Metformin's role in lowering colorectal cancer risk among individuals with diabetes from the Southern Community Cohort Study.

BACKGROUND: Metformin, utilized to manage hyperglycemia, has been linked to a reduced risk of colorectal cancer (CRC) among individuals with diabetes. However, evidence is lacking for non-Hispanic Black individuals and those with lower socioeconomic status (SES), who face elevated risk for both diabetes and CRC. In this study, we investigated the association between metformin use and incident CRC risk within the Southern Community Cohort Study (SCCS), a racially- and SES-diverse prospective cohort. METHODS: Participants reported their diabetes diagnosis and medications, including metformin, upon enrollment (2002-2009) and during follow-up surveys approximately every five years. Incident cases of CRC were identified through state cancer registries and the National Death Index. Proportional hazards models were employed to explore the relationship between metformin use and CRC risk, adjusted for cancer risk factors. RESULTS: A total of 25,992 participants with diabetes were included in the analysis, among whom 10,095 were taking metformin. Of these participants, 76% identified as non-Hispanic Black, and 60% reported household incomes <$15,000/year. Metformin use was associated with a significantly lower CRC risk (HR [95% CI]: 0.71 [0.55-0.93]), with consistent results for both colon (0.80 [0.59-1.07]) and rectal cancers (0.49 [0.28-0.86]). The protective association appeared to be stronger among non-Hispanic White individuals (0.51 [0.31-0.85]) compared to non-Hispanic Black participants (0.80 [0.59-1.08], p-interaction =.13). Additionally, a protective association was observed among obese individuals (BMI ≥30 kg/m2, 0.59 [0.43-0.82] but not among non-obese participants (0.99 [0.65-1.51], p-interaction =.05) CONCLUSION: Our findings indicate that metformin use is associated with a reduced risk of CRC in individuals with diabetes, including among those from predominantly low SES backgrounds. These results support previous epidemiological findings, and demonstrate that the protective association for metformin in relation to incident CRC likely generalizes to populations with higher underlying risk.

Race- and Gender-Specific Associations between Neighborhood-Level Socioeconomic Status and Body Mass Index: Evidence from the Southern Community Cohort Study.

Obesity and a low socioeconomic status (SES), measured at the neighborhood level, are more common among Americans of Black race and with a low individual-level SES. We examined the association between the neighborhood SES and body mass index (BMI) using data from 80,970 participants in the Southern Community Cohort Study, a cohort that oversamples Black and low-SES participants. BMI (kg/m2) was examined both continuously and categorically using cut points defined by the CDC. Neighborhood SES was measured using a neighborhood deprivation index composed of census-tract variables in the domains of education, employment, occupation, housing, and poverty. Generally, the participants in lower-SES neighborhoods were more likely to have a higher BMI and to be considered obese. We found effect modification by race and sex, where the neighborhood-BMI association was most apparent in White female participants in all the quintiles of the neighborhood SES (ORQ2 = 1.55, 95%CI = 1.34, 1.78; ORQ3 = 1.71, 95%CI = 1.48, 1.98; ORQ4 = 1.76, 95%CI = 1.52, 2.03; ORQ5 = 1.64, 95%SE = 1.39, 1.93). Conversely, the neighborhood-BMI association was mostly null in Black male participants (ORQ2 = 0.91, 95%CI = 0.72, 1.15; ORQ3 = 1.05, 95%CI = 0.84, 1.31; ßQ4 = 1.00, 95%CI = 0.81, 1.23; ORQ5 = 0.76, 95%CI = 0.63, 0.93). Within all the subgroups, the associations were attenuated or null in participants residing in the lowest-SES neighborhoods. These findings suggest that the associations between the neighborhood SES and BMI vary, and that other factors aside from the neighborhood SES may better predict the BMI in Black and low-SES groups.

The Use of Food Images and Crowdsourcing to Capture Real-time Eating Behaviors: Acceptability and Usability Study.

BACKGROUND: As poor diet quality is a significant risk factor for multiple noncommunicable diseases prevalent in the United States, it is important that methods be developed to accurately capture eating behavior data. There is growing interest in the use of ecological momentary assessments to collect data on health behaviors and their predictors on a micro timescale (at different points within or across days); however, documenting eating behaviors remains a challenge. OBJECTIVE: This pilot study (N=48) aims to examine the feasibility-usability and acceptability-of using smartphone-captured and crowdsource-labeled images to document eating behaviors in real time. METHODS: Participants completed the Block Fat/Sugar/Fruit/Vegetable Screener to provide a measure of their typical eating behavior, then took pictures of their meals and snacks and answered brief survey questions for 7 consecutive days using a commercially available smartphone app. Participant acceptability was determined through a questionnaire regarding their experiences administered at the end of the study. The images of meals and snacks were uploaded to Amazon Mechanical Turk (MTurk), a crowdsourcing distributed human intelligence platform, where 2 Workers assigned a count of food categories to the images (fruits, vegetables, salty snacks, and sweet snacks). The agreement among MTurk Workers was assessed, and weekly food counts were calculated and compared with the Screener responses. RESULTS: Participants reported little difficulty in uploading photographs and remembered to take photographs most of the time. Crowdsource-labeled images (n=1014) showed moderate agreement between the MTurk Worker responses for vegetables (688/1014, 67.85%) and high agreement for all other food categories (871/1014, 85.89% for fruits; 847/1014, 83.53% for salty snacks, and 833/1014, 81.15% for sweet snacks). There were no significant differences in weekly food consumption between the food images and the Block Screener, suggesting that this approach may measure typical eating behaviors as accurately as traditional methods, with lesser burden on participants. CONCLUSIONS: Our approach offers a potentially time-efficient and cost-effective strategy for capturing eating events in real time.