Association of Obesity Indicators with Hypertension in Type 2 Diabetes Mellitus Patients

Chaudhary, Ghulam Mohyud Din; Din, Asim Tameez Ud; Chaudhary, Farooq Mohyud Din; Tanveer, Azfar; Siddiqui, Khaleeq H.; Din, Asma Tameez Ud; Chaudhary, Noman Ahmed; Chaudhary, Sana Mohyud Din; Tameez-ud-din, Ahsan; Nawaz, Faisal A.

doi:10.7759/cureus.5050

Cited by 10 publications

(10 citation statements)

References 19 publications

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“…Our participants showed elevated BP, BG, and BMI. Such a clinical profile resonates with previous diabetes studies [ 47 ]. Due to the nature of metabolic disease, factors such as high BP, diabetes, and obesity are shown to be the most suitable therapy areas to address via digital health.…”

Section: Discussionsupporting

confidence: 87%

Blood Pressure Monitoring as a Digital Health Tool for Improving Diabetes Clinical Outcomes: Retrospective Real-world Study

Fundoiano-Hershcovitz¹,

Bacher²,

Ritholz³

et al. 2022

J Med Internet Res

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Background Remote data capture for blood glucose (BG) or blood pressure (BP) monitoring and the use of a supportive digital app are becoming the model in diabetes and hypertension chronic care. One of the goals in chronic condition management is to increase awareness and generate behavioral change in order to improve outcomes in diabetes and related comorbidities, such as hypertension. In addition, there is a lack of understanding of the association between BG and BP levels when using digital health tools. Objective By applying a rigorous study framework to digital health data, this study investigated the relationship between BP monitoring and BG and BP levels, as well as a lagged association between BP and BG. We hypothesized that during the first 6 months of BP monitoring, BG and BP levels would decrease. Finally, we suggested a positive association between BP levels and the following month’s BG levels. Methods In this retrospective, real-world case-control study, we extracted the data of 269 people with type 2 diabetes (T2D) who tracked their BG levels using the Dario digital platform for a chronic condition. We analyzed the digital data of the users who, in addition to BG, monitored their BP using the same app (BP-monitoring [BPM] group, n=137) 6 months before and after starting their BP monitoring. Propensity score matching established a control group, no blood pressure monitoring (NBPM, n=132), matched on demographic and baseline clinical measures to the BPM group. A piecewise mixed model was used for analyzing the time trajectories of BG, BP, and their lagged association. Results Analysis revealed a significant difference in BG time trajectories associated with BP monitoring in BPM and NBPM groups (t=–2.12, P=.03). The BPM group demonstrated BG reduction improvement in the monthly average BG levels during the first 6 months (t=–3.57, P<.001), while BG did not change for the NBPM group (t=0.39, P=.70). Both groups showed similarly stable BG time trajectories (B=0.98, t=1.16, P=.25) before starting the use of the BP-monitoring system. In addition, the BPM group showed a significant reduction in systolic (t=–6.42, P<.001) and diastolic (t=–4.80, P<.001) BP during the first 6 months of BP monitoring. Finally, BG levels were positively associated with systolic (B=0.24, t=2.77, P=.001) and diastolic (B=0.30, t=2.41, P=.02) BP. Conclusions The results of this study shed light on the association between BG and BP levels and on the role of BP self-monitoring in diabetes management. Our findings also underscore the need and provide a basis for a comprehensive approach to understanding the mechanism of BP regulation associated with BG.

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Section: Discussionsupporting

confidence: 87%

Blood Pressure Monitoring as a Digital Health Tool for Improving Diabetes Clinical Outcomes: Retrospective Real-world Study

Fundoiano-Hershcovitz¹,

Bacher²,

Ritholz³

et al. 2022

J Med Internet Res

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“…Existing evidence suggests that WHR is a predictor of T2DM, 83 and T2DM patients with a high WHR were more likely to be hypertensive as compared to those with normal WHR. 84 Recent studies demonstrated that GDF-15 is a key regulator of body weight in animals, mainly through suppression of food intake and is also linked to inflammation, 24 , 28 , 85 cardiovascular disease, 86 and metabolic and mental disorders. 27 , 29 , 30 These and other studies suggest the protective role of GDF-15 in various disorders.…”

Section: Discussionmentioning

confidence: 99%

Deciphering the Causal Relationships Between Low Back Pain Complications, Metabolic Factors, and Comorbidities

Tarabeih¹,

Kalinkovich²,

Shalata³

et al. 2022

JPR

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Purpose Low back pain (LBP) is one of the major disabling health conditions in aging societies presenting significant cost burdens to health and social care systems. Its complications and associated disability are often accompanied by mental disorders, metabolic comorbidities, changed body composition, and inflammation. However, their mutual relationships in LBP-associated disability remain unclear. Methods In the present case-control study, a self-report validated questionnaire was used to collect LBP disability data in an ethnically homogeneous Israeli Arab sample (489 males and 589 females). Body composition parameters were assessed through bioelectrical impedance analysis and plasma levels of soluble markers by EISA. Comorbidity status was assessed in personal interview and from the individual medical files. Results Our mixed multiple regression analysis identified that GDF-15 (β = 0.160, p = 2.95×10-8), vaspin (β = 0.085, p = 0.003), follistatin (β = 0.076, p = 0.001), extracellular water (β = 0.096, p = 0.001), waist hip ratio (β = 0.072, p = 0.009), mental disorders (β = 0.077, p = 0.001), and metabolic comorbidities (β = 0.059, p = 0.02) were significantly associated with LBP disability scores, when controlling for age and sex effects. Additive Bayesian network modelling further suggests that LBP disability appears to be directly influenced by age, sex, GDF-15, and extracellular water, and indirectly by mental and metabolic disorders, waist-hip ratio, and follistatin. LBP, in turn, seems to affect the vaspin levels directly. Conclusion Our data suggest the existence of a complex, age-associated, and probably hierarchical, relationship between LBP disability and mental and metabolic disorders, inflammation-related soluble markers, and body composition parameters.

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“…This study aims to analyze the relationship between various anthropometric indices and HDC. Hypertension and diabetes are closely related to each other and often coexist [ 2 , 12 , 13 , 15 – 20 , 63 , 64 ]. Several studies have noted that hypertension is the most common complication of diabetes [ 2 , 12 – 18 ].…”

Section: Discussionmentioning

confidence: 99%

Risk factors for hypertension and diabetes comorbidity in a Korean population: A cross-sectional study

Hee

Lee

2022

PLoS ONE

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Background Hypertension and diabetes are risk factors for severe cardiovascular disease and are prevalent comorbidities. No studies have examined the associations of various risk factors related to anthropometry, bone mineral density and body composition of specific body regions with hypertension and diabetes comorbidity (HDC). This study explored the association between HDC and various risk factors related to specific body regions. Method A total of 7,978 subjects (3,451 men and 4,527 women) aged ≥ 50 years were included in the analysis. A binary logistic regression analysis based on complex sample data analysis was conducted to examine associations between the normal and diabetes groups, between the normal and hypertension groups, and between the normal and HDC groups. Results Among Korean adults aged ≥ 50 years, 11.27% of men and 10.05% of women had both diabetes and hypertension. The HDC prevalence was higher in men than in women. In men, waist-to-height ratio (WHtR, adjusted OR = 1.63 [1.22–2.18], adjusted p<0.001) exhibited a more significant association with HDC than other indices, left leg fat mass (adjusted OR = 0.61 [0.45–0.81], adjusted p = 0.0009) and right leg fat mass (adjusted OR = 0.60 [0.45–0.81], adjusted p = 0.0007) displayed strong negative associations with diabetes, and left leg lean mass (adjusted OR = 0.77 [0.67–0.89], adjusted p = 0.0002) and trunk fat mass (adjusted OR = 1.28 [1.07–1.52], adjusted p = 0.0062) were significantly associated with hypertension. In women, left leg fat mass (adjusted OR = 0.29 [0.22–0.39], adjusted p<0.0001) and right leg fat mass (adjusted OR = 0.32 [0.23–0.45], adjusted p<0.0001) exhibited strong negative associations with HDC, waist circumference (WC) (adjusted OR = 2.15 [1.40–3.30], adjusted p = 0.0005) showed a stronger association with diabetes than did other indices, and WC (adjusted OR = 1.39 [1.11–1.74], adjusted p = 0.0040) and WHtR (adjusted OR = 1.39 [1.09–1.76], adjusted p = 0.0075) were significantly associated with hypertension. Discussion HDC was more strongly associated with fat and lean mass than diabetes and hypertension. The association between HDC and body fat variables was more robust in women than in men.

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Association of Obesity Indicators with Hypertension in Type 2 Diabetes Mellitus Patients

Cited by 10 publications

References 19 publications

Blood Pressure Monitoring as a Digital Health Tool for Improving Diabetes Clinical Outcomes: Retrospective Real-world Study

Blood Pressure Monitoring as a Digital Health Tool for Improving Diabetes Clinical Outcomes: Retrospective Real-world Study

Deciphering the Causal Relationships Between Low Back Pain Complications, Metabolic Factors, and Comorbidities

Risk factors for hypertension and diabetes comorbidity in a Korean population: A cross-sectional study

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