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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Viral Hepat. Author manuscript; available in PMC 2009 November 27.
Published in final edited form as:
PMCID: PMC2784594

Predictors of insulin resistance among Hispanic adults infected with or at risk of infection with the human immunodeficiency virus and hepatitis C virus


Both the human immunodeficiency (HIV) and hepatitis C (HCV) viruses have been associated with insulin resistance (IR). However, our understanding of the prevalence of IR, the underlying mechanisms and predisposing factors is limited, particularly among minority populations. We conducted a study of 333 Hispanic adults including: 76 HIV-monoinfected, 62 HCV mono-infected, 97 HIV/HCV co-infected, and 98 uninfected controls with a specific focus on HCV infection and liver injury as possible predictors of IR. IR was measured using the QUICKI index. The majority (55% to 69%) of participants in all groups had QUICKI values <0.350. Body mass index was associated with IR in all groups. Triglycerides were associated with IR in the uninfected control group only (−1.83, SE = 0.58, p=0.0022). HCV was associated with IR in participants infected with HIV (−0.012, SE = 0.0046, p=0.010). Liver injury, as measured by FIB-4 score, was significantly associated with IR independently of HCV infection (−0.0035, SE=0.0016, p=0.027). In the HIV/HCV co-infected group, treatment with nucleoside reverse-transcriptase inhibitors plus non-nucleoside reverse-transcriptase inhibitors (−0.021, SE=0.080, p=0.048), but not protease inhibitors (−0.000042, SE=0.0082, p=0.96) was associated with IR. HCV infection and antiretroviral agents, including NRTI plus NNRTI treatment are contributors to IR in HIV infection. Liver injury, as measured by the FIB-4 score, is a predictor of IR independently of HCV infection.

Keywords: QUICKI, insulin resistance, HIV, HCV, Hispanic, antiretroviral therapy, liver disease


In the United States, 15 to 30% of HIV-infected persons are also co-infected with HCV [1]. Both infections disproportionately affect minorities, including Hispanics, now the largest minority group in the US [24]. In 2002, HIV was the third leading cause of death among Hispanic men, and the fourth leading cause of death among Hispanic women aged 35 to 44 years [5]. HIV infection accelerates the impact of HCV on liver disease [69], and liver disease is an important cause of death among persons with HIV infection [1012]. Hispanics appear to be more susceptible than other racial groups to HCV-related liver disease progression [1317], and though the underlying mechanisms are not well understood, diabetes has been implicated [15].

There is strong evidence that HCV infection is associated with type 2 diabetes [1823]. Population data from the Third National Health and Nutrition Examination Survey (NHANES III) showed that HCV-infected persons 40 years of age and older are more than three times as likely as those without HCV to have type 2 diabetes after other important risk factors are considered, including age, body mass index (BMI), and a history of drug or alcohol abuse [22]. A recently published longitudinal study in Taiwan showed that HCV infection was an independent predictor of diabetes, especially in persons with a high BMI [23], establishing that HCV infection precedes the development of diabetes. HCV has been associated previously with insulin resistance (IR) [24, 25], but many studies have lacked data on the role of liver injury.

In the general population, established predictors of IR include age, race/ethnicity and obesity. In persons with HIV, in addition to HCV co-infection, there are other risk factors that may promote IR, such as antiretroviral therapy [2630], elevated trigycerides [31, 32] and body fat changes, including visceral fat accumulation and peripheral fat atrophy [3336]. It is possible that Hispanics are more susceptible to these factors than other racial/ethnic groups because of an underlying predisposition to IR [3740].

In this context, we conducted a study to determine the factors associated with IR among Hispanic adults of Caribbean origin infected with or at risk of HIV/HCV infection. The aims of the present study were: 1) to identify independent predictors of IR in relation to HIV/HCV infection status in this racial/ethnic group, 2) to assess the independent effect of HCV infection on IR and, 3) to determine the role of liver injury on IR.


Study Population and Data Collection

The data presented here were derived for secondary analyses from the BIENESTAR study, an open enrollment prospective cohort study designed to examine the role of drug use as a cofactor in HIV-related nutritional and metabolic disorders in Hispanic adults. The cohort is comprised of four groups: HIV-infected current drug users, HIV-infected persons who do not use drugs (‘non-drug users’), HIV-uninfected current drug users and healthy Hispanic controls. Recruitment was done through community outreach on the street, in homeless shelters, support groups, and health clinics. Self-identified Hispanic adults (≥18 years) who spoke Spanish as their first language were considered eligible. Most participants were of Puerto Rican origin, an ethnic group that is a mix of European (predominant), Black and Amerindian race [41]. The exclusion criteria were: pregnant at recruitment, non-HIV associated malignancies, the use of hormones for a sex change, and refusal to sign a consent form to release medical records. IR was not the main outcome of the BIENESTAR study, but fasting insulin and glucose values were collected for descriptive purposes. The data presented here were from the first study visit for each participant at which fasting insulin and glucose values were available. All participants gave informed consent, and were paid a modest stipend for their participation. The study was approved by the Institutional Review Board at the Tufts Medical Center.

Study interviews were conducted in Spanish by bilingual, Hispanic personnel. Data collected by standardized interview included detailed information on housing, HIV history, HIV medications, and health behaviors including smoking, alcohol consumption, and drug use. Participants were asked to fast for a minimum of five hours prior to the clinic visit, and were asked not to drink alcohol or do strenuous physical exercise for a period of 48 hours prior to the visit. Body weight and height were measured by standardized techniques. BMI was calculated as weight in kilograms divided by height in square meters.

Data were available on waist circumference, suprailliacal, and triceps skin folds in 115 HIV-infected participants. These data were used to conduct limited analyses on the role of fat distribution as a predictor of insulin resistance among HIV-infected participants. Central obesity was identified as a waist circumference of >102 cm in men and >88 cm in women [42]. To distinguish visceral adiposity with subcutaneous lipoatrophy from central, subcutaneous obesity [43], visceral adiposity was defined as the presence of central obesity combined with a suprailiac skin fold thickness of <2.0 cm (an a priori and arbitrary cutoff). There were four HIV-infected participants (4%) and one HIV-uninfected participant with visceral adiposity using this definition. This was too low a prevalence to reliably examine visceral adiposity in the analyses. Triceps skin fold was examined as a measure of extremity fat and treated as a continuous variable in the analyses. Skin fold thicknesses were measured in triplicate and the mean of the three measures was used in the analyses.

Insulin resistance

Fasting plasma glucose was determined by the hexokinase enzymatic method (Sigma Diagnostics, St. Louis, MO) and insulin levels were determined by radioimmunoassay (ICN Biomedical Inc., Costa Mesa, CA) with CV of 5%. IR was determined using the Quantitative Insulin Sensitivity Check Index –QUICKI, calculated as, 1/[log(I0) + log(G0)], where I0 is the fasting insulin, and G0 is the fasting glucose [44]. QUICKI correlates as well or better with IR measured by the euglycemic hyperinsulinemic clamp compared to fasting insulin using the Homeostasis Model Assessment of insulin resistance (HOMA-IR) [44, 45]. Mean QUICKI levels in normal populations are reported to range from 0.37 to 0.39 [44, 45], and QUICKI tends to decrease with increasing IR. QUICKI values were used on a continuous scale or dichotomized at the cutoff of 0.350, based on previous studies [44, 45, 46]. Here, for simplicity, IR is used to signify lower QUICKI values.

HIV disease and related variables

Self-reported HIV status was confirmed by enzyme immunoassay (Genetic Systems ™ HIV1 /HIV2 Plus O EIA, Biorad Laboratories, Redmond, WA). HIV RNA levels were measured by reverse transcriptase polymerase chain reaction (PCR) using a Roche Amplicor Monitor (Roche Molecular Systems, Somerville, NJ), with a lower detection limit of 400 copies/mL. An undetectable viral load was given a value of 200 copies/mL, the midpoint between zero and the limit of detection. CD4 cell counts were determined using a specific monoclonal antibody and fluorescence-activated cell sorting analysis.

Antiretroviral regimens

Since there were not enough data to examine individual antiretroviral agents, antiretroviral treatment was treated as a binary variable (yes/no). Therapy was defined as the use of any protease inhibitor (PI), or nucleoside reverse-transcriptase inhibitor (NRTI). These classes were not mutually exclusive. All participants who were taking a NRTI were also taking a NNRTI so these agents could not be evaluated separately. Some (rare) PI users were on PI monotherapy. The duration of use of the class of agent was also examined.

Hepatitis C

Hepatitis C virus (HCV) infection was determined qualitatively by the presence of HCV RNA in serum (AMPLICOR Hepatitis C Virus test, Version 2.0 Roche Molecular Systems Inc., Branchburg, NJ, USA). The number of years with HCV infection was estimated from the reported first use of injection drugs. The presence of hepatitis B virus surface antigen was determined using the HBsAg Enzyme Immunoassay 3.0 ( Biorad Laboratories, Redmond, WA, USA). Data from six participants with positive serum tests for HBsAg were set aside to focus the study on HCV infection.

Liver Injury

The liver function tests aspartamine aminotransferase (AST) and alanine aminotransferase (ALT) were routinely collected, but as minor elevations are frequent, liver injury was defined by the FIB-4 score [47], calculated as: (age [yr] x AST [U/L]) / ((PLT [109/L]) x (ALT [U/L]1/2)), where PLT corresponds platelet count. FIB-4 values > 3.25 are consistent with significant fibrosis at a sensitivity of 86 to 97% [47, 48]. Elevated values of the FIB-4 score in the range of 1.45 to 3.25 may not indicate fibrosis, but have been associated with other sorts of liver biopsy abnormalities including necro-inflammatory activity [47]. FIB-4 was used on a continuous scale in these analyses since we were interested in all sorts of liver injury, including drug toxicity or other processes that can lead to elevated AST or ALT and ultimately, fibrosis. Data for the FIB-4 was collected in the BIENESTAR study beginning September, 2006. Of the 333 participants included in this analysis, 80 (24%) were missing data for FIB-4 score for the reason that the measure had not yet been added to the protocol at the time of the visit.

Statistical Analyses

Statistical analyses were carried out using SAS version 9.1 (SAS Inc., Chicago, IL). Mean and standard deviation (SD) was reported for normally distributed continuous data, median (25th, 75th percentile) for non-normally distributed continuous data, and number and percentage for categorical data. Viral load, insulin and serum triglyceride values were log transformed for analyses. The data were analyzed together, and stratified by HIV/HCV infection status. Differences in participant characteristics in Table 1 were tested with the least square means option in the general linear models procedure (PROC GLM) in SAS for continuous, normally distributed and log transformed data. The Wilcoxon rank sum test was used for non-normally distributed data. Chi-square or Fisher’s exact test was used to test between-group differences for categorical variables. Univariate associations between independent variables of interest and QUICKI were determined with Pearson’s coefficient of correlation for continuous, normally distributed and log transformed continuous variables or by independent groups t-test for categorical variables (Table 2). Multivariate least squares regression models were used to examine independent determinants of QUICKI expressed on the continuous scale to enhance statistical power (Table 3Table 5). Adjustment was made for known predictors of IR including age, gender, body mass index, and serum triglycerides. Lifestyle factors including smoking, alcohol consumption and drug use were also evaluated as predictors of IR. HIV disease-related factors including years with HIV, current CD4 count, nadir CD4 count, viral load, and antiretroviral therapy were considered. Variables were included in the multivariate models either because of their known or suspected association with insulin resistance or based on an observed univariate association with QUICKI at a P<0.10. To assess the independent effect of HCV infection and liver injury on IR in HIV/HCV and HIV mono-infected participants separately, the overall study sample was re-stratified into two groups based on HIV infection status (Table 4). Multivariate analyses were used to examine the independent association between HCV infection and QUICKI while adjusting for the variables described above. The data from all groups were then combined to examine the effect of FIB-4 on QUICKI in the subset of 253 subjects for whom FIB-4 data were available. FIB-4 was added to a model with HCV infection to determine if the addition of a variable describing the presence of liver injury would be an independent predictor of QUICKI or would alter the size of the effect estimate for HCV (Table 5). It was not possible to include a term for years with HIV infection in the model since it is undefined for the HIV-uninfected. To evaluate the influence of antiretroviral therapy on QUICKI in this model which included HIV-uninfected subjects, dummy variables combining HIV status plus antiretroviral treatment use were coded as follows: HIV-infected plus PI use; HIV-infected plus NRTI/NNRTI use; HIV-infected plus no antiretroviral use; HIV-uninfected (reference category).

Table 1
Subject characteristics stratified by HIV/HCV infection status
Table 2
Univariate predictors of QUICKI in 333 study participants a
Table 3
Multivariate analyses: predictors of QUICKI stratified by HIV/HCV infection status
Table 4
Effect of HCV infection as a predictor of QUICKI a
Table 5
Effect of FIB-4 as a predictor of QUICKI a


Subject characteristics stratified by HIV/HCV infection status

Subject characteristics stratified by HIV/HCV infection status are shown in Table 1. There were significant between-group differences in age, gender, as well as smoking and drinking habits. Differences in rates of drug use were due to the recruitment strategy. The mean BMI in all groups fell into the overweight category [49], and was highest in the HIV/HCV uninfected (hereafter termed “uninfected” group). Trigycerides were significantly higher in the HIV-infected groups compared to the HCV-mono infected or uninfected groups (p<0.001 for all differences), but not significantly different from each other (p=0.57). Mean QUICKI values for all groups were below 0.350. The average QUICKI level was significantly lower in the HIV/HCV co-infected group compared to each of the other three groups (p<0.05 for all comparisons), but when QUICKI was examined on a categorical scale, based on the 0.350 cutoff, the difference in prevalence of insulin resistance among the groups was not significant. Average FIB-4 scores were highest in the HIV/HCV co-infected group. Of the 19 participants with FIB-4 >3.25, a value consistent with significant fibrosis, 16 (84%) were HIV/HCV co-infected and 3 (16%) were HCV mono-infected. HIV/HCV co-infected participants had a longer history of HCV and HIV infection than the HCV or HIV-mono infected participants.

Univariate predictors of IR

Table 2 shows the results for univariate associations of IR and the independent variables of interest in the 333 study participants combined as one group. The univariate associations between QUICKI and continuous variables were expressed as correlation coefficients, while for categorical variables, the univariate associations were expressed as a between-group difference in mean QUICKI values. IR was associated with age, body mass index, serum triglycerides, FIB-4 score, HIV-infection and years with HIV infection. Non-drinkers were more IR than drinkers.

Multivariate predictors of IR stratified by HIV/HCV infection status

In independent multivariable analyses for each infection group (Table 3), higher BMI was significantly associated with IR in all four groups. Higher trigycerides were associated with IR only in the uninfected group (p=0.0022). In this group alone, current drinking was associated with less IR. Any use of antiretroviral therapy was associated with IR in the HIV/HCV co-infected group, but not in the HIV-mono-infected group. No other variables, including years with HIV infection, CD4 cell counts or nadir CD4 cell count were significant predictors of IR.

The term for antiretroviral therapy was replaced in the models with a term describing either use of PI or NRTI plus NNRTI agents in independent models for each class of antiretroviral agent. The use of a PI or NRTI plus NNRTI was not associated with IR in the HIV mono-infected group. In the HIV/HCV co-infected group, IR was associated with the use of NRTI plus NNRTI (−0.021, SE=0.010, p=0.048), but not with the use of PI (−0.00042, SE=0.0082, p=0.96). The duration of use of the agent was not significant for either use of NRTI plus NNTRI or use of PI. Of the 173 HIV-infected participants, 49 (28%) were not on any form of antiretroviral therapy. Of the 124 on antiretroviral therapy, 121 (98%) were on a NRTI agent. The NRTI agents used were as follows: AZT, 39 (32%); 3TC, 71 (59%); D4T, 26 (21%) DDI, 16 (13%). Five of the 173 HIV-infected participants were using indinavir.

Effect of HCV infection on IR

Since the stratification used in Table 3 did not allow the assessment of chronic HCV infection as a predictor of IR, the data were re-stratified into two groups: HIV-infected and HIV-uninfected (Table 4). The HIV-infected group included HIV-mono-infected and HIV/HCV co-infected participants, and the HIV-uninfected group included HCV-mono-infected and uninfected participants. Multivariate analyses were run separately in each group with the addition of a term for HCV to the covariates assessed above. Chronic HCV infection was a significant predictor of IR in the HIV-infected (p=0.019), but not in the HIV-uninfected participants (p=0.26). BMI and triglycerides were also significant predictors IR in both groups.

The effect of liver injury on IR

To examine the effect of HCV and liver injury on QUICKI, the data from the 253 persons for whom FIB-4 data were available were analyzed in models with HCV that included or excluded FIB-4 (Table 5). FIB-4 was a significant predictor of QUICKI in the multivariate analyses, and the addition of FIB-4 to the model with HCV attenuated the effect estimate for HCV by about 23%. However, the term for HCV remained significant in the model that included a term for FIB-4.

The role of extremity fat as a predictor of IR

Greater triceps skinfold thickness was associated with IR (r =−0.15, p=0.046) in univariate analyses using data from all groups. In the multivariate analyses shown in Table 3, triceps skin fold was substituted for BMI in models for the HIV-mono-infected (n=52 with triceps skin fold data available) and HIV/HCV co-infected participants (n=60 with triceps skin fold data available). Greater triceps skin fold was associated with IR in the HIV/HCV co-infected group (−0.0020, SE=0.00094, p=0.036), but was not associated with IR in the HIV-mono-infected group (p=0.13) when other variables, including antiretroviral therapy, were considered in multivariate analyses.


The majority of participants in the BIENESTAR cohort were frankly IR as defined by a QUICKI value <=0.350 [4446]. However, the prevalence of IR was not significantly higher in HIV-infected participants compared with the uninfected controls, and HIV-infection was not an independent predictor of IR when adjusted for other variables in multivariate analyses. These results agree with a previous study conducted by our group in the Nutrition for Healthy Living Cohort [50], in which the rate of IR in HIV-infected participants was 51%, a prevalence that was not significantly different from matched controls derived from the NHANES III study (47%) in analyses adjusted for known predictors of IR. Brown et al. found in the MACS cohort higher rates of elevated insulin among HIV-infected men compared to HIV-uninfected control subjects after adjusting for a variety of risk factors including antiretroviral therapy [51]. In the present study no other HIV-related factors were associated with IR. Other studies have shown a weak association of insulin resistance with higher concurrent or nadir CD4 count [52, 53].

In addition to BMI and trigycerides, both of which are recognized risk factors for IR in the general population, predictors of IR in this population included HCV infection and the use of NRTI plus and NNRTI agents, but not PI. The ability of HCV to impact IR was apparent only in the HIV co-infected participants. In this group alone, antiretroviral therapy appeared to be an additional contributor to IR, independently of HCV. These two factors could explain why IR was more common in the HIV/HCV co-infected group compared to the HIV-mono infected group, despite the lower average BMI and triglyceride levels in the HIV/HCV co-infected group. There was no evidence that the effects of HCV infection and antiretroviral therapy act synergistically, though the absence of an effect of antiretroviral therapy in the HIV-mono-infected patients is suggestive. This study may have lacked the statistical power necessary to detect such an effect. Our results confirm previous reports of an effect of HCV on IR [24, 25, 42].

The role of liver injury in IR, induced either by HCV or by antiretroviral therapy [53], is not clear. In a multivariate model, both FIB-4 and HCV were independent predictors of IR. The addition of FIB-4 to the model with HCV attenuated the size of the effect estimate for HCV, but only by about 23%, and the term for HCV infection remained significant. These data support the hypothesis that HCV affects IR by a mechanism that is independent of HCV-induced liver damage [54, 55]. Studies conducted in a chimpanzee model suggest interplay between cellular lipid metabolism and HCV replication [56].

Our results showed an independent effect of antiretroviral therapy on IR in the HIV/HCV co-infected participants although there was no association of IR with PI in this study. Since PI therapy was associated with IR early in its use [57, 58], it is likely that this was specific to select PI agents, such as indinavir, rather than to the class as a whole. NRTI and NNRTI class agents have also been associated with IR [50, 51, 59, 60]. The elevation of serum lactate by NRTI has been proposed as an explanatory mechanism for NRTI-related IR [59]. Lactate levels were not collected in this study. Our data do not support an important role for peripheral lipoatrophy in IR. This result agrees with that of Meininger and colleagues [61] who found that low extremity fat was a predictor of IR only among HIV-infected men with lipodystrophy, but not among HIV-infected men without lipodystrophy. It also agrees with a recent study by Blumer et al., showing NRTI-related IR may develop in persons showing no alterations in body composition [60].

This study has numerous strengths. The participants were recruited from the community as opposed to a clinic-based sample, and therefore the data should be representative of the target population, and without reference to the presence of self-perceived lipodystrophy. A healthy control group was included in this study and evaluated by the same team using the same measures. The study was restricted to Hispanics, which gives the study high internal validity. Factors associated with HCV infection, which have been lacking in previous studies, including liver injury, HIV infection, drug use and other lifestyle factors were measured and evaluated in these analyses.

Limitations of the study include the cross-sectional design, which cannot establish the temporal relationship between HCV infection and IR. IR was measured using a surrogate and not the euglycemic hyperinsulinemic clamp technique [62], considered the gold standard, which may have resulted in some misclassification. The extent of liver injury was not determined using liver biopsies. Finally, the generalizability of our findings may be limited given that only Hispanic subjects were included.

In conclusion, the prevalence of frank IR is high in this group of Hispanic adults, but not higher in person infected with HIV. Our findings contribute to a growing body of evidence that HCV has an important role in IR in the context of HIV. This study also suggests that the effect of HCV on IR is independent of liver injury. Antiretroviral therapy including NRTI agents appears to promote IR in HIV-infected Hispanics independently of obvious alterations in fat distribution. Further studies are needed to clarify the underlying mechanisms. Since HIV, HCV and IR disproportionately affect Hispanics in the US, physicians should routinely monitor this high-risk population, including HCV-infected patients with no apparent liver disease, and encourage exercise, weight control and other interventions that promote insulin sensitivity. Other culturally appropriate interventions, including dietary interventions, to reduce IR in this population are needed.


The authors gratefully acknowledge the BIENESTAR study participants and staff for their time and dedication.

C.C.S. contributed to the analyses, interpretation of the data and writing of the manuscript.

O.I.B contributed to the interpretation of the data and the preparation of the manuscript.

C.W. contributed to the interpretation of the data and the preparation of the manuscript.

J.E.F. contributed to the study design, data collection, analyses, interpretation of the data and writing of the manuscript.


Declaration of funding interests

This study was supported by the National Institute on Drug Abuse (DA11598 and DA14501), the National Institute of Diabetes & Digestive & Kidney Diseases (DK4 5734-07), the Center for AIDS Research (1-P308142853) and the Center for Metabolic Research on HIV and Drug Use (5 P30 DA013868-02). The General Research Center of the Tufts Medical Center, Boston, is supported by the Division of Research Resources of the National Institutes of Health (M01-RR00054).

Authors' declaration of personal interests

Author's declaration of personal interests: Carmen Castaneda-Sceppa

  1. Carmen Castaneda-Sceppa has not served as a speaker, consultant or an advisory board member, and has not received research funding from an organization that could represent a conflict of interest.
  2. Carmen Castaneda-Sceppa is an employee of Northeastern University.
  3. Carmen Castaneda-Sceppa does not own stocks or shares.
  4. Carmen Castaneda-Sceppa does not own any patents.

Author's declaration of personal interests: Odilia I. Bermudez

  1. Odilia I. Bermudez has served as a speaker, a consultant and an advisory board member for [NONE], and has received research funding from [NIH (NIA and NIDDK), Massachusetts Vitamins Settlement Funds and International Nutrition Foundation].
  2. Odilia I. Bermudez is an employee of Tufts University.
  3. Odilia I. Bermudez owns stocks and shares in [NONE].
  4. Odilia I. Bermudez owns patent [NONE].

Author's declaration of personal interests: Christine Wanke

  1. Christine Wanke has served as a speaker, a consultant and an advisory board member for Merck, Abbott and Solvay Pharmaceuticals, is a scientific advisor for Thera Technology and Pfizer and has received research funding from Reliant, Merck, Thera-Technologies, Abbott, BMS and NIH.
  2. Christine Wanke is an employee of Tufts University.
  3. Christine Wanke owns stocks and shares in [NONE].
  4. Christine Wanke owns patent [NONE].

Author's declaration of personal interests: Janet E Forrester

  1. Janet E Forrester has served as a speaker, a consultant and an advisory board member for [NONE], and has received research funding from [NIH (NIDA)].
  2. Janet E Forrester is an employee of Tufts University.
  3. Janet E Forrester owns stocks and shares in [NONE].
  4. [Janet E Forrester] owns patent [NONE].


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