Comparison of Various Indices in Identifying Insulin Resistance and Diabetes in Chronic Spinal Cord Injury

Farkas, Gary J.; Gordon, Phillip; Trewick, Nareka; Gorgey, Ashraf S.; Dolbow, David R.; Tiozzo, Eduard; Berg, Arthur; Gater, David R.

doi:10.3390/jcm10235591

Cited by 10 publications

(11 citation statements)

References 74 publications

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“…Spinal cord injury (SCI) is a severe neurological injury that results in damage to multiple bodily systems and functions. It is often associated with secondary conditions including chronic pain [ 1 , 2 ], spasticity [ 3 ], urinary tract infection [ 4 ], bowel complications [ 5 ], insulin resistance and diabetes [ 6 ], sexual dysfunction [ 7 ], pressure injuries [ 8 ], cardiovascular and respiratory problems [ 9 , 10 ], fatigue [ 11 , 12 ], cognitive impairment, and low vitality/fatigue and mental health problems [ 13 , 14 , 15 , 16 ]. In addition, it can lead to reduced social autonomy [ 17 ] and greater challenges for gaining and sustaining employment [ 18 ], and there exists a high risk of weight gain and sleep disturbance [ 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Investigating Dynamics of the Spinal Cord Injury Adjustment Model: Mediation Model Analysis

Craig

Tran

Arora

et al. 2022

JCM

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Spinal cord injury (SCI) is a severe neurological injury that results in damage to multiple bodily systems. SCI rehabilitation requires a significant focus on improving adjustment to the injury. This paper presents a detailed description of the Spinal Cord Injury Adjustment Model (SCIAM), which clarifies how individuals adjust to SCI and contends that adjustment to SCI is a multifactorial process involving non-linear dynamic adaptation over time. Evidence supporting SCIAM is also discussed. Mediation analyses were conducted to test the mediator dynamics proposed by the model. The analyses tested the relationship between two moderators (self-care and secondary health conditions), mediators (two self-efficacy items and appraisal of quality of life or QoL), and positive versus negative vitality/mental health as outcomes. Results showed that higher self-efficacy and perceived QoL was related to greater independence in self-care and reduced negative impacts of secondary health conditions. This study supported the mediation role of self-efficacy and other appraisals such as perceived QoL in enhancing self-care and buffering the negative impact of health challenges. In conclusion, it is important to employ a holistic model such as SCIAM to conceptualise and increase understanding of the process of adjustment following a severe neurological injury such as SCI.

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Section: Introductionmentioning

confidence: 99%

Investigating Dynamics of the Spinal Cord Injury Adjustment Model: Mediation Model Analysis

Craig

Tran

Arora

et al. 2022

JCM

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“…Solinsky et al [ 84 ] identified insulin resistance in 12.5% of their cohort using elevated fasting plasma glucose as a criterion but in 33.3% when using Homeostatic Model Assessment 2 for Insulin Resistance (HOMA2-IR) criteria. Farkas and colleagues [ 125 ] recently examined the accord among indices of glucose metabolism against the gold standard measure of insulin sensitivity as assessed by the intravenous glucose tolerance test in 29 people with chronic motor complete SCI (79% men, 42.2 ± 11.4 years old, BMI 28.6 ± 6.4 kg/m 2 , C4 to T10). The authors demonstrated that the greatest agreement with insulin sensitivity was with the Quantitative Insulin-sensitivity Check Index (QUICKI), followed by Homeostatic Model Assessment for Insulin Resistance, HOMA2-IR, and the Matsuda Index.…”

Section: Dysglycemia and Insulin Resistance After Scimentioning

confidence: 99%

“…Despite being commonly used for evaluating disorders of carbohydrate metabolism, fasting plasma glucose and hemoglobin A1C had the poorest agreement with insulin sensitivity. Farkas et al [ 125 ] hypothesized that QUICKI’s superior agreement stems from the log transformation of QUICKI values. The authors noted that for people with SCI, in the absence of QUICKI, fasting plasma glucose and hemoglobin A1C should be used in combination rather than in isolation to provide better diagnostic utility.…”

Section: Dysglycemia and Insulin Resistance After Scimentioning

confidence: 99%

“…Dysglycemia diagnosis should include the evaluation of type 2 diabetes mellitus and pre-diabetes based on fasting plasma glucose, the 2-h plasma glucose value after a 75-g oral glucose tolerance test, or hemoglobin A1C criteria ( Table 6 ) [ 43 ]. If quantifying insulin resistance after SCI, it should be evaluated with the QUICKI given its accord with the intravenous glucose tolerance test [ 43 , 125 ] ( Table 6 ). Current pharmacotherapy treatment should be considered a positive qualification for dyslipidemia, hypertension, and/or dysglycemia.…”

Section: Diagnosis Of Cardiometabolic Risk and Syndrome After Scimentioning

confidence: 99%

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The Diagnosis and Management of Cardiometabolic Risk and Cardiometabolic Syndrome after Spinal Cord Injury

Farkas

Burton

McMillan

et al. 2022

JPM

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Individuals with spinal cord injuries (SCI) commonly present with component risk factors for cardiometabolic risk and combined risk factors for cardiometabolic syndrome (CMS). These primary risk factors include obesity, dyslipidemia, dysglycemia/insulin resistance, and hypertension. Commonly referred to as “silent killers”, cardiometabolic risk and CMS increase the threat of cardiovascular disease, a leading cause of death after SCI. This narrative review will examine current data and the etiopathogenesis of cardiometabolic risk, CMS, and cardiovascular disease associated with SCI, focusing on pivotal research on cardiometabolic sequelae from the last five years. The review will also provide current diagnosis and surveillance criteria for cardiometabolic disorders after SCI, a novel obesity classification system based on percent total body fat, and lifestyle management strategies to improve cardiometabolic health.

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“…The QUICKI is the inverse of the HOMA-IR and assesses insulin sensitivity instead of IR (69) and a value <0.339 indicated IR (59)(60)(61)(62).…”

Section: Research Design Study Population and Inclusion And Exclusion...mentioning

confidence: 99%

Is there any difference in insulin resistance status between cases of benign and malignant ovarian neoplasms? A study on surrogate markers of insulin resistance in Indonesian non‑diabetic women

et al. 2022

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The association between insulin resistance (IR) and ovarian neoplasm is little known. The present study attempted to investigate the difference in clinicopathological characteristics, metabolic parameters, and IR prevalence between benign and malignant ovarian neoplasms. The cross-sectional study involved 52 non-diabetic women with benign (n=27) and malignant (n=25) diagnoses in a tertiary hospital in Indonesia. Fasting insulin level (FIL), homeostatic model assessment of IR and β-cell dysfunction (HOMA-IR and HOMA-β), fasting IR index (FIRI), and quantitative insulin sensitivity check index (QUICKI) were used as surrogate markers to evaluate IR. Parametric and nonparametric statistical tests were employed to analyze the different parameters between the two groups. Pearson or Spearman's rank test assessed the correlation between markers and clinical variables. Results revealed that patients with benign neoplasms were younger than those with malignant neoplasms (38.63 vs. 47.40 years; P=0.003) and had a higher median body mass index (BMI) than their counterparts (22.98 vs. 18.61 kg/m 2 ; P=0.014). Different characteristics between benign and malignant neoplasm cases were found in menopausal status, ovary side affected, systolic blood pressure, and BMI classes. Endometrial cysts and mucinous carcinoma were the most often diagnosed benign and malignant neoplasms. Malignant neoplasms had a lower median HOMA-β score than benign neoplasms (49.33 vs. 75.79; P=0.011), indicating more severe β-cell dysfunction. No significant difference was observed in the prevalence of IR between benign and malignant ovarian neoplasms for the following values of each marker: FIL (25.9% vs. 12.0%), HOMA-IR (37.0% vs. 28.0%), FIRI (51.9% vs. 48.0%) and QUICKI (81.5% vs. 92.0%). The indicators of FIL, HOMA-IR, HOMA-β, FIRI, and QUICKI correlated with each other and confirmed the reliability of these surrogate markers for measuring IR status in ovarian neoplasms. In brief, benign ovarian neoplasms tended to have more IR when compared with malignant ovarian neoplasms. However, this difference was not statistically significant.

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Comparison of Various Indices in Identifying Insulin Resistance and Diabetes in Chronic Spinal Cord Injury

Cited by 10 publications

References 74 publications

Investigating Dynamics of the Spinal Cord Injury Adjustment Model: Mediation Model Analysis

Investigating Dynamics of the Spinal Cord Injury Adjustment Model: Mediation Model Analysis

The Diagnosis and Management of Cardiometabolic Risk and Cardiometabolic Syndrome after Spinal Cord Injury

Is there any difference in insulin resistance status between cases of benign and malignant ovarian neoplasms? A study on surrogate markers of insulin resistance in Indonesian non‑diabetic women

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