Caffeine Intake and its Association with Disease Progression, Sleep Quality and Anxiety Symptoms and Nutritional Alterations in People Living with HIV in the Miami Adult Studies on HIV Cohort

Miami-Dade County has approximately 27,000 people living with HIV (PLWH), and the highest HIV incidence in the nation. PLWH have reported several types of sleep disturbances. Caffeine is an anorexic and lipolytic stimulant that may adversely affect sleep patterns, dietary intakes and body composition. High caffeine consumption (>250 mg. per day or the equivalent of >4 cups of brewed coffee) may also affect general functionality, adherence to antiretroviral treatment (ART) and HIV care. This study assess the relationship of high caffeine intake with markers of disease progression, sleep quality, insomnia, anxiety, nutritional intakes and body composition. A convenience sample of 130 PLWH on stable ART were recruited from the Miami Adult Studies on HIV (MASH) cohort, and followed for three months. After consenting, questionnaires on Modified Caffeine Consumption (MCCQ), Pittsburg Insomnia Rating Scale (PIRS), Pittsburg Sleep Quality Index (PSQI), Generalized Anxiety Disorder-7 (GAD-7), socio-demographics, drug and medication use were completed. CD4 count, HIV viral load, anthropometries, and body composition measures were obtained. Mean age was 47.89±6.37 years, 60.8% were male and 75.4% were African-Americans. Mean caffeine intake at baseline was 337.63 ± 304.97 mg/day (Range: 0-1498 mg/day) and did not change significantly at 3 months. In linear regression, high caffeine consumption was associated with higher CD4 cell count (β=1.532, P=0.049), lower HIV viral load (β=-1.067, P=0.048), higher global PIRS (β=1.776, P=0.046), global PSQI (β=2.587, P=0.038), and GAD-7 scores (β=1.674, P=0.027), and with lower fat mass (β=-0.994, P=0.042), energy intakes (β=-1.643, P=0.042) and fat consumption (β=-1.902, P=0.044), adjusting for relevant socioeconomic and disease progression variables. Over three months, these associations remained significant. The association of high caffeine with lower BMI weakened when excluding users of other anorexic and stimulant drugs such as cocaine and methamphetamine, suggesting that caffeine in combination, but not alone, may worsen their action. In summary, high caffeine consumption was associated with better measures of disease progression; but was also detrimental on sleep quality, nutritional intakes, BMI and body composition and associated with insomnia and anxiety. Large scale studies for longer time are needed to elucidate the contribution of caffeine to the well-being of PLWH.

Glycemic control: Risk factors, quality of life, workforce participation, and mortality among US adults with type 2 diabetes

Despite research showing the benefits of glycemic control, it remains suboptimal among adults with diabetes in the United States. Possible reasons include unaddressed risk factors as well as lack of awareness of its immediate and long term consequences. The objectives of this study were to, using cross-sectional data, (1) ascertain the association between suboptimal (Hemoglobin A1c (HbA1c) .7%), borderline (HbA1c 7-8.9%), and poor (HbA1c .9%) glycemic control and potentially new risk factors (e.g. work characteristics), and (2) assess whether aspects of poor health and well-being such as poor health related quality of life (HRQOL), unemployment, and missed-work are associated with glycemic control; and (3) using prospective data, assess the relationship between mortality risk and glycemic control in US adults with type 2 diabetes. Data from the 1988-1994 and 1999-2004 National Health and Nutrition Examination Surveys were used. HbA1c values were used to create dichotomous glycemic control indicators. Binary logistic regression models were used to assess relationships between risk factors, employment status and glycemic control. Multinomial logistic regression analyses were conducted to assess relationships between glycemic control and HRQOL variables. Zero-inflated Poisson regression models were used to assess relationships between missed work days and glycemic control. Cox-proportional hazard models were used to assess effects of glycemic control on mortality risk. Using STATA software, analyses were weighted to account for complex survey design and non-response. Multivariable models adjusted for socio-demographics, body mass index, among other variables. Results revealed that being a farm worker and working over 40 hours/week were risk factors for suboptimal glycemic control. Having greater days of poor mental was associated with suboptimal, borderline, and poor glycemic control. Having greater days of inactivity was associated with poor glycemic control while having greater days of poor physical health was associated with borderline glycemic control. There were no statistically significant relationships between glycemic control, self-reported general health, employment, and missed work. Finally, having an HbA1c value less than 6.5% was protective against mortality. The findings suggest that work-related factors are important in a person’s ability to reach optimal diabetes management levels. Poor glycemic control appears to have significant detrimental effects on HRQOL.^

Glycemic Control: Risk Factors, Quality of Life, Workforce Participation, and Mortality Among US Adults with Type 2 Diabetes

Despite research showing the benefits of glycemic control, it remains suboptimal among adults with diabetes in the United States. Possible reasons include unaddressed risk factors as well as lack of awareness of its immediate and long term consequences. The objectives of this study were to, using cross-sectional data, 1) ascertain the association between suboptimal (Hemoglobin A1c (HbA1c) ≥7%), borderline (HbA1c 7-8.9%), and poor (HbA1c ≥9%) glycemic control and potentially new risk factors (e.g. work characteristics), and 2) assess whether aspects of poor health and well-being such as poor health related quality of life (HRQOL), unemployment, and missed-work are associated with glycemic control; and 3) using prospective data, assess the relationship between mortality risk and glycemic control in US adults with type 2 diabetes. Data from the 1988-1994 and 1999-2004 National Health and Nutrition Examination Surveys were used. HbA1c values were used to create dichotomous glycemic control indicators. Binary logistic regression models were used to assess relationships between risk factors, employment status and glycemic control. Multinomial logistic regression analyses were conducted to assess relationships between glycemic control and HRQOL variables. Zero-inflated Poisson regression models were used to assess relationships between missed work days and glycemic control. Cox-proportional hazard models were used to assess effects of glycemic control on mortality risk. Using STATA software, analyses were weighted to account for complex survey design and non-response. Multivariable models adjusted for socio-demographics, body mass index, among other variables. Results revealed that being a farm worker and working over 40 hours/week were risk factors for suboptimal glycemic control. Having greater days of poor mental was associated with suboptimal, borderline, and poor glycemic control. Having greater days of inactivity was associated with poor glycemic control while having greater days of poor physical health was associated with borderline glycemic control. There were no statistically significant relationships between glycemic control, self-reported general health, employment, and missed work. Finally, having an HbA1c value less than 6.5% was protective against mortality. The findings suggest that work-related factors are important in a person’s ability to reach optimal diabetes management levels. Poor glycemic control appears to have significant detrimental effects on HRQOL.