class, and generic flag) was recorded by the interviewer and verified via a lookup function. Up to 20 medications were reported for each participant.

Based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) [21], Figure 1 demonstrates how the sample size for this study was determined. Inclusion criteria for participants from NHANES 2009-2010 were availability of data across prescription medication use, demographic information, CKD risk factors, and laboratory values for serum and urine creatinine and urine albumin. A priori exclusion criteria was past 12 month dialysis (either hemodialysis or peritoneal dialysis) because UACR and eGFR biomarkers are not reliable once kidney replacement therapy begins.

Fig. 1

Description of the NHANES 2009-2010 sample analyzed.

Fig. 1

Description of the NHANES 2009-2010 sample analyzed.

Close modal

Kidney function

The two most common CKD biomarkers were selected as primary outcome variables. Serum and urine creatinine were analyzed with the DxC800 modular chemistry side which is isotope dilution mass spectrometry (IDMS) Standardized [22]. The DxC800 modular chemistry side uses the Jaffe rate method (kinetic alkaline picrate). eGFR was calculated using the CKD Epidemiology Collaboration (CKD-EPI) equation [23].

The DcX800 method was used to measure albumin concentration [22]. Urine albumin-to-creatinine ratio (UACR) was then calculated. UACR and eGFR were analyzed as continuous and binary variables (UACR ≥30 mg/g or eGFR <60 mL/min per 1.73m2 by serum creatinine-based CKD-EPI; UACR ≥5.98 mg/g or eGFR <65.74 mL/min per 1.73m2 by serum creatinine- CKD-EPI, based on median splits).

Medication use

Prescription medication use was defined as use of any type of prescription (e.g., cardiovascular agents, metabolic agents, psychotherapeutic agents, gastrointestinal agents, etc.) reported in the medication questionnaire. Analgesics were classified as agents containing the following medications: analgesic combinations (Tramadol or Butalbital + caffeine, both in combination with acetaminophen), anti-migraine agents, COX-2 inhibitors and other non-steroidal anti-inflammatory drugs, miscellaneous analgesics, opioid analgesic combinations (all in combination with acetaminophen), opioid analgesics, and salicylates.

For this study, prescription NSAID use was defined as reported use of NSAIDs (COX-1 and COX-2 inhibitors) and/or salicylates. Prescription opioid use was defined as reported use of opioid analgesics and/or opioid analgesic combinations with acetaminophen. For participants reporting use of multiple prescription analgesics, additional categories were created: prescription opioid analgesic + NSAID, prescription opioid analgesic + non-NSAID, and NSAID + non-opioid analgesics. NSAID + non-opioid analgesic was classified as NSAID use; prescription opioid analgesic + non-NSAID was classified as prescription opioid analgesic use. Participants reporting any prescription analgesic across medications were categorized into the analgesic group in order to isolate and examine the analgesic relationships with the primary outcomes.

Participants reporting use of prescription opioid analgesics plus NSAIDs across multiple analgesics (opioid + NSAID) were 1) excluded from the separate NSAID and prescription opioid analgesic primary analyses, and then 2) added to the separate NSAID and prescription opioid analgesic categories for sensitivity analyses.

Covariates

Self-report demographic information was assessed including sex, age, poverty/income ratio, and ethnic race. Smoking status was defined as self-report use of any nicotine product in the past 5 days ("During the past 5 days, did you use any product containing nicotine including cigarettes, pipes, cigars, chewing tobacco, snuff, nicotine patches, nicotine gum, or any other product containing nicotine?"). Diabetes status was defined as self-report of having been told that they had diabetes ("Other than during pregnancy, have you ever been told by a doctor or health professional that you have diabetes or sugar diabetes?"), taking diabetic medication ("Are you now taking diabetic pills to lower your blood sugar? These are sometimes called oral agents or oral hypoglycemic agents."), taking insulin ("Are you now taking insulin?"), or having a serum glucose value of 126mg/dL or higher. Hypertension status was defined as self-report of having been told that they have antihypertension ("Have you ever been told by a doctor or other health professional that you had hypertension, also called high blood pressure?"), taking hypertensive medication ("Because of your high blood pressure/hypertension, are you now taking prescribed medicine?"), or having an average systolic value of ≥140 mm Hg or diastolic value of ≥90 mm Hg.

Statistical Analyses

Key demographic and clinical information was compared within prescription medication-use category via χ2 (gender, ethnic race, hypertension, diabetes, smoking status) and Mann-Whitney (age, poverty/income ratio) tests.

Multiple regression models controlling for sex, age, poverty/income ratio, ethnic race, diabetes, hypertension, and smoking status were used to independently examine the following comparisons, for UACR and eGFR, respectively: 1) use of any prescription analgesic versus use of other type of prescription medication or no prescription medication use, 2) use of prescription NSAIDs versus use of non-analgesic prescription medication and/or use of another type of prescription analgesic (including prescription opioid medications), or no prescription medication use, and 3) use of prescription opioid medication versus use of non-analgesic prescription medication and/or use of another type of prescription analgesic (including prescription NSAIDs), or no prescription medication use. Multiple linear regression was used when UACR and eGFR were analyzed continuously, and multiple logistic regression was used when UACR and eGFR were dichotomized based on clinical cut-points (UACR ≥30 mg/g or eGFR <60 mL/min per 1.73m2 by serum creatinine-based CKD-EPI) and median splits (UACR ≥5.98 mg/g or eGFR <65.74 mL/min per 1.73m2 by serum creatinine-based CKD-EPI).

Sensitivity analyses were conducted by including prescription opioids plus NSAIDs across multiple medications (opioid + NSAID) (including n = 58 that were excluded in the primary analysis), in the operationalization of prescription opioid analgesic use, and then in the operationalization of NSAID use. This approach allowed determination of stability of results when prescription opioid analgesic + NSAID combinations were allowed in the analyses.

Statistical analyses were performed in SPSS version 22 (IBM Corporation) using appropriate weights and strata for the complex sample strategy employed in NHANES 2009-2010. Missing data was assumed to be missing at random and therefore listwise deletion was used. UACR and eGFR were log-transformed to approximate normality.

Demographic characteristics

Study participants were 50.4% women, 44.1% white ethnic race, 18.7% African American, and 44+/-14 (mean+/-SD) years of age. Additionally, 14.1% had diabetes, 33.1% had hypertension, and 28.4% were current smokers.

Medication use and comparisons across demographic characteristics

Frequencies of use were participants with complete survey data were: any prescription analgesic 12.3% (606/4910); prescription opioid analgesics 4.3% (210/4910); NSAIDs 5.4% (266/4910); non-analgesic medication 37.6% (1844/4910); no medication 50.1% (2460/4910). Participants reporting prescription analgesics, NSAIDs, and opioids were older and had lower poverty-income ratios (Table 1). Women reported use of more prescription analgesics, NSAIDs, and prescription opioid analgesics compared to men. White participants reported greater use of prescription analgesics and prescription opioid analgesics, and less NSAIDs, compared to other ethnic race groups. Participants with diabetes and hypertension reported greater use of prescription analgesics, prescription opioid analgesics, and NSAIDs compared to those without diabetes and hypertension. Lastly, current smokers used more prescription analgesics and prescription opioid analgesics compared to non-smokers.

Table 1

Association between prescription analgesic, NSAID, and opioid use by key demographic characteristics

Association between prescription analgesic, NSAID, and opioid use by key demographic characteristics
Association between prescription analgesic, NSAID, and opioid use by key demographic characteristics

Relationships between prescription analgesic use and biomarkers of CKD

Frequencies of use for participants with full data for regression models were similar to the total sample: any prescription analgesic 12.7% (507/3980); prescription opioid analgesics 5.1% (204/3980); NSAIDs 5.6% (224/3980); non-analgesic medication 38.7% (1540/3980); no medication 48.6% (1933/3980). Prescription analgesic use (β = 0.17, p = 0.021) and prescription opioid analgesic use (β = 0.19, p = 0.002) were significantly associated with higher UACR values, while NSAID use was not (β = 0.17, p= 0.105, Table 2). No type of prescription analgesic use was related to UACR > 30 mg/g. When median splits of UACR were examined as dependent variables, prescription analgesic use was related to UACR ≥5.98 mg/g (median; OR = 1.34, 95%CI = 1.01-7.79, p = 0.045). No type of prescription analgesic use was related to eGFR, whether examined as either a continuous (Table 3) or categorical (eGFR <60 ml/min/1.73m2; median split eGFR <65.74 mL/min per 1.73m2, data not shown) variable. The most common prescription opioids reported were acetaminophen-hydrocodone (71/210; 34%), hydrocodone (27/210; 13%), acetaminophen-oxycodone (25/210; 12%), and oxycodone (24/210; 11%).

Table 2

UACR by Analgesic, NSAID, and Opioid Use

UACR by Analgesic, NSAID, and Opioid Use
UACR by Analgesic, NSAID, and Opioid Use
Table 3

eGFR by Analgesic, NSAID, and Opioid Use

eGFR by Analgesic, NSAID, and Opioid Use
eGFR by Analgesic, NSAID, and Opioid Use

Sensitivity analyses

Results from the sensitivity analyses indicated that results of the regression models, when dependent variables were continuous or dichotomized (based on clinical cut-points or median splits) did not change (data not shown) when adding prescription opioid analgesics + NSAIDs use across multiple medications, in the operationalization of prescription opioid analgesic use, and in the operationalization of NSAID use.

Among US adults, prescription opioid use associated with higher UACR values compared to non-users. Use of any analgesic, but not NSAIDs, associated with higher UACR indicating that prescription opioid analgesics likely drive this relationship. The relationship between prescription analgesic use and higher UACR was independent of CKD risk factors including sex, ethnic race, hypertension, diabetes, and current smoking status. However, the other biomarker of CKD, eGFR, was not related to use of analgesic medications.

Pain is a common symptom for patients with CKD and ESRD, yet little is known about analgesic-induced complications in people with kidney disease [24,25]. As such, most research examining the link between analgesic use and markers of CKD have focused on ESRD [7,8,26,27], and the role of NSAIDs has been emphasized [5,6,10,11,12]. Adverse effects of prescription opioid analgesics may be difficult to distinguish from some of the symptoms related to advanced CKD or dialysis treatment [26]. While relationships between non-opioid analgesic use, albuminuria and low eGFR were examined in a previous US representative sample [14] and in a study of women [28], previous studies have not examined relationships between use of prescription opioid analgesics and biomarkers of CKD. Given the large increase in prescription opioid analgesic use over the past decade, the present data uniquely uncover associations between use of these agents and higher UACR. Albuminuria is an early indicator of CKD that typically precedes loss of kidney function. The lack of association between prescription opioid analgesic use and eGFR probably reflects the relatively healthy study sample with few people having low eGFR. Longitudinal studies and studies of people with a wider range of eGFR, inclusive of a sufficient number with lower values, are needed to more fully elucidate relationships between prescription opioid analgesic use and overall kidney function. Additionally, longitudinal studies with larger sample sizes will are needed to determine if there are specific opioids, and related agents, that may be responsible for the overall relationship between opioids and albuminuria.

There is biological plausibility for opioids to have effects on the kidney. Most previously reported effects primarily relate to changes in water and electrolyte balance [29]. However, a recent study in mice reported morphine-induced albuminuria through compromised integrity of podocyte structure and the slit diaphragm of the glomerular basement membrane. In particular, effacement of podocyte foot processes was observed in this model [30]. As the present association of opioid use with albuminuria in humans is a novel finding, further experimental studies are needed to more clearly elucidate underlying mechanisms.

This study has both strengths and limitations worthy of note. First, it is a cross-sectional, observational study of a nationally-representative US sample that identified a newly discovered relationship between prescription opioid analgesic use and risk for higher UACR. However, while these sorts of observations can be used to generate hypotheses, they cannot be assumed to infer causality. Second, this sample of US adults had few with low eGFR and/or high UACR, and it may be that chronic pain or illness is driving the relationship between prescription opioid use and albuminuria. Third, exact dosages of NSAIDs and opioids in relation to biomarkers of CKD are beyond the scope of our analyses. Attempting to determine these relationships would lead to a large reduction in cell sizes within independent variables, and therefore, reduce statistical power to study such relationships. A determination of dosages of NSAIDs and opioids in relation to biomarkers of CKD is well-suited for a longitudinal cohort study of high-risk groups. However, the novel relationship between opioid use and albuminuria observed in this study identifies a signal requiring further study of risks for analgesic use. Fourth, reliance on self-report of prescription analgesic use is a limitation due to potential inaccuracy of recall and confounding by non-prescription analgesic use. However given that actual analgesic use (e.g. over-the-counter products such as NSAIDs, analgesic prescription misuse, or illicit analgesics) would only increase the prevalence of analgesic use, the observed relationship between use of prescription analgesics, particularly opioids, likely underestimates the strength of these relationships. Recall bias may also exist across prescription versus over-the-counter medications. However, over-the-counter medication use and recall bias should occur evenly across the medication use groups that comprised the independent variables. In addition, reason for prescription medication use was not captured in the dataset, and therefore therapeutic indications of the medications are not known. Finally, the NHANES measure of smoking status was use of any nicotine product in the past 5 days. While a narrowly defined measure, it still explained a significant amount of variance in UACR. Moreover, the bias would likely be to underestimate smoking exposure, which would be prone to underestimating the actual association.

Importantly, the present study shines a light on yet another possible opioid toxicity and sets the stage for new research to further examine opioid use in high-risk groups, such as patients with risk factors for CKD (e.g. diabetes, hypertension, and prevalent albuminuria/proteinuria) and/or prevalent CKD. Notably, albuminuria is also a risk factor for cardiovascular disease at levels even lower than those associated with CKD, which raises concern about broader unfavorable effects of opioid use [31,32].

Opioid use has long been associated with numerous adverse health consequences, and the present study adds higher levels of albuminuria as yet another risk related to use of these medications. Further studies are needed to understand the long-term consequences of prescription opioid analgesic use on the kidney, including evaluation of higher risk cohorts and longitudinal assessments of clinical event outcomes including progression to advanced stage CKD, ESRD, and cardiovascular complications.

Barbosa-Leiker and McPherson have received research funding from the Bristol-Myers Squibb Foundation. Tuttle has received consulting fees from Eli Lilly and Company, Amgen, and Noxxon Pharma, as well as research support from Eli Lilly and Company.

This project was supported in part by the Department of Justice, the Life Science Discovery Fund (Roll, PI).

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