Archive for month: August, 2024

“In the cohort study of 374,021 older veterans with diabetes, higher HbA1c stability, indicated by more time spent within target HbA1c ranges, was linked to a reduced risk of ADRD. Additionally, when HbA1c levels fell outside the target range, spending more time below individualized HbA1c targets was associated with a higher risk of ADRD, even in individuals not on medications known to cause hypoglycemia,” the researchers reported.

Individuals with diabetes often develop Alzheimer’s disease and related dementias. Factors such as hyperglycemia, hypoglycemia, and glycemic variability are linked to a higher risk of ADRD. Traditional glycemic metrics, like average glycated hemoglobin A1c (HbA1c), might not capture the complex and dynamic factors connecting diabetes to ADRD. The HbA1c time in range (TIR), a measure of glycemic control that reflects HbA1c stability within specific ranges over time, could provide new insights into how glucose levels over time relate to the incidence of ADRD.

Against the above background, Patricia C. Underwood, William F. Connell School of Nursing, Boston College, Boston, Massachusetts, and colleagues examine the association between HbA1c TIR and the incidence of ADRD in older veterans with diabetes.

The cohort study utilized administrative and healthcare utilization data from the Veterans Health Administration and Medicare, covering the period from January 1, 2004, to December 31, 2018. It included veterans aged 65 and older with diabetes who had at least four HbA1c tests during a 3-year baseline period starting between January 1, 2005, and December 31, 2014. Data analysis took place between July and December 2023.

Hemoglobin A1c TIR was calculated as the percentage of days during the baseline period when HbA1c levels were within individualized target ranges based on clinical characteristics and life expectancy, with higher HbA1c TIR considered more favorable. The study estimated the association between HbA1c TIR and the incidence of Alzheimer’s disease and related dementias. Additional models assessed ADRD incidence in participants who mostly fell above or below their HbA1c target ranges.

The study led to the following findings:

• The study included 374 021 veterans with diabetes (mean age, 73.2 years; 99% males). During follow-up of up to 10 years, 11% developed ADRD.
• Adjusted Cox proportional hazards regression models showed lower HbA1c TIR was associated with increased risk of incident ADRD (HbA1c TIR of 0 to <20% compared with ≥80%: hazard ratio, 1.19).
• The direction of out-of-range HbA1c levels was associated with incident ADRD.
• Having greater time below range (≥60%, compared with ≥60% TIR) was associated with significantly increased risk (hazard ratio, 1.23).
• Findings remained significant after excluding individuals with baseline use of medications associated with hypoglycemia risk (ie, insulin and sulfonylureas) or with hypoglycemia events.

The findings showed that in older adults with diabetes, greater HbA1c stability within individualized target ranges was linked to a reduced risk of Alzheimer’s disease and related dementias. Conversely, lower HbA1c TIR may help identify patients at higher risk for ADRD.

“These results underscore the advantages of using personalized HbA1c target ranges tailored to age, life expectancy, and comorbidities. Clinicians should collaborate with patients to maintain HbA1c stability, which can help lower the risk of ADRD in older adults with diabetes,” the researchers concluded.

Reference:

Underwood PC, Zhang L, Mohr DC, et al. Glycated Hemoglobin A1c Time in Range and Dementia in Older Adults With Diabetes. JAMA Netw Open. 2024;7(8):e2425354. doi:10.1001/jamanetworkopen.2024.25354

As per the schedule, the last date for submission of the online admission application form, correction in the online application form and submission of the online application form for the NRI quota is also August 12, 2024. There will be no opportunity for correction in the online application form after the last date of application, after which the application will be automatically locked and no changes can be made to it. The online admission application form should be submitted through the official website of BUFHS.

The application fee is Rs. 3000/- + 18% GST (Rs. 3540/-) and the SC candidates have to pay a fee of Rs. 1500 + 18% GST (Rs. 1770/-). The last date for submission of the application fee through the online payment gateway is 13 August 2024.

Moreover, upto August 16, 2024, the university will display the provisional merit list. Thereafter, if candidates have any objection to the Provisional Merit List then they can submit their objections through email only till 05:00 PM on 16 August 2024. Later, the final merit list will be displayed after considering the objections on 19th August 2024.

Meanwhile, to fill the choices for the 2nd round of online counselling, candidates are required to submit their online preferences of colleges/categories through the BUFHS official website. However, the date for choice filling for the 2nd round of online counselling will be displayed later.

Further, candidates applying under the sports category are required to submit the printed/hard copy of the online submitted application form along with self-attested photocopies of 10+1 & 10+2 DMC, Sports Graduation Certificate issued by the Director Sports, Punjab and other supporting certificates/documents in person to the University by 13th August 2024. Below is the complete schedule-

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The study was a retrospective observational study among 316,693 multicenter cohorts with type 2 diabetes across seven hospitals. Propensity score matching was performed to calibrate age, gender, medical year, baseline estimated glomerular filtration rate, urine protein by dipstick, and glycated hemoglobin for creating a balanced comparison. Finally, 13,096 metformin users were compared to 13,096 non-metformin users. The adverse key endpoints measured included a doubling of serum creatinine, eGFR ≤ 15 mL/min/1.73 m², and ESKD.

Results

The metformin group demonstrated significantly better renal outcomes than the non-metformin group:

• Doubling of Serum Creatinine: Metformin users had a lower incidence (HR 0.71; 95% CI 0.65-0.77).

• eGFR ≤ 15 mL/min/1.73 m²: The risk was significantly reduced (HR 0.61; 95% CI 0.53-0.71).

• End-Stage Kidney Disease (ESKD): Metformin users had a reduced risk (HR 0.55; 95% CI 0.47-0.66).

Subgroup Analyses

• The renoprotective effect of metformin was consistent across a wide array considering the radically wide variations in patient subgroups in terms of age, sex, comorbidities, and medications used concomitantly.

• The metformin group witnessed slower deterioration in renal function in nearly all subgroups than the non-metformin group.

The study stresses further that, as compared to other antihyperglycemic agents, metformin might be useful in the management of renal function in patients with T2D. Prior to this, there had been a lot of controversies hovering around the findings, but these results support very strongly metformin’s role in slowing down progression in DKD. This meta-analysis connected metformin use with a significantly reduced rate for the incidence of severe renal outcomes, especially with regard to doubling in serum creatinine and in those patients with extremely low levels of eGFR/ESKD.

The results clearly emerge that, in patients with T2D, clinicians should not consider metformin for glycemic control alone but also for renoprotection. The study suggested that metformin may be one of the effective tools in delaying the progression of DKD, improving overall patient outcomes.

Metformin significantly lowers the risk of decline in renal function among subjects with T2DM. This current study thus supports metformin therapy in T2DM patients in reducing DKD progression and hence adds more support to administration beyond simple glucose control.

Reference:

Wang, H.-H., Lin, S.-H., Hung, S.-Y., Chiou, Y.-Y., Hsu, W.-C., Chang, C.-M., Liou, H.-H., Chang, M.-Y., Ho, L.-C., Wu, C.-F., & Lee, Y.-C. (2024). Renal protective effect of metformin in type 2 diabetes patients. The Journal of Clinical Endocrinology and Metabolism, dgae477. https://doi.org/10.1210/clinem/dgae477

USA: A retrospective study suggested that a deep learning model matched the performance of radiologists in detecting clinically significant prostate cancer using multiparametric MRI. The findings were published online in Radiology Journal. 

In an internal test set of 400 exams, the researchers found that the deep learning model demonstrated performance comparable to experienced radiologists in detecting clinically significant prostate cancer, with both achieving an area under the receiver operating characteristic curve (AUC) of 0.89.  

Prostate cancer is cancer
that occurs in the prostate. The prostate is a small walnut-shaped gland in
males that produces the seminal fluid that nourishes and transports sperm. Prostate
cancer is one of the most common types of cancer. Multiparametric MRI of the prostate is a standard imaging
method used to diagnose clinically significant prostate cancer (csPCa) and
assist in targeting lesions during a biopsy. Considering this Jason C. Cai, et
al conducted a study to create a deep learning model using patient-level labels
to predict the presence of clinically significant prostate cancer (csPCa)
without information on tumor location and to compare its performance with that
of radiologists.

To achieve this, the
research team included 5,735 patients in the study, of whom 5,215 were male.
Out of these, 1,514 examinations (involving 1,454 patients) revealed clinically
significant prostate cancer (csPCa) and were conducted between January 2017 and
December 2019. For the internal test set, radiology reports were utilized,
while PI-RADS ratings were used for the external ProstateX test set. The study
compared performance using areas under the receiver operating characteristic
curves (AUCs) and the DeLong test and employed gradient-weighted class
activation maps (Grad-CAMs) to illustrate tumor localization.

They found the following
results: –

·
In the internal test set, which included 400
examinations, both the deep learning (DL) classifier and radiologists achieved
an AUC of 0.89. In the external test set, consisting of 204 examinations, the
DL classifier had an AUC of 0.86, while radiologists had an AUC of 0.84.

·
The combination of the DL classifier
and radiologists achieved an AUC of 0.89.

·
Grad-CAMs showed activation over the csPCa
lesions in 35 out of 38 true-positive cases in the internal test set and in 56
out of 58 true-positive cases in the external test set.

“The
deep learning model is expected to enhance the detection of prostate cancer and
improve the diagnostic accuracy of radiologists”, the researchers concluded.

Reference

Cai,
J. C., Nakai, H., Kuanar, S., Froemming, A. T., Bolan, C. W., Kawashima, A.,
Takahashi, H., Mynderse, L. A., Dora, C. D., Humphreys, M. R., Korfiatis, P.,
Rouzrokh, P., Bratt, A. K., Conte, G. M., Erickson, B. J., & Takahashi, N.
(2024). Fully Automated Deep Learning Model to Detect Clinically Significant
Prostate Cancer at MRI. Radiology, 312(2), e232635.
https://doi.org/10.1148/radiol.232635