A comparative study of the relationship between time in range assessed by self-monitoring of blood glucose and continuous glucose monitoring with microalbuminuria outcome, HOMA-IR and HOMA-ß test.

AIMS: To compare the time in range (TIR) obtained from self-monitoring of blood glucose (SMBG) with that obtained from continuous glucose monitoring (CGM), and explore the relationship of TIR with microalbuminuria outcome, HOMA-IR and HOMA-ß test. METHODS: We recruited 400 patients with type 2 diabetes to carry out blood glucose monitoring by both SMBG and CGM for 3 consecutive days. TIR, TAR, TBR and other blood glucose variation indices were calculated respectively through the glucose data achieved from SMBG and CGM. The HOMA-IR and HOMA-ß test was evaluated by an oral glucose tolerance test. Urinary microalbumin-to-creatinine ratio completed in the laboratory. RESULTS: The median (25 %, 75 % quartile) of TIRCGM and TIRSMBG were 74.94(44.90, 88.04) and 70.83(46.88, 87.50) respectively, and there was no significant difference, p = 0.489; For every 1 % increase in TIRCGM, the risk of microalbuminuria decreased by 1.6 % (95%CI:0.973, 0.995, p = 0.006) and for every 1 % increase in TIRSMBG, the risk of microalbuminuria decreased by 1.3 % (95%CI:0.975, 0.999, p = 0.033). Stepwise multiple linear regression analysis showed an independent positive correlation between TIR (including TIRCGM and TIRSBMG) and LnDI30 and LnDI120 levels (p = 0.000). CONCLUSIONS: The TIR calculated by SMBG was highly consistent with that reported by CGM and was significantly associated with the risk of microalbuminuria and the HOMA-ß. Higher TIR quartiles were associated with lower incidence of microalbuminuria as well as higher lever of HOMA-ß. For patients with limited CGM application, SMBG-derived TIR may be an alternative to CGM-derived TIR, to assess blood glucose control.

Development and evaluation of a risk prediction tool for risk-adapted screening of colorectal cancer in China.

Risk prediction tools for colorectal cancer (CRC) have potential to improve the efficiency of population-based screening by facilitating risk-adapted strategies. However, such an applicable tool has yet to be established in the Chinese population. In this study, a risk score was created using data from the China Kadoorie Biobank (CKB), a nationwide cohort study of 409,854 eligible participants. Diagnostic performance of the risk score was evaluated in an independent CRC screening programme, which included 91,575 participants who accepted colonoscopy at designed hospitals in Zhejiang Province, China. Over a median follow-up of 11.1 years, 3136 CRC cases were documented in the CKB. A risk score was created based on nine questionnaire-derived variables, showing moderate discrimination for 10-year CRC risk (C-statistic = 0.68, 95 % CI: 0.67-0.69). In the CRC screening programme, the detection rates of CRC were 0.25 %, 0.82 %, and 1.93 % in low-risk (score <6), intermediate-risk (score: 6-19), and high-risk (score >19) groups, respectively. The newly developed score exhibited a C-statistic of 0.65 (95 % CI: 0.63-0.66), surpassing the widely adopted tools such as the Asia-Pacific Colorectal Screening (APCS), modified APCS, and Korean Colorectal Screening scores (all C-statistics = 0.60). In conclusion, we developed a novel risk prediction tool that is useful to identify individuals at high risk of CRC. A user-friendly online calculator was also constructed to encourage broader adoption of the tool.

A Metabolomic Signature of Obesity and Risk of Colorectal Cancer: Two Nested Case-Control Studies.

Obesity is a leading contributor to colorectal cancer (CRC) risk, but the metabolic mechanisms linking obesity to CRC are not fully understood. We leveraged untargeted metabolomics data from two 1:1 matched, nested case-control studies for CRC, including 223 pairs from the US Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial and 190 pairs from a prospective Chinese cohort. We explored serum metabolites related to body mass index (BMI), constructed a metabolomic signature of obesity, and examined the association between the signature and CRC risk. In total, 72 of 278 named metabolites were correlated with BMI after multiple testing corrections (p FDR < 0.05). The metabolomic signature was calculated by including 39 metabolites that were independently associated with BMI. There was a linear positive association between the signature and CRC risk in both cohorts (p for linear < 0.05). Per 1-SD increment of the signature was associated with 38% (95% CI: 9-75%) and 28% (95% CI: 2-62%) higher risks of CRC in the US and Chinese cohorts, respectively. In conclusion, we identified a metabolomic signature for obesity and demonstrated the association between the signature and CRC risk. The findings offer new insights into the underlying mechanisms of CRC, which is critical for improved CRC prevention.