G M Reaven

Stanford Medicine, Stanford, California, United States

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Publications (571)3652.99 Total impact

  • Danit Ariel, Gerald Reaven
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    ABSTRACT: This study was based on the hypothesis that: (1) coronary heart disease (CHD) risk is accentuated in the insulin-resistant subset of persons with normal glucose tolerance (NGT) or prediabetes (PreDM); (2) the prevalence of insulin resistance, and associated abnormalities, is greater in subjects with PreDM; and (3) insulin resistance is the major contributor to increased CHD risk in these individuals.
    Acta Diabetologica 10/2014; · 3.68 Impact Factor
  • Ki-Chul Sung, Gerald Reaven, Sun Kim
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    ABSTRACT: Aim The plasma concentration ratio of triglyceride (TG)/high-density lipoprotein cholesterol (HDL-C) has identified increased cardio-metabolic risk and outcome in European populations. The goal of this study was to see if this ratio would also have clinical utility in identifying cardio-metabolic risk in an East Asian population. Methods Measurements of various cardio-metabolic risk factors, including coronary calcium scores, were available on 12,166 apparently healthy Korean adults. Approximately 25% of men and women with the highest TG/HDL-C ratios were classified as being at high cardio-metabolic risk, and their risk factor profiles compared to the remainder of the population, as well as to individuals with the metabolic syndrome (MetS). Results High cardio-metabolic risk (upper 25%) was defined as a TG//HDL-C ratio ≥3.5 (men) or ≥2.0 (women), and all cardio-metabolic risk factors measured, including coronary calcium scores, were significantly more adverse when compared to individuals beneath these cut-points. Although cardio-metabolic risk profiles appeared reasonably comparable in subjects identified by either a high TG/HDL-C or a diagnosis of MetS, use of the TG/HDL-C increased the numbers at high risk. Conclusion Evidence that determination of the plasma TG/HDL-C concentration ratio provides a simple way to identify individuasl at increased cardio-metabolic risk has been extended to an East Asian population. The ability of an elevated TG/HDL-C ratio to accomplish this goal is comparable to that achieved using the more complicated MetS criteria.
    Diabetes Research and Clinical Practice 07/2014; · 2.54 Impact Factor
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    ABSTRACT: Salsalate treatment has been shown to improve glucose homeostasis, but the mechanism remains unclear. The aim of this study was to evaluate the effect of salsalate treatment on insulin action, secretion, and clearance rate in nondiabetic individuals with insulin resistance.
    Diabetes Care 07/2014; 37(7):1944-50. · 8.57 Impact Factor
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    ABSTRACT: Background and Aims To evaluate the effects of 14 weeks of liraglutide plus modest caloric restriction on lipid/lipoprotein metabolism in overweight/obese persons with prediabetes. Methods and Results Volunteers with prediabetes followed a calorie restricted diet (-500 Kcal/day) plus liraglutide (n=23) or placebo (n=27) for 14 weeks. The groups were similar in age (58±7 vs. 58±8 years) and body mass index (31.9±2.8 vs. 39.1±3.5 kg/m²). A comprehensive lipid/lipoprotein profile was obtained before and after intervention using Vertical Auto Profile (VAP). Weight loss was greater in the liraglutide group than in the placebo group (6.9 vs. 3.3 kg, p<0.001), as was the fall in fasting plasma glucose concentration (9.9 mg/dL vs. 0.3 mg/dL, p<0.001). VAP analysis revealed multiple improvements in lipid/lipoprotein metabolism in liraglutide-treated compared with placebo-treated volunteers, including decreases in concentrations of total cholesterol, low-density lipoprotein cholesterol and several of its sub-classes, triglyceride, and non-high-density cholesterol. The liraglutide-treated group also had a significant shift away from small, dense low-density lipoprotein-particles, as well as decreases in apolipoprotein B concentration and ratio of apolipoprotein B/apolipoprotein A-1. There were no significant changes in the lipoprotein profile in the placebo-treated group. Conclusion Treatment with liraglutide plus modest calorie restriction led to enhanced weight loss, a decrease in fasting plasma glucose concentration, and improvement in multiple aspects of lipid/lipoprotein metabolism associated with increased cardiovascular disease (CVD) risk. The significant clinical benefit associated with liraglutide-assisted weight loss in a group at high risk for CVD – obese/overweight individuals with prediabetes – as seen in our pilot study, suggests that this approach deserves further study.
    Nutrition Metabolism and Cardiovascular Diseases 06/2014; · 3.88 Impact Factor
  • G. Reaven
    Journal of Internal Medicine 03/2014; · 5.79 Impact Factor
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    ABSTRACT: Liraglutide can modulate insulin secretion by directly stimulating beta cells or indirectly through weight loss and enhanced insulin sensitivity. Recently, we showed that liraglutide treatment in overweight individuals with prediabetes (impaired fasting glucose and/or impaired glucose tolerance) led to greater weight loss (-7.7% vs -3.9%) and improvement in insulin resistance compared with placebo. The current study evaluates the effects on beta cell function of weight loss augmented by liraglutide compared with weight loss alone. This was a parallel, randomised study conducted in a single academic centre. Both participants and study administrators were blinded to treatment assignment. Individuals who were 40-70 years old, overweight (BMI 27-40 kg/m(2)) and with prediabetes were randomised (via a computerised system) to receive liraglutide (n = 35) or matching placebo (n = 33), and 49 participants were analysed. All were instructed to follow an energy-restricted diet. Primary outcome was insulin secretory function, which was evaluated in response to graded infusions of glucose and day-long mixed meals. Liraglutide treatment (n = 24) significantly (p ≤ 0.03) increased the insulin secretion rate (% mean change [95% CI]; 21% [12, 31] vs -4% [-11, 3]) and pancreatic beta cell sensitivity to intravenous glucose (229% [161, 276] vs -0.5% (-15, 14]), and decreased insulin clearance rate (-3.5% [-11, 4] vs 8.2 [0.2, 16]) as compared with placebo (n = 25). The liraglutide-treated group also had significantly (p ≤ 0.03) lower day-long glucose (-8.2% [-11, -6] vs -0.1 [-3, 2]) and NEFA concentrations (-14 [-20, -8] vs -2.1 [-10, 6]) following mixed meals, whereas day-long insulin concentrations did not significantly differ as compared with placebo. In a multivariate regression analysis, weight loss was associated with a decrease in insulin secretion rate and day-long glucose and insulin concentrations in the placebo group (p ≤ 0.05), but there was no association with weight loss in the liraglutide group. The most common side effect of liraglutide was nausea. A direct stimulatory effect on beta cell function was the predominant change in liraglutide-augmented weight loss. These changes appear to be independent of weight loss. ClinicalTrials.gov NCT01784965 FUNDING: The study was funded by the ADA.
    Diabetologia 12/2013; · 6.88 Impact Factor
  • Sun H Kim, Gerald Reaven
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    ABSTRACT: Context:The possibility that differences in insulin sensitivity explain why women, especially younger women, have a lower cardiovascular disease (CVD) risk than men remains an unsettled issue.Objective:The objective of this study was to evaluate whether sex disparities in CVD risk are associated with differences in insulin resistance.Design/Setting/Participants:This was a cross-sectional study of women (n = 468) and men (n = 354) who had the measurement of CVD risk factors and steady-state plasma glucose (SSPG) concentration (insulin resistance) using the insulin suppression test. The population was also divided by median age (51 y) to evaluate the effect of age on sex differences.Main Outcome Measures/Results:In general, the SSPG concentration was similar between sexes. At higher BMI (≥30 kg/m(2)), women had significantly lower SSPG concentration than men (sexBMI interaction, P = .001). However, sex differences in CVD risk factors were not due to differences in SSPG but accentuated by a higher degree of insulin resistance in younger (age < 51 y) but not older (≥ 51 y) individuals. In younger individuals, women had significantly (P ≤ .007) lower diastolic blood pressure and fasting glucose and triglyceride concentration compared with men in SSPG tertile 3 (most insulin resistant) but not in tertile 1 (least insulin resistant). Older women had lower diastolic blood pressure compared with men, regardless of SSPG. High-density lipoprotein cholesterol remained higher in women, regardless of age or SSPG.Conclusions:The female advantage is not due to a difference in insulin action but results from an attenuation of the relationship between insulin resistance and CVD risk, especially in younger individuals.
    The Journal of Clinical Endocrinology and Metabolism 09/2013; · 6.31 Impact Factor
  • Gerald Reaven
    The Lancet 07/2013; 382(9887):126-127. · 39.21 Impact Factor
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    ABSTRACT: OBJECTIVE The aim was to evaluate the ability of liraglutide to augment weight loss and improve insulin resistance, cardiovascular disease (CVD) risk factors, and inflammation in a high-risk population for type 2 diabetes (T2DM) and CVD.RESEARCH DESIGN AND METHODS We randomized 68 older individuals (mean age, 58 ± 8 years) with overweight/obesity and prediabetes to this double-blind study of liraglutide 1.8 mg versus placebo for 14 weeks. All subjects were advised to decrease calorie intake by 500 kcal/day. Peripheral insulin resistance was quantified by measuring the steady-state plasma glucose (SSPG) concentration during the insulin suppression test. Traditional CVD risk factors and inflammatory markers also were assessed.RESULTSEleven out of 35 individuals (31%) assigned to liraglutide discontinued the study compared with 6 out of 33 (18%) assigned to placebo (P = 0.26). Subjects who continued to use liraglutide (n = 24) lost twice as much weight as those using placebo (n = 27; 6.8 vs. 3.3 kg; P < 0.001). Liraglutide-treated subjects also had a significant improvement in SSPG concentration (-3.2 vs. 0.2 mmol/L; P < 0.001) and significantly (P ≤ 0.04) greater lowering of systolic blood pressure (-8.1 vs. -2.6 mmHg), fasting glucose (-0.5 vs. 0 mmol/L), and triglyceride (-0.4 vs. -0.1 mmol/L) concentration. Inflammatory markers did not differ between the two groups, but pulse increased after liraglutide treatment (6.4 vs. -0.9 bpm; P = 0.001).CONCLUSIONS The addition of liraglutide to calorie restriction significantly augmented weight loss and improved insulin resistance, systolic blood pressure, glucose, and triglyceride concentration in this population at high risk for development of T2DM and CVD.
    Diabetes care 07/2013; · 7.74 Impact Factor
  • G M Reaven
    Diabetologia 05/2013; · 6.88 Impact Factor
  • A Liu, G M Reaven
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    ABSTRACT: BACKGROUND AND AIMS: The metabolic syndrome (MetS) has been shown to predict coronary heart disease (CHD). Non-high-density lipoprotein cholesterol (non-HDL-C) is also known to predict CHD, and recent evidence indicated non-HDL-C was able to predict MetS in adolescents. The study aim was to determine whether non-HDL-C serves as a useful metabolic marker for MetS in adults. METHODS AND RESULTS: Fasting non-HDL-C measurements were obtained in 366 non-diabetic adults not on lipid-lowering therapy. In addition to traditional non-HDL-C cut-points based on Adult Treatment Panel III guidelines, receiver-operating characteristic curve analysis was used to identify an optimal cut-point for predicting MetS. A secondary goal was to assess the relationship between non-HDL-C and insulin resistance, defined as the upper tertile of steady-state plasma glucose concentrations measured during the insulin suppression test. Prevalence of MetS was 40% among participants. Those with MetS had higher mean non-HDL-C (4.17 ± 1.0 vs 3.70 ± 0.85 mmol/L, p < 0.001), and the upper vs lower tertile of non-HDL-C concentrations was associated with 1.8-fold increased odds of MetS (p < 0.05). Traditional non-HDL-C cut-points ≥4.14 and ≥4.92 mmol/L demonstrated respective sensitivities 46% and 24% (specificities 72% and 89%) for identifying MetS. The optimal non-HDL-C cut-point ≥4.45 mmol/L had sensitivity 39% (specificity 82%). Comparable results were observed when non-HDL-C was used to identify insulin resistance. CONCLUSION: While MetS was associated with increased non-HDL-C, an effective non-HDL-C threshold to predict MetS in adults was not identified. Dyslipidemic nuances may explain why non-HDL-C may be less useful as a metabolic marker for MetS and/or insulin resistance than for CHD.
    Nutrition, metabolism, and cardiovascular diseases: NMCD 01/2013; · 3.52 Impact Factor
  • Gerald Reaven
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    ABSTRACT: The goal of this review was to summarize evidence supporting the view that insulin resistance/compensatory hyperinsulinemia play an important role in the pathogenesis of coronary heart disease (CHD) in nondiabetic individuals. Results of case-control and epidemiological studies in nondiabetic individuals will be reviewed to examine the link between insulin resistance/compensatory hyperinsulinemia, associated abnormalities, and CHD. The primary focus of the review will be on the central role that dyslipidemia plays in the link between insulin resistance/compensatory hyperinsulinemia and CHD. Additional issues to be addressed include the following: (1) the relationship among obesity, insulin resistance, and CHD; (2) a listing of other abnormalities that contribute to risk of CHD in insulin-resistant individuals; and (3) discussion of the importance of differential tissue insulin sensitivity in the development of abnormalities that increase CHD risk in insulin-resistant, nondiabetic individuals. The information will reflect the author's decision as to what issues are believed to be of particular relevance or less well appreciated concerning the complex relationship between insulin resistance and CHD. Resistance to insulin-mediated glucose disposal and hyperinsulinemia is a common finding in apparently healthy individuals and is associated with a number of abnormalities that greatly increase risk of CHD.
    Arteriosclerosis Thrombosis and Vascular Biology 08/2012; 32(8):1754-9. · 6.34 Impact Factor
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    G Reaven
    Journal of Internal Medicine 09/2011; 270(6):600-1; author reply 602-3. · 5.79 Impact Factor
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    ABSTRACT: Differences in insulin regulation of free fatty acids (FFAs) are not readily apparent at the same insulin concentrations used to differentiate relative insulin-mediated glucose disposal. Resistance to insulin-mediated glucose disposal and higher daylong FFA concentrations occur more commonly in obese individuals. However, the relationship between the ability of insulin to suppress FFA release from adipose tissue and stimulate glucose disposal in muscle has not been clearly defined in this population. The current study was initiated to test the hypothesis that these 2 facets of insulin action are related, with greater defects in insulin-mediated glucose disposal associated with less effective insulin inhibition of FFA release from adipose tissue. Subjects included 56 healthy nondiabetic overweight/moderately obese women classified as insulin resistant or insulin sensitive based on whole-body glucose disposal. All underwent a modified 240-minute 2-stage insulin infusion with basal (∼15 µU/mL) and physiologically elevated (∼80 µU/mL) steady-state insulin concentrations. Plasma glucose, insulin, FFA, and glycerol were measured throughout. Whereas plasma glucose differed most during physiological hyperinsulinemia in insulin-resistant vs insulin-sensitive subjects, plasma FFA/glycerol differed most during basal insulin concentrations. The FFA concentrations during the basal insulin steady state correlated highly (r = 0.85, P < .001) with glucose concentrations during the hyperinsulinemic steady state. Overweight/moderately obese women exhibit dramatic differences in the ability of insulin to suppress plasma FFA, which correlate highly with differences in insulin-mediated glucose disposal. Variability in insulin regulation of FFA is most apparent at basal insulin concentrations, whereas differences in glucose disposal are most apparent during physiologic hyperinsulinemia. Both can be quantified using a simple 2-stage insulin infusion study, with first-stage FFA concentrations and second-stage glucose concentrations being most informative.
    Metabolism: clinical and experimental 08/2011; 60(12):1741-7. · 3.61 Impact Factor
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    G M Reaven
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    ABSTRACT: The diagnostic category of the metabolic syndrome (MetS) has received considerable attention over the last decade, and prestigious organizations continue to strive for its incorporation into medical practice. This review has three goals: (i) summarize the history of the several attempts to define a diagnostic category designated as the MetS; (ii) question the aetiological role of abdominal obesity in the development of the other components of the MetS; and (iii) evaluate a diagnosis of the MetS as an effective way to identify apparently healthy individuals at increased risk to develop cardiovascular disease (CVD) or type 2 diabetes (2DM). The most important conclusion is that the MetS seems to be less effective in this population than the Framingham Risk Score in predicting CVD, and no better, if not worse, than fasting plasma glucose concentrations in predicting 2DM.
    Journal of Internal Medicine 02/2011; 269(2):127-36. · 5.79 Impact Factor
  • Sun H Kim, Gerald Reaven, Steven Lindley
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    ABSTRACT: C-reactive protein (CRP) is an inflammatory marker associated with obesity, insulin resistance, and cardiovascular disease. A recent study found CRP levels to be higher in individuals treated with certain antipsychotic medications such as olanzapine; however, it is not clear whether this is associated directly with drug intake or indirectly with drug-associated weight gain and insulin resistance. The objective of this study was to explore the potential predictors of CRP including insulin resistance, components of the metabolic syndrome, psychiatric diagnosis, and antipsychotic medication in patients treated with antipsychotics. Sixty-four outpatients without diabetes being treated with a single second generation antipsychotic medication had direct measurements of insulin resistance at the end of a 180-min infusion of glucose, insulin, and octreotide (insulin suppression test) as well as components of the metabolic syndrome. Insulin resistance was the strongest predictor of CRP (r=0.52, P<0.001). When adjusted for insulin resistance, there was no significant relationship between CRP and any of the components of the metabolic syndrome criteria, specific drug treatment or psychiatric diagnoses. In conclusion, insulin resistance is strongly associated with CRP levels and likely contributes to earlier associations between CRP and certain antipsychotic treatments.
    International clinical psychopharmacology 01/2011; 26(1):43-7. · 3.35 Impact Factor
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    ABSTRACT: We recently reported that a preponderance of small adipose cells, decreased expression of cell differentiation markers, and enhanced inflammatory activity in human subcutaneous whole adipose tissue were associated with insulin resistance. To test the hypothesis that small adipocytes exhibited these differential properties, we characterised small adipocytes from epididymal adipose tissue of Zucker Obese (ZO) and Lean (ZL) rats. Rat epididymal fat pads were removed and adipocytes isolated by collagenase digestion. Small adipocytes were separated by sequential filtration through nylon meshes. Adipocytes were fixed in osmium tetroxide for cell size distribution analysis via Beckman Coulter Multisizer. Quantitative real-time PCR for cell differentiation and inflammatory genes was performed. Small adipocytes represented a markedly greater percentage of the total adipocyte population in ZO than ZL rats (58±4% vs. 12±3%, p<0.001). In ZO rats, small as compared with total adipocytes had 4-fold decreased adiponectin, and 4-fold increased visfatin and IL-6 levels. Comparison of small adipocytes in ZO versus ZL rats revealed 3-fold decreased adiponectin and PPARγ levels, and 2.5-fold increased IL-6. In conclusion, ZO rat adipose tissue harbours a large proportion of small adipocytes that manifest impaired cell differentiation and pro-inflammatory activity, two mechanisms by which small adipocytes may contribute to insulin resistance.
    Diabetes & Vascular Disease Research 10/2010; 7(4):311-8. · 3.04 Impact Factor
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    Sun H Kim, Gerald Reaven
    Diabetes 09/2010; 59(9):2105-6. · 7.90 Impact Factor
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    ABSTRACT: Although they have become a widely used experimental technique for identifying differentially expressed (DE) genes, DNA microarrays are notorious for generating noisy data. A common strategy for mitigating the effects of noise is to perform many experimental replicates. This approach is often costly and sometimes impossible given limited resources; thus, analytical methods are needed which increase accuracy at no additional cost. One inexpensive source of microarray replicates comes from prior work: to date, data from hundreds of thousands of microarray experiments are in the public domain. Although these data assay a wide range of conditions, they cannot be used directly to inform any particular experiment and are thus ignored by most DE gene methods. We present the SVD Augmented Gene expression Analysis Tool (SAGAT), a mathematically principled, data-driven approach for identifying DE genes. SAGAT increases the power of a microarray experiment by using observed coexpression relationships from publicly available microarray datasets to reduce uncertainty in individual genes' expression measurements. We tested the method on three well-replicated human microarray datasets and demonstrate that use of SAGAT increased effective sample sizes by as many as 2.72 arrays. We applied SAGAT to unpublished data from a microarray study investigating transcriptional responses to insulin resistance, resulting in a 50% increase in the number of significant genes detected. We evaluated 11 (58%) of these genes experimentally using qPCR, confirming the directions of expression change for all 11 and statistical significance for three. Use of SAGAT revealed coherent biological changes in three pathways: inflammation, differentiation, and fatty acid synthesis, furthering our molecular understanding of a type 2 diabetes risk factor. We envision SAGAT as a means to maximize the potential for biological discovery from subtle transcriptional responses, and we provide it as a freely available software package that is immediately applicable to any human microarray study.
    PLoS Computational Biology 03/2010; 6(3):e1000718. · 4.83 Impact Factor
  • Biruh Workeneh, Fahim Abbasi, Gerald Reaven
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    ABSTRACT: It has recently been suggested that a low urine pH be added to the abnormalities linked to insulin resistance. This conclusion is based on the finding of a low urine pH in individuals with clinical syndromes associated with insulin resistance and not on studies in which a direct measure of insulin sensitivity was shown to be significantly related to differences in urine pH. To address this issue, we quantified insulin-mediated glucose uptake (IMGU) by using the insulin suppression test in 96 apparently healthy, nondiabetic individuals and defined its relation to fasting urine pH. Urine samples were collected and analyzed from a cohort of healthy subjects within a narrow body mass index range who were recruited to determine insulin sensitivity. There was an approximate 6-fold variation in values for IMGU in this population, with no relation to urine pH (r = 0.02). Furthermore, there was no relation between body mass index, as a surrogate estimate of insulin resistance, and urine pH (r = 0.06). On the basis of these findings, we question the view that a low urine pH be added to the abnormalities linked to insulin resistance in low-risk populations.
    American Journal of Clinical Nutrition 03/2010; 91(3):586-8. · 6.50 Impact Factor

Publication Stats

29k Citations
3,652.99 Total Impact Points


  • 1970–2014
    • Stanford Medicine
      • • Department of Medicine
      • • Division of Endocrinology, Gerontology and Metabolism
      • • Division of Cardiovascular Medicine
      • • Division of Clinical Pharmacology
      Stanford, California, United States
  • 1965–2013
    • Stanford University
      • • Stanford Center for Sleep Sciences and Medicine
      • • Division of Cardiovascular Medicine
      • • Department of Medicine
      • • Department of Obstetrics and Gynecology
      Palo Alto, CA, United States
  • 2010
    • Baylor College of Medicine
      • Division of Nephrology
      Houston, TX, United States
  • 1984–2010
    • National Institutes of Health
      Maryland, United States
  • 1987–2003
    • Università degli studi di Parma
      • Department of Clinical and Experimental Medicine
      Parma, Emilia-Romagna, Italy
  • 2001
    • University of Alabama at Birmingham
      • Department of Medicine
      Birmingham, AL, United States
  • 2000
    • Tokyo Medical and Dental University
      • Department of Internal Medicine
      Edo, Tōkyō, Japan
    • University of São Paulo
      • Faculty of Medicine (FM)
      São Paulo, Estado de Sao Paulo, Brazil
    • Fakultní nemocnice Plzeň
      Pilsen, Plzeňský, Czech Republic
  • 1997–2000
    • Faculty of Medicine in Pilsen
      Pilsen, Plzeňský, Czech Republic
  • 1999
    • University of Geneva
      • Department of Internal Medicine
      Genève, GE, Switzerland
  • 1995–1997
    • VA Palo Alto Health Care System
      Palo Alto, California, United States
  • 1993–1996
    • University of California, Los Angeles
      • • Department of Medicine
      • • Department of Microbiology, Immunology & Molecular Genetics
      Los Angeles, CA, United States
    • London School of Hygiene and Tropical Medicine
      Londinium, England, United Kingdom
  • 1987–1996
    • National Defense Medical Center
      • Tri-Service General Hospital
      Taipei, Taipei, Taiwan
  • 1987–1995
    • Tri-Service General Hospital
      T’ai-pei, Taipei, Taiwan
  • 1991
    • Università degli Studi di Genova
      Genova, Liguria, Italy
  • 1990
    • University of California, San Francisco
      • Division of Hospital Medicine
      San Francisco, CA, United States
  • 1989–1990
    • Taipei Veterans General Hospital
      • Department of Medicine
      Taipei, Taipei, Taiwan
    • Taoyuan General Hospital
      Taoyuan City, Taiwan, Taiwan
  • 1982
    • Palo Alto University
      Palo Alto, California, United States