The gamma gap predicts 4-year all-cause mortality among nonagenarians and centenarians

A synergistic impact of body mass index and gamma gap on heart failure and mortality rate among older patients with coronary artery disease: a prospective study with 10-year follow-up

PURPOSE

This prospective study with 10-year follow-up aimed to analyze potential impact of body mass index (BMI) and gamma gap on heart failure and mortality rate in older patients with coronary artery disease (CAD).

METHODS

There were 987 consecutive older patients with CAD included and divided into four groups according to BMI and gamma gap levels.

RESULTS

Median age was 86 years. The highest proportion of heart failure (46.2%) and the highest mortality rate (84.4%) was observed in patients with low BMI and high gamma gap, whereas the lowest proportion of heart failure (18.9%) and the lowest mortality rate (62.9%) was observed in those with high BMI and low gamma gap. After full adjustment in multivariate Logistic regression analysis, heart failure was most common in patients with low BMI and high gamma gap compared with those with high BMI and low gamma gap (hazard ratio [HR]: 2.82, 95% confidence interval [CI]: 1.79-4.48, P < 0.05). Meanwhile, multivariate Cox regression analysis showed that mortality rate was the highest in those with low BMI and high gamma gap compared with patients with high BMI and low gamma gap (HR: 1.65, 95% CI: 1.32-2.07, P < 0.05).

CONCLUSION

The combination of low BMI and high gamma gap could further promote heart failure and increase mortality rate in older patients with CAD. Future studies should explore the underlying mechanisms linking low BMI, high gamma gap, and mortality rate, as well as the potential benefits of nutritional and immunological interventions to improve health prognosis in older patients with CAD.

Association of serum globulin with all-cause mortality in incident hemodialysis patients

BACKGROUND

Serum globulin is a major component of total protein and can be elevated in inflammatory disease states. While inflammation is common in hemodialysis patients and associated with mortality and morbidity, the association between serum globulin and mortality have never been examined in hemodialysis patients.

METHODS

In a retrospective cohort of 104,164 incident hemodialysis patients treated by a large dialysis organization from 2007 to 2011, we explored the association between baseline serum globulin, A/G ratio and serum protein levels and all-cause, cardiovascular and infection-related mortality with adjustments for demographic variables and laboratory markers of malnutrition and inflammation using Cox proportional hazard models.

RESULTS

Patients with globulin concentration >3.8 g/dL had higher all-cause and infection-related mortality risk (Hazard Ratio [HR] 1.11, 95% Confidence Interval [95%CI]: 1.06, 1.16 and HR 1.28, 95%CI: 1.09, 1.51; respectively) in the fully adjusted model when compared to the reference group of 3.0-<3.2 g/dL. In addition, patients with A/G ratio <0.75 had a 45% higher all-cause mortality hazard (HR 1.45, 95%CI: 1.38, 1.52) and patients with total serum protein <5.5 g/dL had a 34% higher risk of death (HR: 1.34, 95%CI: 1.27, 1.42) when compared to the reference (A/G ratio 1.05-<1.15 and total serum protein 6.5-<7 g/dL, respectively).

CONCLUSIONS

Among incident hemodialysis patients, higher globulin level was associated with higher mortality risk independent of other markers of malnutrition and inflammation, including albumin. Lower A/G ratio and serum protein was also associated with higher mortality hazard. The mechanisms that contribute to elevated serum globulin should be further explored.

Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles

PURPOSE

Heterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes.

METHODS

This is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes.

RESULTS

The database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11-17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10-6.00), p = 0.03) increases in hazard of death relative to phenotype III.

CONCLUSION

We identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.

The Gamma Gap Predicts All-Cause Mortality in Chronic Dialysis Patients

Introduction:

The gamma gap (γ-gap) represents the total serum protein concentration minus the albumin concentration. The main aim of this study was to test whether the gamma gap is a predictor of mortality and whether it is associated with other predictors of mortality in chronic hemodialysis patients (CHPs).

Materials and Methods:

We studied a cohort of 100 CHPs with a mean age of 59 ± 12.3 years with duration of dialysis 6.5 ± 4.7 years. Serum proteins were determined by electrophoresis. The association of the gamma gap with serum C-reactive protein (CRP), fibrinogen and albumin concentration was evaluated for correlation. Cox regression analysis was used to identify the predictors of mortality.

Results:

The γ-gap correlates positively with CRP (r = 0.247, P = 0.013) and fibrinogen (r = 0.239, P = 0.016), and inversely with albumin (r = -0.430, P < 0.0001). The regression coefficients (b) and Exp (b) hazard ratio coefficients of covariates in Cox-regression survival analysis in all-cause outcomes were: b = 0.1486, Exp (b) = 1.1602 (P < 0.0001); b = 0.0655, Exp (b) = 1.0677 (P < 0.0015) and b = -0.118, Exp (b) = 0.8887 (P < 0.0009), for γ-gap, CRP and albumin, respectively.

Conclusions:

In patients on chronic hemodialysis, the gamma gap, along with serum albumin and CRP levels, is an independent predictor of mortality. Gamma gap levels correlate directly with serum CRP and fibrinogen levels and inversely with serum albumin levels.

Evaluation of the protein gap for detection of abnormal serum gammaglobulin level: an imperfect predictor

OBJECTIVES

The value of the serum protein gap (PG, difference between total protein and albumin) in the detection of hyper- or hypogammaglobulinemia is not well established. We assessed the performance of PG for the detection of hyper- or hypogammaglobulinemia in a large sample of patients.

METHODS

We reviewed all paired measurements of serum total protein, albumin, quantitative immunoglobulins, and serum protein electrophoresis tested between March 2014 and June 2017 at the Eastern Ontario Regional Laboratory Association. Sensitivity, specificity, positive predictive value, negative predictive value and likelihood ratios of PG at thresholds between 18 and 44 g/L for the detection of hyper- and hypogammaglobulinemia were assessed.

RESULTS

There were 19,575 and 5,426 simultaneous paired data points to assess hyper- and hypogammaglobulinemia identified by serum protein electrophoresis (SPE) and nephelometry, respectively. The mean PG was 36.3 g/L (SD 8.6). The prevalence of hypergammaglobulinemia (>16 g/L by SPE) and hypogammaglobulinemia (IgG <7 g/L) was 21.9 and 5.5%, respectively. High PG (≥38 g/L) had sensitivity and specificity of 76.2 and 71.5% respectively for hypergammaglobulinemia. PG ≥38 g/L had a negative predictive value (NPV) of 93.1% for monoclonal, and 96.9% for polyclonal gammopathy. A PG threshold of ≤18 g/L had of sensitivity of 0.4%, specificity of 100%, PPV of 100% and NPV of 80.1% to detect hypogammaglobulinemia (IgG <7 g/L).

CONCLUSIONS

High and low PG values were not sensitive in detecting hyper- or hypogammaglobulinemia, although negative predictive values were high for both. Performance of PG should be further evaluated prospectively in specific populations at risk of for abnormal IgG levels.

Characterizing COVID-19 Clinical Phenotypes and Associated Comorbidities and Complication Profiles

BACKGROUND

There is limited understanding of heterogeneity in outcomes across hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of distinct clinical phenotypes may facilitate tailored therapy and improve outcomes.

OBJECTIVE

Identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes.

DESIGN, SETTINGS, AND PARTICIPANTS

Retrospective analysis of 1,022 COVID-19 patient admissions from 14 Midwest U.S. hospitals between March 7, 2020 and August 25, 2020.

METHODS

Ensemble clustering was performed on a set of 33 vitals and labs variables collected within 72 hours of admission. K-means based consensus clustering was used to identify three clinical phenotypes. Principal component analysis was performed on the average covariance matrix of all imputed datasets to visualize clustering and variable relationships. Multinomial regression models were fit to further compare patient comorbidities across phenotype classification. Multivariable models were fit to estimate the association between phenotype and in-hospital complications and clinical outcomes. Main outcomes and measures: Phenotype classification (I, II, III), patient characteristics associated with phenotype assignment, in-hospital complications, and clinical outcomes including ICU admission, need for mechanical ventilation, hospital length of stay, and mortality.

RESULTS

The database included 1,022 patients requiring hospital admission with COVID-19 (median age, 62.1 [IQR: 45.9-75.8] years; 481 [48.6%] male, 412 [40.3%] required ICU admission, 437 [46.7%] were white). Three clinical phenotypes were identified (I, II, III); 236 [23.1%] patients had phenotype I, 613 [60%] patients had phenotype II, and 173 [16.9%] patients had phenotype III. When grouping comorbidities by organ system, patients with respiratory comorbidities were most commonly characterized by phenotype III (p=0.002), while patients with hematologic (p<0.001), renal (p<0.001), and cardiac (p<0.001) comorbidities were most commonly characterized by phenotype I. The adjusted odds of respiratory (p<0.001), renal (p<0.001), and metabolic (p<0.001) complications were highest for patients with phenotype I, followed by phenotype II. Patients with phenotype I had a far greater odds of hepatic (p<0.001) and hematological (p=0.02) complications than the other two phenotypes. Phenotypes I and II were associated with 7.30-fold (HR: 7.30, 95% CI: (3.11-17.17), p<0.001) and 2.57-fold (HR: 2.57, 95% CI: (1.10-6.00), p=0.03) increases in the hazard of death, respectively, when compared to phenotype III.

CONCLUSION

In this retrospective analysis of patients with COVID-19, three clinical phenotypes were identified. Future research is urgently needed to determine the utility of these phenotypes in clinical practice and trial design.

Gamma gap thresholds and HIV, hepatitis C, and monoclonal gammopathy

BACKGROUND

An elevated gamma gap (>4 g/dL), the difference between serum total protein and albumin, can trigger testing for chronic infections or monoclonal gammopathy, despite a lack of evidence supporting this clinical threshold.

METHODS

Using the National Health and Nutrition Examination Survey (NHANES) 1999-2014, gamma gap was derived in three subpopulations based on availability of testing for human immunodeficiency virus (HIV; N = 25,680), hepatitis C (HCV; N = 45,134), and monoclonal gammopathy of unknown significance (MGUS; N = 6,118). Disease status was confirmed by HIV antibody and Western blot, HCV RNA test, or electrophoresis with immunofixation. Sensitivity, specificity, and likelihood ratios were calculated for different gamma gap thresholds. Area under the curve (AUC) was used to assess performance and cubic splines were used to characterize the relationship between the gamma gap and each disease.

RESULTS

Mean gamma gaps of participants with HIV, HCV, or MGUS ranged from 3.4-3.8 g/dL. The AUC was 0.80 (95%CI: 0.75,0.85) for HIV, 0.74 (0.72,0.76) for HCV, and 0.64 (0.60,0.69) for MGUS. An elevated gamma gap of over 4 g/dL corresponded to sensitivities of 39.3%, 19.0%, and 15.4% and specificities of 98.4%, 97.8%, and 95.4% for HIV, HCV, and MGUS, respectively. A higher prevalence of all three diseases was observed at both low and high gamma gaps.

DISCUSSION

An elevated gamma gap of 4 g/dL is insensitive for HIV, HCV, or MGUS, but has a high specificity for HIV and HCV, suggesting that the absence of an elevated gamma gap does not rule out HIV, HCV, or MGUS. Conversely, an elevated gap may justify further testing for HIV and HCV, but does not justify electrophoresis in the absence of additional clinical information.