Category Archives: Diabetes

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Most people with type 1 diabetes do not appear to be at increased risk for hospitalization or death from COVID-19 compared to the general population, new research suggests.

Two retrospective studies of type 1 diabetes and COVID-19 were published in the October issue of Diabetes Care.

One, by Roman Vangoitsenhoven, MD, PhD, of University Hospitals Leuven, Belgium, and colleagues, found no evidence of increased hospitalization for COVID-19 among people with type 1 diabetes during the first 3 months of the pandemic in Belgium.

The other, from Maria Vamvini, MD, of the Joslin Diabetes Center, Boston, Massachusetts, and colleagues, showed that age and glycemic control didn't differ significantly between adults with type 1 diabetes hospitalized for COVID-19 and those hospitalized for other reasons.

Previous data from the UK Biobank and the Type 1 Diabetes (T1D) Exchange support these findings.

Altogether, these results suggest that although the risk for death from COVID-19 is higher overall among people with type 1 diabetes, that increased risk is mostly limited to a subset of particularly vulnerable patients, said Catarina Limbert, MD, PhD, during a press briefing at the virtual annual meeting of the European Association for the Study of Diabetes (EASD).

"Those with type 1 diabetes dying from COVID-19 were a specific population," stressed Limbert, of the University Center of Central Lisbon and Hospital Dona Estefania, Lisbon, Portugal.

"They had hemoglobin A1c levels above 10% and were over age 50 with a long diabetes duration. They were the more fragile, who couldn't survive the severity and aggressiveness of the virus. Good glucose control is a good sign and protective," she added.

Daniel Drucker, MD, of Mount Sinai Hospital, Toronto, Canada who spoke at the EASD press briefing regarding potential mechanisms involved in COVID-19 morbidity in diabetes reiterated the importance of glycemic control.

He showed a slide with the following advice for patients with both types of diabetes during the pandemic in addition to the general and now-familiar physical distancing, personal hygiene, hand washing, and wearing of masks:

Prepare a list of all medications, written and on the phone.

Consider supplies of medications, test strips, and continuous glucose monitoring equipment.

Don't neglect exercise, diet, and blood glucoseand blood pressure control.

Use telemedicine and devices to communicate with healthcare professionals.

Maintain appropriate levels of hydration, exercise, and glucose and ketone monitoring.

Optimize glycemic control whenever possible.

In hospitalized patients with type 2 diabetes, medications may need adjustment. Insulin is often the preferred glucose-lowering prescription.

In the Belgian study, medical records were analyzed for a total of 2336 patients with type 1 diabetes who received care at two specialist diabetes centers. The hospital admission rate was compared with national population data.

Overall, 0.21% (n = 5) of the patients with type 1 diabetes were admitted to the hospital with COVID-19, similar to the 0.17% (n = 15,239) of the general population, as of April 30, 2020 (P = .76).

During the same period, 127 individuals with type 1 diabetes were hospitalized for reasons other than COVID-19, including poor glycemic control (22%), diabetic ketoacidosis (8%), planned surgery (21%), diabetic foot problems (5%), and delivery (5%).

"It is noteworthy that the number of hospitalizations for reasons other than COVID-19 exceeded by far the number of COVID-19related hospitalizations," Vangoitsenhoven and colleagues write.

"Interpretation of adverse outcomes of people with type 1 diabetes during the COVID-19 epidemic should therefore be performed cautiously, as overinterpretation of the impact of COVID-19 itself on adverse outcomes in people with type 1 diabetes is likely," they conclude.

The Boston study, which was smaller, involved retrospective chart reviews of seven patients with type 1 diabetes hospitalized with COVID-19 and another 28 patients hospitalized for other reasons, all during the period from March to May 2020. The groups didn't differ in outpatient insulin doses corrected for weight or in glycemic control in the months preceding admission.

Diabetic ketoacidosis (DKA) occurred in one patient with COVID-19 and in two of the non-COVID patients. Both groups had significant preexisting diabetes-related complications, including nephropathy in more than half of each group and receipt of an organ transplant with immunosuppression in 14% of each group.

The composite outcome intensive care unit (ICU) admission, intubation, or death occurred in two COVID-19 patients (both cases involved ICU admission without intubation, and both patients recovered) and in four non-COVID patients, of whom two died.

The two groups showed "remarkable" similarity in age and glycemic control, although the COVID-19 patients were more likely to be Black (four vs two), consistent with other retrospective studies.

None of the patients had new-onset type 1 diabetes, which contrasts with the 15% seen in the T1D Exchange study.

Just 1 of the 7 patients with COVID-19 (14%) had DKA, compared with 30% of the confirmed and probable COVID-19 patients in the T1D Exchange study.

The significant difference in age about 52 years in the current study vs 21 years in the T1D Exchange study might explain those differences, Vamvini and colleagues say.

Limbert has received grants and personal fees from Abbott, Ipsen, and Sanofi. Vangoitsenhoven has disclosed no relevant financial relaitonships. Vamvini was supported by the National Institute of Diabetes and Digestive and Kidney Diseases. Drucker receives research support, consulting fees, and/or lecture fees from Novo Nordisk, Merck, Pfizer, and Intarcia.

Diabetes Care. 2020 Oct;43:e118-e119. Vangoitsenhoven et al, Full text; 2020 Oct;43:e120-e122. Vamvini et al, Full text

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Emerging Data on Type 1 Diabetes and COVID-19 Reassuring - Medscape

INTRODUCTION

Insulin-producing cells do not exist in isolation, and their environment has substantial effects on their architecture and function. In addition to the adjacent , delta, ghrelin, pancreatic polypeptide, and other endocrine cells, the exocrine pancreas, vasculature, and innervation all modify cell organization and insulin release (1). Islets are innervated by autonomic parasympathetic and sympathetic fibers, as well as by sensory fibers (2, 3). Evidence from many studies over the past century has identified a critical role for neural signals in modulating insulin and glucagon release to regulate blood glucose (4). For example, anticipatory signals increase insulin release upon food consumption but before any changes in blood glucose, and neural signals suppress insulin and stimulate glucagon release to counteract hypoglycemia (4). Since central nervous system (CNS) and nerve stimulation studies demonstrate that neural signals can override the effects of circulating glucose (5, 6), neural modulation is an attractive target for therapies to improve metabolic control.

Our current understanding of islets and their innervation largely relies on traditional histological techniques using immunolabeled structures in thin sections. These studies have provided a wealth of knowledge about islet structure at high resolution. However, pancreata are highly heterogeneous (7), with distinct regional embryological origins. Sections also lack landmarks to precisely and consistently identify the location of internal structures (8). Until now, laborious serial sectioning and reconstruction have been needed to deliver information about islet anatomy throughout the pancreas. In addition, thin filamentous structures, such as nerves, are difficult to quantify and trace over large volumes using this approach. Recent studies have applied confocal imaging of small pieces and thick sections of cleared pancreatic tissue to examine endocrine innervation (914). These have revealed dense nerve processes within both mouse and human islets. However, given the heterogeneity in the pancreas, there is a clear need for high-resolution, organ-wide imaging to accurately quantify and map regional variation and to assess the three-dimensional (3D) relationship between islets and their environment in health and disease.

Here, we used a tissue-clearing technique, iDISCO+ (15), to determine the 3D distribution of insulin-producing cells, glucagon-producing cells, and neurofilament 200 kDa (NF200)positive innervation across the whole pancreas in healthy animals and in mouse models of diabetes. NF200 is a pan-neuronal marker expressed in sympathetic, sensory, and vagal neurons but, unlike other neural markers, is not expressed in pancreatic endocrine cells (1618). NF200 is expressed in small and large myelinated and small unmyelinated fibers (19), so examining NF200+ fibers provides a comprehensive overview of pancreatic innervation. In addition, NF200 protein levels are altered by nerve damage and repair (2022), so NF200 intensity may reflect remodeling of pancreatic nerves. Using whole-organ 3D imaging and analysis, we readily quantified cell volume and provide detailed information about islet distribution and heterogeneity in mouse and human pancreatic tissue from healthy and diabetic donors. We quantified the dense endocrine innervation and its regional variation and demonstrated significant differences between innervated and noninnervated islets. Islet nerve density is significantly increased in diabetic nonobese diabetic (NOD) mice, with streptozotocin (STZ) treatment, and greater in pancreatic tissue from diabetic human donors. We systematically quantified intrapancreatic ganglia and nerve contacts with and cells to demonstrate that these are largely preserved in diabetes. These findings constitute a 3D atlas of pancreatic innervation for pancreas and diabetes investigators examining pancreatic innervation, the regional heterogeneity in the healthy pancreas, and responses to metabolic disease. Our studies suggest that diabetes is associated with significant remodeling of neural inputs into islets and that neural contacts with endocrine cells are preserved in diabetes.

We applied tissue clearing and whole-organ 3D imaging to examine cell mass, expressed as cell volume, and islet number, as well as spatial distribution in whole pancreata from C57BL/6 mice (Fig. 1, A to C, and movies S1 and S2).

(A) Pancreatic dissection. Photo credit: A.A., Icahn School of Medicine at Mount Sinai. (B) Duodenal (left) and splenic (right) pancreas, maximum projection (1.3). Scale bars, 500 m. (C) Pancreata, maximum projection at 4 (left) and 12 (right). Scale bars, 500 and 200 m. (D) cell volume. (E) Insulin+ islets per cubic millimeter. (F) Insulin intensity (normalized to whole pancreas). (G) Insulin+ islet volume distribution (left axis) and median volume (right axis). Islets per group: 27,092/12,260/14,832. (H) 3D projection of insulin, NF200+ exocrine innervation, and NF200+ surfaces within insulin+ islets (yellow). (I) Exocrine nerve volume. (J) Endocrine nerve volume per insulin+ islet. (K) Endocrine nerve volume/islet volume. (L) Left: 3D model of pancreatic innervation (NF200, white) and insulin (green). Right: Distance transformation analysis with islet surfaces pseudocolored based on distance from the nearest nerve surface. Scale bar, 500 m. Boxed area magnified in the right panel. Scale bar, 200 m. Data are shown as means SEM or median 95% confidence interval as indicated. Analyses by unpaired t test, *P < 0.05 and **P < 0.01. T, total; D, duodenal; S, splenic. N = 7 (D to G) and N = 5 (I to K).

The total cell volume made up 1.31 0.17% of the total pancreatic volume (Fig. 1D), with a greater cell volume in the splenic region. In line with previous reports (23, 24), there were 3874 264.2 islets per pancreas, with 1822 230.4 in the duodenal and 2052 129 in the splenic regions. Islet density (islet number per cubic millimeter) did not differ significantly across the pancreas (Fig. 1E). Insulin intensity showed significant regional variation with intensity in the duodenal pancreas being 25% greater than that in the splenic region (Fig. 1F).

We next examined islet distribution throughout the pancreas to determine whether there were regional differences in cell volume per islet (Fig. 1G). Islets with cell volumes between 1000 and 50,000 m3 were the most abundant (39.29%), followed by islets in the 50,000 to 499,999 m3 range (36.58%). Very large islets (>500,000 m3) comprised 20% of the islet population, and insulin+ structures with volumes below 1000 m3, consisting of five or fewer cells, were the least abundant (3.13%).

There are reported differences in the origins of nerves innervating the duodenal and splenic pancreas (25). Therefore, we hypothesized that there may be regional variations in pancreatic innervation. Thus, we next analyzed the 3D distribution of the pan-neuronal marker NF200 in the healthy mouse pancreas to determine regional variations and relationship to islets (Fig. 1H and movies S3 to S5).

The exocrine nerve volume was 40% greater in the duodenal pancreas compared with the splenic region (Fig. 1I). Pancreatic islets were highly innervated compared to exocrine tissue, with an endocrine nerve density over sixfold greater than the exocrine nerve density. In addition, there was significant regional variation in islet innervation. Nerve volume per islet in the duodenal region was almost double that in the splenic region (Fig. 1J). This difference was more pronounced when the endocrine nerve volume was corrected for cell volume (Fig. 1K). These findings are consistent with marked regional variation in the density of islet innervation.

The proximity of nerves and endocrine cells may have important biological consequences. Autonomic neurotransmission occurs over 1 to 2 m (26), but endocrine and immune cells may influence nerve growth, repair, and function over longer ranges (27, 28). As a result, we examined the proportion of islets in contact with NF200+ fibers and the distance of each islet from the closest NF200+ fiber (Fig. 1L and movie S6). Only 6.1% of islets contained or were in contact with NF200+ fibers, with no significant difference between duodenal and splenic regions (Fig. 2A). The proportion of innervated (NF200+) islets increased with islet volume (fig. S1C). Most islets were within 250 m of an NF200+ fiber, and islets in the duodenal pancreas were significantly closer to nerves than those in the splenic pancreas (fig. S1A).

(A) Distribution of insulin+ islets (<1.6 and >1.6 m from the nearest nerve). Islets per group: 25,310/10,030/15,280. (B) Mean insulin+ islet volume NF200+ innervation; islets per group: 11,869/929/4690/325/7179/604. (C) Total insulin+ islet volume NF200+ innervation. (D) NF200 intensity sum normalized for insulin+ islet volume; islets per group: 5174/4530/2264/687/2196/1788/701/330/2978/2742/1563/357. (E) Intrapancreatic ganglia (NF200, magenta) and cells (insulin, green). Arrows mark ganglia. Scale bar, 50 m. (F) NF200+ intrapancreatic ganglia per cubic millimeter. (G) Intrapancreatic ganglia volume. Ganglia per group: 123/43/80. (H) Distance between intrapancreatic ganglia and insulin+ islets. Ganglia per group: 123/43/80. (I) cells contacting nerves per islet. Islets per group: 69/40/29. Data are shown as means SEM or median 95% confidence interval as indicated. Analyses by Kruskal-Wallis test with Dunns test (B to D) or unpaired t test (F to I), ***P < 0.001. T, total; D, duodenal; S, splenic. N = 5 (A to D), N = 3 (F to H), and N = 4 (I).

To test the hypothesis that innervated islets differ from those without innervation, we then analyzed islet volume based on whether islets were innervated by NF200+ fibers, hypothesizing that neural signals may play a role in determining islet size. NF200-innervated islets were 10-fold larger than islets without NF200 innervation (Fig. 2B and fig. S1B), and as a result, innervated islets made up 43% of the total cell volume in the pancreas (Fig. 2C). Both innervated and noninnervated islets in the splenic region were larger than those in the duodenal pancreas (Fig. 2B).

Next, we analyzed the intensity of NF200+ immunostaining within each islet. NF200 protein levels are associated with structural stability of nerves and increased conduction velocity, so NF200+ immunostaining intensity may have functional correlates (29, 30). While the largest islets were more likely to be innervated, the intensity of NF200+ immunostaining was twofold greater in the smallest islets compared to the largest islets and greater in subpopulations of duodenal islets (Fig. 2D).

These data demonstrate that innervated islets are a small fraction of the total islet number but are significantly larger than islets without NF200 innervation and form a substantial portion of the total cell volume. These findings suggest the potential involvement of NF200+ nerves in islet development and cell growth.

Intrapancreatic ganglia integrate inputs from the sympathetic and parasympathetic nervous systems and provide significant islet innervation (31). Regional differences in ganglia size in the pancreas have been reported (32). Intrapancreatic ganglia are sparse (21.5 2.5 ganglia/mm3; Fig. 2, E and F), with an average volume of 83,467 10,646 m3 (Fig. 2G) and located close to islets (47.3 5.7 m; Fig. 2H). There were no significant regional differences in ganglia density, size, or location.

To assess whether islet innervation could directly influence endocrine cell function through neural signals, we quantified the number of cells contacting NF200+ nerves. Only 9.4 2.2% of cells contacted NF200+ nerves (Fig. 2I) with no regional difference. As expected, a larger number of cells contacted nerves in large islets compared to small islets (fig. S1D), but the proportion of cells contacting NF200+ nerves did not differ with islet size (fig. S1E). In aggregate, these data provide a comprehensive 3D atlas of the anatomy and NF200+ innervation of the entire mouse endocrine and exocrine pancreas that can be used as a benchmark to assess the effects of specific pancreatic innervation during development and in disease.

The 3D relationships between islets and innervation across the whole endocrine pancreas are largely unknown in diabetes. Hence, we determined how pancreatic anatomy and cell innervation were affected in a mouse model of type 1 diabetes (T1D). NOD mice provide a model of diabetes with autoimmune cell destruction and spontaneous T1D development. We examined the 3D structure of NF200+ innervation and islets in nondiabetic NOD mice (average nonfasting blood glucose, 115 4 mg/dl) and diabetic NOD mice (average nonfasting blood glucose, 495 62 mg/dl; Fig. 3A and movies S7 and S8).

(A) Pancreata from nondiabetic and diabetic NOD mice [maximum projection at 1.3 (top), 4 (middle), and 12 (bottom); scale bars, 2000, 500, and 200 m]. (B) cell volume. (C) Insulin+ islets per cubic millimeter of pancreatic tissue. (D) Insulin intensity (normalized against total pancreas, nondiabetic). (E) Insulin+ islet volume distribution (left axis) and median volume (right axis). Islets per group: 11,404/6285/5119/4057/2203/1854. (F) Exocrine nerve volume. (G) Endocrine nerve volume per insulin+ islet. (H) Endocrine nerve volume by islet volume. (I) Distribution of insulin+ islets located <1.6 and >1.6 m from the nearest nerve. (J) Mean insulin+ islet volume NF200+ innervation. Islets per group: 8815/857/4296/436. (K) Total insulin+ islet volume NF200+ innervation. (L) NF200 intensity sum normalized for insulin+ islet volume. Islets per group: 4941/3341/1209/383/2862/1586/189/73. (M) Intrapancreatic ganglia per cubic millimeter. (N) Intrapancreatic ganglia volume. Ganglia per group: 112/82. (O) Distance between intrapancreatic ganglia and insulin+ islets. Ganglia per group: 111/54. (P) cells contacting nerves per islet. Islets per group: 28/14. Data are shown as means SEM or median 95% confidence interval where indicated. Analyses by one-way ANOVA with Tukeys test (B to D and F to G), Kruskal-Wallis with Dunns test (H and J to L), or unpaired t test between diabetic and nondiabetic groups (H). *P < 0.05, **P < 0.01, and ***P < 0.001. T, total; D, duodenal; S, splenic. N = 7 nondiabetic and N = 7 diabetic (B to E, P); N = 8 nondiabetic and N = 7 diabetic (F to L); N = 6 nondiabetic and N = 6 diabetic (M to O).

Across the whole pancreas, islet density and cell volume in female nondiabetic NOD mice were similar to that seen in male C57BL/6 mice (Figs. 1, D and E, and 3, A to C). In female diabetic NOD mice, the cell volume was significantly lower across the whole pancreas, reduced to 10% of the volume in nondiabetic NOD mice in both splenic and duodenal regions (Fig. 3B). The islet number was also significantly reduced in diabetic NOD mice, particularly in the splenic, but not duodenal pancreas (Fig. 3C). However, the intensity of insulin immunostaining was preserved in the remaining islets that were detected in diabetic NOD mice (Fig. 3D). There was a significant inverse correlation between blood glucose levels and both islet number and cell volume (fig. S2A).

The volume distribution of insulin+ islets in nondiabetic NOD mice was also comparable to C57BL/6 mice (Fig. 3E). However, islet volume distribution was significantly shifted in diabetic NOD mice, with marked loss of larger islets. Insulin+ islets over 50,000 m3 were reduced by more than half, and the median islet volume decreased by more than 50%. The loss of large islets was particularly notable in the duodenal pancreas (Fig. 3E).

Together, these data demonstrate marked decreases in insulin+ islet number and volume and marked alterations in islet volume distribution in diabetic compared to nondiabetic NOD mice, particularly in the duodenal pancreas. Our data also suggest that the remaining islets in diabetic NOD mice maintained their insulin content.

Previous studies have reported alterations in pancreatic innervation in mouse models of diabetes (13, 3336). Therefore, we examined pancreatic innervation in NOD mice to determine effects on nerve density in the different regions of the pancreas (movies S9 and S10).

Nerve density in insulin+ islets was increased more than twofold in diabetic NOD mice (Fig. 3, G and H), particularly in the splenic pancreas. Islet nerve density in the splenic pancreas positively correlated with blood glucose (fig. S2C). The regional differences in endocrine nerve density observed in C57BL/6 mice were absent in nondiabetic NOD mice. There was no difference in exocrine nerve density between nondiabetic and diabetic NOD mice and no correlation with blood glucose (Fig. 3F and fig. S2B).

Previous studies suggest that neural signals contribute to cell survival (37), so increased islet innervation could result from differences in the susceptibility of innervated and noninnervated islets to immune destruction. To test this, we examined the proportion of NF200+ islets (islets containing or in contact with NF200+ fibers) in NOD mice. We did not see any significant change in the proportion of NF200+ islets (14.6 versus 9.8% islets in nondiabetic and diabetic NOD mice, respectively; Fig. 3I). However, the proportion of NF200+ islets was increased in a subset of islets with volumes between 50,000 and 500,000 m3 in diabetic NOD mice (fig. S2F). The median distance between islets and nerves was similar in diabetic and nondiabetic NOD mice for the total pancreas but significantly reduced in the splenic pancreas (fig. S2D).

In keeping with the results in C57BL/6 mice, NF200+ islets were significantly larger than NF200 islets in both diabetic and nondiabetic NOD mice (Fig. 3J), although, as expected, the average volume of both NF200 and NF200+ islets decreased in diabetic NOD mice. Innervated insulin+ islets remained 60% of the total cell volume in both diabetic and nondiabetic NOD mice (Fig. 3K).

In published studies, the intensity of NF200 immunostaining decreases with nerve damage and increases in nerve regeneration (20, 22). To indirectly assess the effects of autoimmune diabetes on nerve integrity in islets, we examined the intensity of NF200 immunostaining in diabetic and nondiabetic NOD mice and found that the intensity of NF200 immunostaining was significantly increased in islets from diabetic NOD mice (Fig. 3L).

We next examined intrapancreatic ganglia to determine whether autoimmune diabetes altered their distribution or size. There was no significant difference in intrapancreatic ganglia density (18.9 5.2 versus 28.2 10.1 ganglia/mm3, nondiabetic versus diabetic NOD mice, respectively; Fig. 3M) or volume (61,779 5961 versus 59,348 6977 m3, nondiabetic versus diabetic NOD mice, respectively; Fig. 3N), but the distance between intrapancreatic ganglia and islets increased fourfold in diabetic NOD mice (40 5.3 versus 171.7 17.6 m, nondiabetic versus diabetic, respectively; Fig. 3O).

Next, we examined the proportion of cells in contact with NF200+ fibers in nondiabetic and diabetic NOD mice. Despite a significant increase in islet nerve density, there was no significant change in the proportion of cells contacting nerves in diabetic NOD mice (Fig. 3P).

Autoimmune cell destruction principally affects cells in NOD mice resulting in islets composed primarily of glucagon+ cells. The changes in cell innervation in mouse models of diabetes are largely unknown. In diabetic NOD mice, glucagon staining is clearly present, but glucagon+ cells from a single islet may form several clusters rather than a clearly defined, single islet (Fig. 4, A and B). As previously reported (38), the ratio of glucagon to insulin volume (Fig. 4C) was significantly increased in diabetic NOD mice (movies S11 and S12). In nondiabetic NOD mice, NF200 nerve density in cell clusters was markedly higher than nerve density in insulin+ islets. Nerve density in diabetic NOD mice was unchanged (Fig. 4D). The proportion of innervated cell clusters was similar to that of innervated insulin+ islets in nondiabetic NOD mice and increased twofold in diabetic NOD mice (Fig. 4E). In keeping with increased NF200 nerve density in cell clusters of nondiabetic NOD mice, the proportion of cells contacting NF200+ fibers was more than fivefold higher than cells contacting NF200+ fibers in nondiabetic NOD mice. However, the proportion of cell nerve contacts did not change in diabetic mice (Fig. 4F).

(A) Maximum projections of light-sheet images of pancreatic samples from nondiabetic and diabetic NOD mice stained for insulin (green), NF200 (magenta), and glucagon (blue) and imaged at 4 magnification. Scale bars, 200 m. (B) cell volume corrected for pancreatic volume in NOD mice. (C) Glucagon+ cell volume as a percentage of insulin+ cell volume in NOD mice. (D) NF200+ nerve volume within glucagon+ cell clusters in NOD mice. (E) Glucagon+ cell cluster volume (left axis) and median nerve distance (right axis) in NOD mice. (F) Percentage of cells contacting nerves per islet. Number of islets: 23/16. Data are shown as mean SEM or as median 95% confidence interval where indicated. Analyses by one-way ANOVA with Tukeys test (D) or Kruskal-Wallis with Dunns test (B, C, and E). *P < 0.05. T, total; D, duodenal; S, splenic. N = 3 nondiabetic and N = 3 diabetic NOD mice.

In summary, insulin+ islet nerve density and NF200 immunostaining are increased in the surviving insulin+ islets of diabetic NOD mice, and cell contacts with NF200+ fibers are preserved. cell nerve density and cell contacts with NF200+ fibers are greater than contacts with cells, and cell nerve density also increases in diabetic NOD mice.

On the basis of our findings in NOD mice, we hypothesized that nerve density may progressively increase in surviving islets during the development of diabetes. To test this hypothesis, we examined the time course of changes in insulin+ islets and pancreatic nerves in mice with STZ-induced diabetes, as well as in age- and sex-matched C57BL/6 mice. Diabetes secondary to multiple low-dose STZ treatment is likely induced by both direct cell toxicity and islet inflammation. Therefore, using a standard 5-day low-dose STZ model, we examined NF200, insulin, and glucagon staining in mice sacrificed 5 and 15 days after completion of STZ treatment (nonfasting blood glucose: 259 18 and 430 17 mg/dl, respectively) and compared these to untreated littermate controls (nonfasting blood glucose: 123 9 mg/dl; Fig. 5A and movies S13 and S14).

(A) Pancreata at days 5 (left) and 15 (right) after STZ treatment, maximum projections at 1.3 (top), 4 (middle), and 12 (bottom). Scale bars: 1000, 500, and 200 m. (B) cell volume. (C) Insulin+ islets per cubic millimeter. (D) Insulin intensity (normalized against total pancreas, control). (E) Insulin+ islet volume distribution (left axis) and median volume (right axis). Islets per group: 10,479/4682/5797/10,091/5162/4929/14,380/7543/6837. (F) Exocrine nerve volume. (G) Endocrine nerve volume per insulin+ islet. (H) Endocrine nerve by islet volume. Data are shown as mean SEM or as median 95% confidence interval where indicated. Analyses by one-way ANOVA with Tukeys test for comparison between control, STZ day 5, and STZ day 15, and unpaired t test for comparison between duodenal and splenic pancreas (B to D and F to H) or Kruskal-Wallis with Dunns test (E); *P < 0.05, **P < 0.01, and ***P < 0.001. T, total; D, duodenal; S, splenic. N = 5 control, N = 6 STZ day 5, and N = 7 STZ day 15 (B to E); N = 6 control, N = 5 STZ day 5, and N = 5 STZ day 15 (F to H).

First, we analyzed islet number and total cell volume to determine the time course and effects of STZ treatment, hypothesizing that STZ may differentially affect these parameters in different pancreatic regions. As expected in this model, total cell volume was reduced to 40% of control, and intensity of insulin immunostaining was decreased by 50% with STZ treatment (Fig. 5, B to D). Islet number and cell volume negatively correlated with blood glucose in STZ-induced diabetes (fig. S3A). STZ treatment did not significantly alter the distribution of cell volumes throughout the pancreas, suggesting that its effects were uniform across islets of all sizes (Fig. 5E). However, there was a significant decrease in the individual islet volume, first in the duodenal pancreas at day 5 and later at day 15 in both the duodenal and splenic regions. These findings demonstrate that STZ treatment progressively reduces total cell volume, intensity of insulin immunostaining, and the volume of individual islets, with significant differences in the time course and extent of these changes between duodenal and splenic pancreas.

We next examined pancreatic innervation in STZ-treated mice to determine the time course and regional distribution of effects on nerve density (movies S15 and S16). Nerve density in the exocrine pancreas was significantly increased 15 days after STZ treatment (Fig. 5F and fig. S3B). STZ treatment significantly increased islet innervation and islet nerve density by twofold on day 5 (Fig. 5, G and H). Islet nerve density was significantly correlated with blood glucose (fig. S3A).

To test the hypothesis that neural signals may play a role in cell preservation, we assessed whether STZ treatment had differential effects on islets based on whether they contained NF200+ nerves or not. STZ treatment led to a progressive increase in the proportion of NF200+ islets across the duodenal and splenic pancreas (Fig. 6A) and all islet sizes (fig. S3F) but did not reach significance (P = 0.14). STZ treatment significantly reduced the distance between insulin+ islets and NF200+ fibers on day 5, primarily in the splenic pancreas (fig. S3D). In both control and STZ-treated mice, innervated islets are significantly larger than noninnervated islets but decline in volume with STZ treatment (Fig. 6B). The total volume of innervated islets, but not of noninnervated islets, significantly decreased with STZ treatment (Fig. 6C) but remained 54% of the remaining total cell volume.

(A) Distribution of insulin+ islets located <1.6 and >1.6 m from the nearest nerve. (B) Mean volume for insulin+ islets NF200+ innervation. Islets per group: 10,199/929/5300/366/9837/1022. (C) Total volume for insulin+ islets NF200+ innervation. (D) Intensity of NF200 immunolabeling normalized for insulin+ islet volume. Islets per group: 3013/2762/1837/605/2842/1791/1057/336/5800/3274/1500/386. (E) Ganglia per cubic millimeter. (F) Volume of intrapancreatic ganglia. Ganglia per group: 114/73/97. (G) Distance between intrapancreatic ganglia and insulin+ islets. Ganglia per group: 114/73/97. (H) Percentage of cells contacting nerves per islet. Islets per group: 69/28/69. Data are shown as means SEM or as median 95% confidence interval where indicated. Analyses by Kruskal-Wallis with Dunns test (B to D) for comparison between control, STZ day 5, and STZ day 15, and unpaired t test for comparison between duodenal and splenic pancreas (E to H). *P < 0.05, **P < 0.01, and ***P < 0.001. T, total; D, duodenal; S, splenic. N = 6 control, N = 5 STZ day 5, and N = 5 STZ day 15 (A to D); N = 6 control, N = 3 STZ day 5, and N = 3 STZ day 15 (E to H).

To determine whether STZ-induced diabetes modified the expression of NF200, we assessed changes in intensity of NF200 immunostaining in relation to insulin+ islet volume and time after treatment (Fig. 6D). The intensity of NF200 immunostaining (corrected for cell volume) was significantly increased in the largest islets (>500,000 m3) 5 days after STZ treatment and by twofold to fourfold in all islets at 15 days after STZ treatment. These findings demonstrate that STZ treatment increases exocrine and endocrine nerve density and NF200 expression, results that are in keeping with increased nerve growth.

We next examined intrapancreatic ganglia in mice treated with STZ to determine whether cell destruction changed their density or size. While STZ treatment did not change intrapancreatic ganglion density or distance from the islet, there was a 30% decrease in ganglion volume 15 days after STZ treatment (Fig. 6, E to G). Similar to our findings in NOD mice, although islet nerve density increased with STZ treatment, the proportion of cells contacting NF200+ fibers did not change significantly (Fig. 6H).

We next assessed cell volume and nerve density in cell clusters in STZ-treated mice. STZ treatment increased the ratio of glucagon+ to insulin+ cell volume, but total glucagon+ cell volume was reduced after 15 days (Fig. 7, A to C, and movie S17). NF200 nerve density in cell clusters was significantly increased in STZ-treated mice (Fig. 7D), but the proportion of innervated cell clusters did not change (Fig. 7E). Similarly, the proportion of cells contacting NF200+ fibers was not significantly altered by STZ treatment (Fig. 7F).

(A) Pancreata at days 5 (left) and 15 (right) after STZ treatment. Insulin, green; NF200, magenta; glucagon, blue. Imaged at 4 magnification. Scale bars, 200 m. (B) Quantification of cell volume corrected for pancreatic volume in STZ-treated mice. (C) Quantification of glucagon+ cell volume as a percentage of insulin+ cell volume in STZ-treated mice. (D) Quantification of NF200+ nerve volume within glucagon+ cell clusters in STZ-treated mice. (E) Glucagon+ cell cluster volume (left axis) and median nerve distance (right axis) in STZ-treated mice. (F) Percentage of cells contacting nerves per islet. Islet number: 98/37/53. Data are shown as means SEM or as median 95% confidence interval where indicated. Analyses by one-way ANOVA with Tukeys test (C to D) or Kruskal-Wallis with Dunns test (B and E). *P < 0.05, **P < 0.01, and ***P < 0.001. T, total; D, duodenal; S, splenic. N = 6 control, N = 4 STZ day 5, and N = 6 STZ day 15.

In summary, 3D representation faithfully represents the progressive reduction in islet number, cell volume, and intensity of insulin immunostaining in response to STZ treatment. Further, STZ treatment increases insulin+ islet nerve density, the proportion of innervated islets, and intensity of NF200 immunostaining. cell nerve density is increased with STZ treatment, but the proportion of and cells that are in contact with NF200+ fibers is not significantly altered in STZ-treated mice.

Islet innervation differs between species (3, 39) and the 3D relationships between islets and pancreatic nerves in healthy versus diabetic patients remain largely unknown. To assess these, islets and NF200+ innervation were examined in small, cleared pancreatic samples from healthy human donors and donors with type 2 diabetes (T2D; Table 1) by light-sheet imaging to assess islet distribution and relationship to innervation (Fig. 8A and movies S18 and S19).

CVA, cardiovascular accident; HbA1c, hemoglobin A1C; N/A, not applicable; PFA, paraformaldehyde; M, male; F, female.

(A) Maximum projections of pancreatic samples from human donors without (C1 to C5) and with type 2 diabetes (DM2; D1 to D3) at 1.3. Scale bars, 1000 m. (B) cell volume. (C) Insulin+ islets per cubic millimeter. (D) Insulin+ islet volume distribution (left axis) and median volume (right axis). (E) Exocrine nerve volume. (F) Endocrine nerve volume per insulin+ islet. (G) Endocrine nerve volume corrected for insulin+ islet volume. (H) Distribution of insulin+ islets located at <1.6 and >1.6 m from nerves. Islets per group: 28,315 control and 6790 DM2. (I) Mean volume of insulin+ islets NF200+ innervation. Islets per group: 25,519/236/7448/345. (J) Total volume of insulin+ islets NF200+ innervation. (K) Intrapancreatic ganglia (NF200, magenta; confocal, 20). Boxed areas magnified in lower panels with cell bodies indicated by arrows. Scale bars, 50 m (top) and 25 m (bottom). (L) Ganglia per cubic millimeter. (M) Volume of intrapancreatic ganglia. Ganglia per group: 31/12. (N) Distance between intrapancreatic ganglia and insulin+ islets. Ganglia per group: 31/12. (O) cells contacting nerves per islet. Islets per group: 73/28. Data are shown as means SEM or as median 95% confidence interval where indicated. Analyses by unpaired t test (B to G and L to M), Mann-Whitney test (H), or Kruskal-Wallis with Dunns test (I to J). *P < 0.05, **P < 0.01, and ***P < 0.001. T, total; D, duodenal; S, splenic. N = 5 control and N = 3 DM2.

As expected, total cell volume (Fig. 8B) and islet number (Fig. 8C) were highly variable (40). The cell volume (as a percentage of the total pancreatic sample volume) was lower in the diabetic donors, varying between 0.47 and 2.2% in the control group and 0.85 and 0.97% in the diabetic group. Islet numbers ranged from 66 to 200 islets/mm3 in the control group to 58 to 287 islets/mm3 in the diabetic group. While islet number per cubic millimeter was greater in humans than in mice, the cell volume (%) was very similar in murine and human tissues. The cell volume distribution in nondiabetic pancreata was not significantly different to that in mice (Fig. 8D). Larger islets were disproportionately reduced, and the mean volume of an individual islet was significantly lower in diabetic compared to healthy individuals.

In human samples from healthy donors, NF200+ innervation was similar between endocrine (0 to 0.89% nerve volume per islet) and exocrine tissue (0.06% to 0.94% nerve volume per exocrine tissue; Fig. 8, E to G). Exocrine nerve volume, endocrine nerve volume per islet, islet nerve density, and proportion of innervated islets were greater in diabetic individuals (Fig. 8H). In nondiabetic individuals, innervated insulin+ islets are significantly larger than those without innervation, in line with the findings in mice, but innervated insulin+ islets are a smaller proportion of the total islet volume than seen in mouse pancreata. In diabetic individuals, innervated islets are significantly smaller than in nondiabetic individuals (Fig. 8, I and J).

We next assessed intrapancreatic ganglia in human pancreatic samples. Human intrapancreatic ganglia were larger than those found in mice (Fig. 8K), but ganglion density and distance from islets were similar to C57BL/6 and nondiabetic NOD mice. There was no significant difference in ganglia size between nondiabetic and diabetic donors (Fig. 8, L to N). Last, we examined contacts between NF200+ nerves and cells. The proportion of cells in contact with NF200+ fibers was half of that in control mice (4.15 versus 9.44%) and was preserved in individuals with diabetes (6.07%; Fig. 8O).

Together, cell volume and distribution in human pancreata were comparable to murine pancreata, and innervated islets were significantly larger than noninnervated islets. In samples from individuals with diabetes, exocrine and endocrine innervation as well as proportion of innervated islets were increased, and nerve contacts with cells persist.

The pancreas is composed of multiple cell types, is richly vascularized, and is densely innervated by sympathetic, parasympathetic, and sensory nerves. We wanted to compare our analyses of pancreatic innervation using the pan-neuronal marker, NF200, with pathway-specific pancreatic innervation. Using a modified iDISCO+ protocol specifically optimized for pancreatic tissue, we examined tyrosine hydroxylase (TH) immunolabeling to mark sympathetic nerve fibers (movie S20) and vesicular acetylcholine transporter (VAChT) immunolabeling to identify parasympathetic nerve fibers (movie S21) across the mouse pancreas. There was more TH+ (Fig. 9, A to D) and VAChT+ (Fig. 9, H to K) innervation than NF200+ innervation in both the exocrine and endocrine pancreas. In keeping with our findings examining NF200+ fibers, TH and VAChT nerve density were threefold to more than sixfold greater in the endocrine than in the exocrine pancreas. The proportion of islets containing or in contact with TH+ fibers was 27.7% (Fig. 9E) compared to 35.0% for VAChT+ fibers (Fig. 9L). In line with our findings with NF200, both TH (Fig. 9F) and VAChT (Fig. 7M) innervated islets were significantly larger than noninnervated islets, and as a result, the majority of insulin+ islet volume is composed of innervated islets (Fig. 9, G and N).

(A) Maximum projections of TH and insulin. Scale bars, 2000 m at 1.3 and 200 m at 4. (B) TH+ exocrine nerve volume. (C) TH+ endocrine nerve volume per insulin+ islet. (D) TH+ endocrine nerve volume by insulin+ islet volume. (E) Distribution of insulin+ islets located at <1.6 and >1.6 m from TH+ nerves. Islets per group: 10,610/4773/5837. (F) Mean insulin+ islet volume for insulin+ islets with and without TH+ innervation. Islets per group: 8542/3314/4699/1320/3843/1994. (G) Total volume for insulin+ islets with and without TH+ innervation. (H) Maximum projections of VAChT and insulin. Scale bars, 2000 m at 1.3 and 200 m at 4. (I) VAChT+ exocrine nerve volume. (J) VAChT+ endocrine nerve volume per insulin+ islet. (K) VAChT+ endocrine nerve volume corrected for insulin+ islet volume. (L) Distribution of insulin+ islets located at <1.6 and >1.6 m from VAChT+ nerves. Islets per group: 12,661/7165/5496. (M) Mean volume for insulin+ islets with and without VAChT+ innervation. Islets per group: 8542/3314/4699/1320/3843/1994. (N) Total volume for insulin+ islets with and without VAChT+ innervation. Data are shown as means SEM or as median 95% confidence interval where indicated. Analyses by unpaired t test (B to E and I to L) or Kruskal-Wallis with Dunns test (F, G, M, and N). **P < 0.01 and ***P < 0.001. T, total; D, duodenal; S, splenic. N = 5.

The optimized iDISCO+ protocol was also effective for labeling fibers expressing TRPV1 (transient receptor potential cation channel, subfamily V, member 1) and synapsin (fig. S4). In addition, we applied a novel alternative approach to visualize islet vasculature by combining insulin immunolabeling with fluorophore-tagged dextran or CD31 antibody, followed by optical clearing using ethyl cinnamate (ECi) to preserve fluorescence while allowing for additional immunostaining tissue (fig. S4) (41). These data demonstrate that modification of optical clearing protocols allows for visualization of multiple markers in pancreatic tissue.

In summary, NF200, TH, and VAChT immunostaining all demonstrate that large islets have enriched islet innervation and that large innervated islets represent at least half of total pancreatic islet volume. However, analysis of pancreatic innervation using TH and VAChT immunostaining suggests that endocrine nerve density is greater than revealed by NF200 immunostaining.

Tissue clearing, 3D imaging, and unbiased image analysis have been widely used in the CNS to provide new insights into anatomical pathways and patterns of regional activation. However, there have been few applications in peripheral organs such as the pancreas. Whole-organ clearing and imaging are especially suited for the mapping of filamentous structures, particularly to delineating innervation across large distances that can be difficult to achieve using traditional serial sections and 2D imaging. Tissue clearing has been used previously in thick pancreatic sections (350 to 1000 m) (9, 10, 12, 4244) and small pieces of pancreatic tissue (11) and then imaged with optometry or high-magnification confocal microscopy for detailed analysis [see (4547) for review]. This has provided important information about islet characteristics and structural relationships over a close range. In particular, previous studies using 3D imaging in pancreatic sections, fetal tissue, and young mice have provided data about islet innervation (9, 10, 13, 14, 42, 48). Our data extend these important published studies. Different pancreatic regions have diverse embryological origins and variations in islet density and function and are supplied by neurons from different extrapancreatic ganglia. Without assessing pancreatic structure across the whole organ, our understanding and quantification of pancreatic anatomy, including possible regional differences, are incomplete and possibly inaccurate.

Tissue clearing and volume imaging of the pancreas provided several new insights. Innervation of the endocrine pancreas is significantly enriched compared to the surrounding exocrine pancreas, with marked regional variation. Islets are closely associated with pancreatic innervation, and innervated islets are significantly larger than noninnervated islets, in both mouse and human. Intrapancreatic ganglia are sparse and close to islets. Almost half of cells and a tenth of cells contact NF200+ fibers, irrespective of islet size or location. Last, islet nerve density and expression of NF200 are increased in the remaining islets of two mouse models of T1D, with temporal and regional differences, and greater in human T2D, in keeping with nerve remodeling.

3D imaging across the whole pancreas provides straightforward measurement of multiple islet characteristics and identifies significant regional differences that would be laborious or impossible to obtain by serial sectioning. We readily measured cell volume across multiple pancreata, and our findings are in agreement with previous studies at 1 to 2% in mouse pancreas (24) and at 1 to 4% in human pancreas (49, 50). Increased islet volume in the splenic pancreas is in keeping with previous observations using 2D histology, in isolated islets and in transgenic mice (51, 52). Intensity of insulin immunostaining was significantly lower in the splenic pancreas. The splenic pancreas contains significantly larger islets, and previous studies report lower insulin immunostaining intensity and fewer insulin granules in large islets, while other studies demonstrate lower c-peptide content in cells from the splenic pancreas (53, 54). Our approach also facilitates rapid analysis of islet volume distribution across the pancreas. The majority of islets were between 1000 and 500,000 m3, equivalent to islet diameters of 12 to 98 m (assuming spherical islets), with around a fifth of islets having volumes larger than 500,000 m3. Whole-organ imaging demonstrated significant differences in islet biology between diabetes models. Islet number and cell volume were reduced in diabetic NOD mice, with the intensity of insulin immunostaining relatively preserved in some islets and a notable shift to small islets. Islet number and volume were also reduced with STZ treatment, but intensity of insulin immunostaining was markedly reduced, and size distribution was minimally altered. Together, these observations validate tissue clearing and 3D imaging as a reliable straightforward method to assess cell volume and other characteristics across the entire pancreas.

Whole-tissue 3D imaging confirmed dense pancreatic innervation and revealed markedly greater nerve density in the endocrine pancreas, over sixfold greater than in the exocrine pancreas of mice. These findings confirm the results of previous studies reporting close association between islets and nerves using 2D histology and 3D examination of pancreatic sections (3, 9, 10). We extended these findings to show that endocrine NF200+ innervation was not uniform throughout the pancreas but enriched in the duodenal portion. These regional differences may reflect the distinct embryological origins of the duodenal and splenic regions. Further studies will determine whether regional differences in pancreatic innervation contribute to reported regional differences in islet composition, size, function, and susceptibility to immune loss.

3D analysis of islets and innervation across the whole pancreas revealed previously unknown features of the close anatomical relationship between islets and nerves. In mice and human samples, innervated islets are a relatively small fraction of all islets by number, but they are, on average, 10-fold larger than noninnervated islets. As a result, innervated islets represent around half of the total cell volume. The large volume of innervated islets is in accordance with a role for neural signals in islet development and maintenance. In both mice and zebrafish, cells aggregate close to pancreatic nerves in development, and islet architecture is disrupted by loss of neural signals (55, 56). There is also close physical association between nerves and islets in human embryos, particularly in the middle and late trimester, when there is rapid development of the endocrine pancreas (55). Less is known about the role of neural signals in cell maintenance, but vagotomy reduces cell replication in rats (57). Vagotomy disrupts the afferent and efferent signals to several intra-abdominal organs, so further studies are needed to determine whether loss of neural signals, specifically to the pancreas, disrupts islet structure and function during development and after birth.

Islets are closely associated with the pancreatic duct and highly vascularized, so it is possible that the proximity between nerves and islets is related to innervation of the duct or vessels. Islet blood vessels are richly innervated, and neural signals have marked effects on islet blood flow (58). However, several studies suggest that vascular signals actually reduce endocrine cell differentiation during development, and in zebrafish, islets remain densely innervated even in the absence of islet vascularization.

Although NF200-innervated islets are large, on average, NF200 immunostaining intensity is greater in smaller innervated islets. NF200 expression has been linked to nerve diameter and conduction velocity, so it is possible that differences in NF200 immunostaining intensity may have functional consequences (29, 30). In human pancreata, small islets have a greater proportion of cells compared to other endocrine cell types and higher insulin content (59). Similarly, small rat islets were functionally superior to larger islets in ex vivo studies and after transplantation (60). In keeping with previous work (3), NF200+ nerves contact a small proportion of cells in each islet, with no proportional differences between pancreatic regions or islet sizes. The proportion of innervated cells is similar to the percentage of cells that are reported to act as hub cells in the islets, and hub cells are reported to be modulated by cholinergic agonists (61). Although a minority of cells contact NF200+ nerves, neural signals could influence activity across multiple cells through electrical coupling. In contrast, and in keeping with previous work (3), NF200+ nerves contact a much greater proportion of cells, which lack gap junctions. Whether variation in NF200 immunostaining intensity or proximity of individual cells to nerves contributes to functional heterogeneity of cells is currently unknown and warrants further studies.

The development of diabetes in NOD mice and in STZ-treated mice is associated with rapid and significant increases in islet nerve density. In NOD mice, increased nerve/islet volume suggests that nerve volume may be preserved, while cell volume is reduced in surviving islets. The proportion of innervated cell clusters increased in diabetic NOD mice. In STZ-treated mice, nerve volume per islet, nerve density per islet, and nerve density in cell clusters are all increased. These findings suggest that increased nerve density is restricted to the remaining insulin+ islets in NOD mice, while both cell and cell nerve density is increased in STZ-treated mice. The increases in insulin+ islet nerve density in diabetic NOD and STZ-treated mice are similar in magnitude to nerve density changes in response to physiologically relevant stimuli. In the CNS, fasting leads to an almost twofold increase in agouti related neuropeptide (AgRP)/neuropeptide Ypositive (NPY+) terminals in the paraventricular nucleus, a major pathway regulating food intake (62). In the peripheral nervous system, skin inflammation increased sensory nerve density twofold and was associated with increased sensitivity to thermal and mechanical stimuli (63). Further studies will be required to test the functional consequences of increased islet nerve density.

The intensity of NF200 immunostaining was significantly increased in the islets of diabetic NOD and STZ-treated mice. NF200 staining intensity increases in response to nerve regeneration (20, 21), so up-regulation of NF200 may reflect ongoing regeneration of islet innervation. These findings, and the time course of the increased nerve density with STZ treatment, are highly suggestive of nerve regeneration; reported rates of nerve regrowth after crush injury are up to 4 mm/day, and restoration of electrical activity in peripheral nerves after chemical injury occurs within days. Our findings may be responses to STZ directly, to hyperglycemia, and to inflammatory processes and/or interactions between endocrine cells and neurons to regulate neural density. Increased nerve density is reported in response to inflammation in several tissues and their adjacent structures, and these changes can either be protective or exacerbate inflammation (64, 65). Increased insulin+ islet nerve density in NOD mice, and the increase in exocrine and endocrine nerve density in STZ-treated mice, may reflect the major sites of immune activity/inflammation. Both hyperglycemia and STZ treatment increase cell production of nerve growth factor (NGF) (66). Its receptor, tyrosine kinase receptor A (TrkA), is expressed on both sympathetic and sensory nerves, and previous 2D imaging studies report that sensory and sympathetic nerve density are increased with STZ treatment (33). In previous studies, NGF overexpression in cells significantly increased sympathetic islet innervation (67). The cross-talk between nerves and islets in healthy versus diabetic tissue remains largely unstudied.

There is considerable variability in the reported changes in cell mass in models of diabetes. In our studies in diabetic NOD mice, cell volume was not significantly higher as a proportion of total pancreatic volume, but the ratio of to cell volumes was significantly increased (Fig. 4, B and C). Similar to our findings, Plesner et al. (68) describe a non-significant increase in cell mass in prediabetic and diabetic NOD mice and a significant increase in the proportion of cells/islet area in diabetic NOD mice. Our longitudinal studies show changes in cell volume with time after STZ treatment, with a significant decrease 2 weeks after the completion of treatment. This is in keeping with previous studies (69), but the cell response to STZ treatment has also been described to increase (70), to remain unchanged (71), or to vary with time after STZ treatment (72).

In our studies, and cell contacts are maintained in diabetic NOD and STZ-treated mice. One limitation of our assessment is that we quantify the number of endocrine cells contacting nerves, but we cannot quantify the number of contacts per endocrine cell. It is possible that the number of contacts with or cells is modified in diabetes. Sympathetic fibers also contact delta cells and vasculature to a lesser extent (3) in mouse islets. Further work is needed to determine whether NF200+ fibers contact delta cells or other islet structures and the effects of diabetes and STZ treatment on these contacts.

Our results examining innervation in NOD mice are consistent with recent 3D imaging of thick pancreatic sections (9, 13) reporting regions with increased innervation in these mice. Prior 2D studies have reported loss of islet sympathetic innervation in NOD mice (35), but these studies used different neural markers and examined NOD mice with longer duration of diabetes. It is possible that the increased islet innervation in NOD mice we observe is lost with increasing duration of diabetes. Alternatively, there may be pathway-specific changes such that sympathetic innervation is reduced, but parasympathetic and/or sensory innervation are increased, leading to an increase in NF200+ innervation found in our studies. Future studies using iDISCO+ will be required to dissect the longitudinal changes and contribution of specific neural pathways in mouse models of T1D, as well as the associations between innervation and immune infiltration.

There are several differences between mouse and human islets as well as similarities between these species. Human cell volume (%) was similar to the proportion of insulin+ islets in C57BL/6 mice. Islet size distribution was also remarkably similar between mouse and human pancreata. Similar to the findings in mice, intrapancreatic ganglia in human tissue are sparse and close to islets, but they are markedly larger, of the order of 200 neurons on average. There have been conflicting results about islet innervation in human versus murine samples. In our studies, the proportion of innervated islets and the finding that innervated islets were larger than noninnervated were similar in both humans and mice. Initial 2D imaging reported reduced islet innervation in human samples, but recent data from optically cleared human samples using markers for sympathetic nerves suggest that human islets, like mouse islets, have a dense neural network (9, 11). In keeping with previous studies examining TH+ fibers in human pancreatic samples (3, 11), innervation density is similar in human exocrine and endocrine pancreas. We found that NF200+ innervation is present in human islets, in keeping with previous studies demonstrating TH+ fibers in islets (3, 11), but at a lower density than mouse islets. A smaller proportion of human cells contact NF200+ fibers compared to mouse islets.

There are also similarities between innervation in STZ-treated mice and in samples from T2D individuals. In pancreata from T2D donors, nerve volume per islet, nerve density, and the proportion of innervated islets are all increased. The human tissue samples we analyzed provide a snapshot of islets and innervation from postfixed tissue as well as from individuals with variable comorbidities, age, and time from death. These factors likely contribute to the sample variability, in line with previous human data (73). In aggregate, our data suggest that islet innervation is present in human islets, albeit at lower levels than mouse islets, and innervation appears to be at least preserved, possibly increased, in human T2D individuals. The increase in exocrine innervation in pancreatic tissue from T2D donors may reflect more generalized pancreatic pathology that is increasingly recognized as a feature of T2D. One limitation of our studies is that we did not examine insulin islets by glucagon staining in the human samples, so it is unknown whether whole islet nerve density or cell contacts are altered in T2D. Further studies examining specific neural pathways and further endocrine cell types in human pancreatic tissue are required to fully assess normo- and pathophysiological species differences.

Our studies using NF200 as a neural marker do not differentiate between parasympathetic, sympathetic, and sensory fibers. Using an optimized iDISCO+ protocol, we examined sympathetic and parasympathetic innervation in wild-type mice, and many findings mirror those seen with NF200+ innervation. Similar to our findings using NF200, both sympathetic and parasympathetic endocrine innervation are enriched compared to exocrine innervation, and innervated islets are significantly larger than noninnervated islets. These findings differ from published studies that show similar TH+ innervation density in exocrine and endocrine tissue (74). However, previous reports analyzed innervation in female mice sampling cryosections every 400 m rather than innervation across the whole pancreas. The proportion of innervated-to-noninnervated islets is also greater when assessed using sympathetic and parasympathetic markers. The distribution of TH+ and VAChT+ innervation differs, with several large volume TH+ fibers contributing to higher TH+ volume compared to VAChT+ innervation in the exocrine pancreas. In keeping with previous reports in adult mice (75, 76), we observed occasional TH+ cells. We excluded these, as far as possible, based on their morphology, volume, and overlap with insulin immunostaining, but it is conceivable that our estimate of TH+ innervation may be an overestimate. TH+ and VAChT+ endocrine innervation are higher than for NF200. However, while NF200 has been reported to be expressed in a wide range of myelinated and unmyelinated fibers, our results and previous studies suggest that NF200 does not label all fibers (19), and it is possible that we may have overlooked alterations in NF200 fibers in our studies. Alternative pan-neuronal markers have significant limitations. For example, protein gene product 9.5 (PGP9.5) is expressed in islet endocrine cells and innervation. Pathway-specific markers are also imperfect. TH labels most, but not all, sympathetic nerve fibers since there are also populations that are TH but express NPY (77). VAChT immunostaining is primarily in the terminal neuronal arborization and so visualizing larger cholinergic nerve fibers may be incomplete (78). While there are similarities among innervation patterns with NF200, TH, and VAChT, our studies do not allow us to determine whether the changes in pancreatic innervation with diabetes are generalized or specific to sympathetic, parasympathetic, or sensory pathways. Future work will assess important pathway-specific changes in pancreatic innervation and their contacts with specific endocrine cell types in both mouse and human metabolic disease. One disadvantage of iDISCO+ is that it does not preserve endogenous fluorescence. Therefore, we also developed and validated a novel alternative approach that combines immunostaining and tissue clearing with ECi for use in adult murine tissues (41). This preserves endogenous fluorescent signals while allowing for antibody labeling of additional targets. ECi clearing also provides a less toxic alternative to iDISCO+ (41). A combination of fluorescently tagged dextran to delineate blood vessels, immunostaining for innervation and islets, and ECi tissue clearing will allow us to further assess the organ-wide association between innervation, islets, and vasculature.

In summary, we have used whole-organ tissue clearing and imaging to create a 3D atlas mapping islets and innervation across the pancreas as a tool to quantify cell mass, define islet characteristics, map pancreatic innervation, and assess the anatomical interaction between islets and innervation in healthy and diabetic mice and humans. This approach demonstrates dense islet innervation and identifies distinguishing features of innervated islets and the regional differences. Such regional variations illustrate the importance of whole-organ imaging when assessing pancreatic anatomy. Our studies confirm that innervation is present in human islets and directly contacts cells. We demonstrate that islet innervation is markedly increased in diabetic NOD mice, STZ-treated mice, and likely in diabetic human pancreata. In combination with up-regulation of NF200 immunostaining, this suggests increased rapid reorganization of pancreatic innervation and possible nerve growth within islets. Future studies will identify the neurochemical characteristics, time course, and functional consequences of these changes. Intrapancreatic ganglia and nerve contacts in islets are maintained in diabetes. The tissue clearing and imaging approaches we have used and optimized are broadly applicable to investigating pancreatic structures and innervation in other diseases, such as pancreatitis and pancreatic cancer, and are relevant to imaging vasculature and innervation in other organs. Our data also have important translational implications. Our data suggest that the close association between islets and pancreatic nerves is maintained in human T2D; therefore, the anatomical pathways that would allow for targeted neuromodulation to regulate pancreatic function are preserved. Defining pancreatic neurocircuitry is crucial to understanding neural regulation of pancreatic function, as it elucidates anatomical pathways for direct effects on endocrine cells. Future studies will determine critical interactions between cells and nerves, whether variation in islet innervation density is associated with differences in islet function, and whether metabolic disease leads to functional deficits in islet innervation independent of structure.

Ad libitum fed C57BL/6 mice were maintained under controlled conditions (12-hour light/12-hour dark cycle, 22C). NOD mice (NOD/ShiLtJ, the Jackson Laboratory, Bar Harbor, ME, USA) and STZ-treated mice were used to model T1D. Female NOD mice aged 12 to 16 weeks with two consecutive blood glucose measurements of >300 mg/dl (morning, nonfasting) were termed diabetic. Littermates with blood glucose <200 mg/dl were used as nondiabetic controls. Multiple low-dose STZ-treated mice (males, aged 10 weeks) were generated by treating C57BL/6N mice (Charles River, Wilmington, MA, USA) intraperitoneally with freshly made STZ (40 mg/kg; Sigma-Aldrich, St. Louis, MO) in citrate-saline buffer (pH 4.5) for five consecutive days and euthanizing them at 5 or 15 days following the final STZ injection. NonSTZ-treated littermates were used as controls. All protocols were approved by the Institutional Animal Care and Use Committee.

Mice were anesthetized with isoflurane (3%) and perfused with heparinized saline followed by 4% paraformaldehyde (PFA; Electron Microscopy Sciences, Hatfield, PA, USA). Pancreata were dissected, cleared of adipose tissue, divided into duodenal and splenic regions (Fig. 1A), with the gastric lobe included with the duodenal lobe, and postfixed overnight in 4% PFA at 4C. For antibody evaluation experiments, small pancreatic samples (2 to 3 mm diameter) were assessed. On the following day, the tissue was washed in phosphate-buffered saline (PBS; 3) before proceeding with optical clearing protocols.

Human samples (Table 1) were obtained from Prodo Laboratories Inc. (Aliso Viejo, CA, USA) and postfixed in 4% PFA. Since human samples were processed upon acquisition and not simultaneously as with mouse tissue, we could not compare staining intensity between samples. All samples were harvested from the superior margin of the tail of the pancreas.

Whole-organ staining and clearing were performed using iDISCO+ (15). Dissected pancreata were dehydrated [20, 40, 60, 80, and 100% methanol at room temperature (RT)], delipidated [100% dichloromethane (DCM; Sigma-Aldrich, St. Louis, MO, USA)], and bleached in 5% H2O2 (overnight, 4C). Pancreata were rehydrated (80, 60, 40, and 20% methanol) and permeabilized [5% dimethyl sulfoxide/0.3 M glycine/0.1% Triton X-100/0.05% Tween-20/0.0002% heparin/0.02% NaN3 in PBS (PTxwH)] for 1 day. Pancreata were then placed in blocking buffer [PTxwH + 3% normal donkey serum (Jackson ImmunoResearch, West Grove, PA, USA)] at 37C overnight. Samples were incubated with primary antibodies (table S1) in blocking buffer for 3 or 6 days (small pancreatic pieces and hemipancreata, respectively) at 37C. After five washes with PTxwH at RT (final wash overnight), samples were incubated with secondary antibodies in blocking buffer (1:500) for 3 or 6 days. Samples were washed with PTxwH (five times, RT) and PBS (five times, RT), dehydrated with a methanol gradient, then washed in 100% methanol (three times, 30 min each) and DCM (three times, 30 min each), and then transferred to dibenzyl ether (DBE; Sigma-Aldrich) to clear. Primary antibody specificity was confirmed in pancreatic tissue from reporter mice expressing tdTomato in defined neural populations. There was no immunolabeling without primary antibodies using iDISCO+ or ECi. A modified iDISCO+ protocol used 0.5% Triton X-100 and 0.1% Tween-20 for the permeabilization, blocking, and primary and secondary antibody buffers.

A modified ECi tissue clearing protocol was used for samples from animals injected intravenously with fluorophore-tagged dextran (100 m, 25 mg/ml) or a direct conjugated CD31 antibody (100 m, 50 mg/ml). Tissue was harvested, postfixed, and washed with PBS as described above. Samples were incubated with 3% H2O2 (10 min, RT), washed in PBS with 0.2% Triton X-100 (Ptx2; three times over 3 h, RT), and incubated overnight in PTx2 + heparin (10 mg/ml; PTwH) and 3% normal donkey serum at RT. Samples were incubated with primary antibodies in PTwH with 3% normal donkey serum (2 days, RT) followed by PTwH washes (four times over 4 hours). Samples were incubated with secondary antibodies in PTwH with 3% normal donkey serum (2 days, RT), followed by PTwH washes as above. Optical clearing was achieved by incubating samples in 50% ethanol, 70% ethanol, 100% ethanol (all pH 9, 4 hours, 4C), 100% ethanol (pH 9, overnight, 4C), and finally one wash and one overnight incubation (RT) in ECi (Sigma-Aldrich) before imaging.

Z-stacked optical sections were acquired with an UltraMicroscope II (LaVision BioTec, Bielefeld, Germany; 1.3, 4, or 12 magnification with dynamic focus with a maximum projection filter). Human samples were imaged at 1.3 with dynamic focus and with multiple Z-stacks acquired at 4 with 20% overlap and tiled using the plugin TeraStitcher through the ImSpector Pro software (LaVision BioTec). Spatial resolutions of light-sheet images were 5 m by 5 m by 5 m at 1.3, 1.63 m by 1.63 m by 5 m at 4, and 0.602 m by 0.602 m by 2 m at 12.

Small mouse pancreatic sections were imaged in glass-bottom eight-well chambers (Ibidi, Grfelfing, Germany) filled with immersion media DBE or ECi and imaged using an inverted Zeiss LSM 880 confocal microscope with a 10 [numerical aperture (NA), 0.3] objective and a step size of 5 m. Spatial resolution for confocal images acquired at 10 was 1.67 m by 1.67 m by 5 m.

Imaris versions 9.1 to 9.3.1 (Bitplane AG, Zrich, Switzerland) were used to create digital surfaces covering the islets (1.3 and 4 images) and innervation (4 images) to automatically determine volumes and intensity data. Volume reconstructions were performed using the surface function with local contrast background subtraction. For detection of islets, the threshold factor corresponded to the largest islet diameter in each sample. For detection of nerves, the threshold factor was set to 12.2 m. A smoothing factor of 10 m was used for islets analyzed at 1.3, and a factor of 3.25 m was used for analysis of islets and nerves at 4. For detection of TH+ nerves, TH+ cells (75, 76) were manually removed from the final TH+ nerve surface by excluding volumes below 120 m3 residing within insulin+ islets and overlapping with insulin staining. The Imaris Distance Transform Matlab XTension function was used to calculate the distance of each islet surface from the innervation surface. Distances of islets are reported as the intensity minimum of the distance transformation channel (intensity 0 = islet touching nerve) for each islet surface to the nerve surface as calculated by the distance transformation operation. In confocal images, digital surfaces were created to cover nerves and individual cells, cells, or ganglia. For detection of ganglia, a region of interest was manually created around each individual ganglion to create a digital surface specifically covering cell bodies, but not nerve fibers. The Imaris Distance Transform Matlab XTension was then used as above to determine the distance between ganglia and insulin+ islets or the distance between nerves and individual or cells with a distance of 0 indicating a nerve contact. Limitations to our analyses of endocrine cell contacts include the following: We may not have captured / cells with lower staining intensity; in some cases, we could not completely separate adjacent endocrine cells and therefore counted multiple adjacent cells as a single cell; our method quantifies the number of endocrine cells contacting nerves but does not allow for quantification of number of contacts per endocrine cell.

Data are shown as means SEM. Distribution was assessed by Shapiro-Wilk test. Significance was determined by unpaired two-way t test or one-way analysis of variance (ANOVA) with post hoc Tukeys multiple comparisons test (Gaussian distribution), Mann-Whitney test, or Kruskal-Wallis test followed by Dunns multiple comparisons test (nonparametric distribution). Significance was set at an level of 0.05.

Acknowledgments: Funding: A.A. was supported by a senior postdoctoral fellowship from the Charles H. Revson Foundation (grant no. 18-25) and a postdoctoral scholarship from the Swedish Society for Medical Research (SSMF). This work was supported by the American Diabetes Association Pathway to Stop Diabetes Grant ADA #1-17-ACE-31 and, in part, by grants from the NIH (DK105015, P-30 DK020541, U01MH105941, R01NS097184, OT2OD024912, and UC4DK104211), JDRF (2-SRA-2017-514-S-B), and the Alexander and Alexandrine Sinsheimer Scholar Award. This work was supported in part by a Mindich Child Health and Development Institute Pilot and Feasibility Grant. Microscopy and image analysis were performed at the Microscopy CoRE at the Icahn School of Medicine at Mount Sinai. We wish to thank the Human Islet and Adenoviral Core (HIAC) of the NIDDK-supported Einstein-Sinai Diabetes Research Center (DRC) and the families of the donors. Author contributions: A.A., A.G.-O., A.F.S., and S.A.S. conceived and designed the study and interpreted the data. A.A. performed all light-sheet experiments and analyzed and interpreted the data. A.A., M.J.-G., and R.L. performed confocal experiments and analyzed and interpreted the data. C.R., M.J.-G., and A.A. provided STZ-treated mice. C.R. and A.G.-O. provided NOD mice and human samples. N.T. and Z.W. provided technical and methodological input. A.A. and S.A.S. drafted the manuscript with input from all other authors. All authors approved of the final submitted version of this paper. Competing interests: S.A.S. is a named inventor of the intellectual property, Compositions and Methods to Modulate Cell Activity, and is a co-founder of, consults for, and has equity in the private company Redpin Therapeutics (preclinical stage gene therapy company developing neuromodulation technologies). The authors declare that they have no other competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

Continued here:
A 3D atlas of the dynamic and regional variation of pancreatic innervation in diabetes - Science Advances

SAN DIEGO--(BUSINESS WIRE)--Tandem Diabetes Care, Inc. (NASDAQ: TNDM), a leading insulin delivery and diabetes technology company, plans to release its third quarter 2020 results after the financial markets close on Thursday, November 5, 2020. The Company will hold a conference call and simultaneous webcast on the same day at 4:30 pm Eastern Time (1:30 pm Pacific Time), to discuss its third quarter 2020 financial and operating results.

Conference Call/Webcast Details:Date: November 5, 2020Time: 4:30 pm Eastern Time (1:30 pm Pacific Time)Toll Free Dial-In Number: (855) 427-4396International Dial-In Number: (484) 756-4261Conference ID: 8072078Webcast Link: https://edge.media-server.com/mmc/p/mp7mdi2q

An archive of the webcast will be available for 30 days following the event on Tandem Diabetes Cares Investor Center website located at http://investor.tandemdiabetes.com in the Events & Presentations section.

About Tandem Diabetes Care, Inc.

Tandem Diabetes Care, Inc. (www.tandemdiabetes.com) is a medical device company dedicated to improving the lives of people with diabetes through relentless innovation and revolutionary customer experience. The Company takes an innovative, user-centric approach to the design, development and commercialization of products for people with diabetes who use insulin. Tandems flagship product, the t:slim X2 insulin pump, is capable of remote software updates using a personal computer and features integrated continuous glucose monitoring, and optional automated insulin delivery technology. Tandem is based in San Diego, California.

Follow Tandem Diabetes Care on Twitter @tandemdiabetes, use #tslimX2 and $TNDM.Follow Tandem Diabetes Care on Facebook at http://www.facebook.com/TandemDiabetes.Follow Tandem Diabetes Care on LinkedIn at http://www.linkedin.com/company/TandemDiabetes.

Tandem Diabetes Care is a registered trademark and t:slim X2 is a trademark of Tandem Diabetes Care, Inc.

See more here:
Tandem Diabetes Care to Announce Third Quarter 2020 Financial Results on November 5, 2020 - Business Wire

Diabetes: Your diet play an important role in maintaining healthy blood sugar levels

Diabetes is a progressive condition and chronic condition which requires constant management of blood sugar levels. If diagnosed early, the progression can be slowed considerably with therapeutic diet and physical activity. But if left uncontrolled, the blood sugar levels may start affecting different organs of the body. It is advised that diabetics should keep a constant check on the blood sugar levels and follow all necessary precautions to avoid major fluctuations.

Physical activity helps in controlling progression of the condition. But what is not clear is the ideal dietary pattern to be followed. There is a lot of uncertainty in nutrition claims, what was true yesterday may have some other aspects to reconsider with emerging research studies. One such research is about how effective is a plant-based diet in managing diabetes. In this article, Mrs. Sweedal Trinidade who is anutritionist and senior dietetics officer at P.D. Hinduja National Hospital explains in detail the pros and cons of a plant-based diet and its effect on diabetes.

A plant-based diet focuses more on eating legumes, whole grains, vegetables, fruits, nuts, and seeds with little or no animal products.

1) No additives: Plant-based diets are minimally or not processed. So, no additives may contribute to insulin resistance.

2)Fibre: Plant-based diets are rich in cellulose and hemicellulose. Both are majorly responsible for maintaining glycemic index of the meals and improving postprandial blood sugar levels.

3) Probiotics: More commonly known as food for gut-friendly bacteria. They are the type of fibre, found in fruits, vegetables and legumes are fermented by intestinal bacteria to produce short-chain fatty acids, which also improve sugar metabolism by increasing insulin sensitivity.

4) Antioxidant-rich: Plant-based diets are usually rich in various antioxidants like-

a) Polyphenols that inhibit glucose absorption and stimulate insulin secretion

b) Magnesium promotes insulin sensitivity thereby improving sugar metabolism

Also read:Diabetes Diet: Know How Many Almonds You Should Eat To Lower Blood Sugar Levels

Diabetes diet: A healthy diet can help in controlling blood sugar levelsPhoto Credit: iStock

Glycaemic index and glycaemic load of food: Plant-based diets are rich in fibre thereby improving satiety, reducing calorie density of meals. They maintain the glycaemic index of the foods and reduce glycemic load of the meals. A perfect balance can help in improving blood sugars considerably.

Also read:Diabetes: Exercise Tips To Manage Blood Sugar Levels; Benefits Of Exercising For Diabetics

Now the question is why India is known as diabetes capital when the diet followed is predominantly vegetarian? This makes it important to highlight the downside of plant-based diet:

In case you decide to rely completely on plant-based diet a lot of thought has to go in planning and balancing the diet or else you will land up in the following:

1) Plant-based diets are loaded with carbs usually: This may affect blood sugars.

2) Less of complex carbohydrates and more of simple sugars: This will greatly impact the glycemic load of meals and ultimately the blood sugar levels.

3) Proteins deficiency: If the diet is not planned properly you may miss out on essential amino acids and also land up consuming protein-deficient diet. Hence having foods in right combination can compensate for this. Example right combination of cereals and pulses might help.

Many following plant based diet suffer from protein deficiencyPhoto Credit: iStock

4) Vitamins and minerals: Calcium and iron, no doubt many vegetables are rich in minerals like iron and calcium but due to presence of phytates, oxalates and fiber the bioavailability is low.

5) Vitamin B12: Plant diets are deficient in vitamin B12. Hence it is important to include foods fortified in Vitamin B12.

Also read:5 Nuts And Seeds Loaded With Omega-3 Fatty Acids You Must Add To Your Plant-Based Diet

It is very important to analyse the pros and cons before adopting any diet. Finally, striking the right balance and ensuring no nutritional deficiencies can not only promote good health but also optimal glycemic control!

(Mrs. Sweedal Trinidade, Senior Dietetics Officer, Dietary Services, P.D. Hinduja National Hospital and MRC, Mumbai)

Disclaimer: This content including advice provides generic information only. It is in no way a substitute for qualified medical opinion. Always consult a specialist or your own doctor for more information. NDTV does not claim responsibility for this information.

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Diabetes: Can A Plant Based Diet Help Control Blood Sugar Levels? Nutritionist Shares The Pros And Cons Of This Diet - NDTV

The number of people with type 2 diabetes is expected to exceed the five million mark by 2030. The number of people that officially have diabetes is estimated to be one million off the actual figure. Both this discrepancy and rise can be attributed in part to the way we prioritise imminent danger.

Human evolution has hardwired us to process pain and find ways to avoid it.

This instinct is invaluable for survival but it proves to be a bug when it comes to chronic diseases, such as diabetes.

Diabetes symptoms do not usually cause pain or make you unwell so it is easy to be blindsided to its damaging effects.

The symptoms, however subtle, should not be ignored because they usually signal something serious is up.

READ MORE:Diabetes type 2 warning - the six 'less well-recognised' symptoms of high blood sugar

According to Doctor Aragona Giuseppe, GP and medical advisor at Prescription Doctor, one subtle symptom that may spell serious problems is needing to urinate more than usual, particularly at night.

According to Dr Giuseppe, the reason for increased urination is because when you have diabetes the excess glucose builds up in your blood and your kidneys are made to work overtime to filter and absorb the excess glucose, hence the need to wee more often.

This is also the reason why people become more thirsty with type 2 diabetes - another telltale sign something serious is up, she explained.

"When your kidneys cant keep up this excess glucose is excreted into your urine which takes fluids from your bodily tissues which then leaves you dehydrated, meaning you are constantly thirsty," said Dr Giuseppe.

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Other serious warning signs include:

As Dr Giuseppe explained, the reason people with type 2 diabetes lose weight more rapidly is because the low levels of insulin prevents the body from getting glucose from the blood and into the bodys cells to use as energy, this means that the body starts burning fat and muscle for energy which means rapid weight-loss.

According to the NHS, see a GP if:

"You'll need a blood test, which you may have to go to your local health centre for if it cannot be done at your GP surgery," explains the health body.

As it points out, the earlier diabetes is diagnosed and treatment started, the better.

If you are diagnosed with type 2 diabetes, you are usually recommended to make lifestyle changes to control your blood sugar levels.

High blood sugar levels are a constant threat if you have type 2 diabetes but you can stabilise your blood sugar by making healthy dietary decisions.

There's technically nothing you cannot eat if you have type 2 diabetes, but you'll have to limit certain foods.

Generally you should avoid starchy items, such as white pasta and bread because these foods can send blood sugar levels soaring.

That's because simple carbohydrates are broken down into glucose (blood sugar) relatively quickly.

In addition, physical exercise helps lower your blood sugar level.

"You should aim for 2.5 hours of activity a week," advises the NHS.

Here is the original post:
Type 2 diabetes symptoms: The sign when peeing that may signal a 'serious' problem - Express

This article was originally published here

BMJ Open. 2020 Aug 20;10(8):e034723. doi: 10.1136/bmjopen-2019-034723.

ABSTRACT

INTRODUCTION: Depression and diabetes are highly disabling diseases with a high prevalence and high rate of comorbidity, particularly in low-income ethnic minority patients. Though comorbidity increases the risk of adverse outcomes and mortality, most clinical interventions target these diseases separately. Increasing physical activity might be effective to simultaneously lower depressive symptoms and improve glycaemic control. Self-management apps are a cost-effective, scalable and easy access treatment to increase physical activity. However, cutting-edge technological applications often do not reach vulnerable populations and are not tailored to an individuals behaviour and characteristics. Tailoring of interventions using machine learning methods likely increases the effectiveness of the intervention.

METHODS AND ANALYSIS: In a three-arm randomised controlled trial, we will examine the effect of a text-messaging smartphone application to encourage physical activity in low-income ethnic minority patients with comorbid diabetes and depression. The adaptive intervention group receives messages chosen from different messaging banks by a reinforcement learning algorithm. The uniform random intervention group receives the same messages, but chosen from the messaging banks with equal probabilities. The control group receives a weekly mood message. We aim to recruit 276 adults from primary care clinics aged 18-75 years who have been diagnosed with current diabetes and show elevated depressive symptoms (Patient Health Questionnaire depression scale-8 (PHQ-8) >5). We will compare passively collected daily step counts, self-report PHQ-8 and most recent haemoglobin A1c from medical records at baseline and at intervention completion at 6-month follow-up.

ETHICS AND DISSEMINATION: The Institutional Review Board at the University of California San Francisco approved this study (IRB: 17-22608). We plan to submit manuscripts describing our user-designed methods and testing of the adaptive learning algorithm and will submit the results of the trial for publication in peer-reviewed journals and presentations at (inter)-national scientific meetings.

TRIAL REGISTRATION NUMBER: NCT03490253; pre-results.

PMID:32819981 | DOI:10.1136/bmjopen-2019-034723

Continued here:
mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study - DocWire...

This transcript has been edited for clarity.

Hello, my name is Kamlesh Khunti. I'm professor of primary care diabetes and vascular medicine at the University of Leicester.

Thank you for joining me on this video regarding prevention of diabetes.

As you all know the prevalence of type 2 diabetes is a serious threat to sustainability of health systems internationally. But there is good quality evidence from randomised controlled trials that behavioural interventions that support people who are at high risk, such as those with impaired glucose tolerance (IGT), to lose weight, or to develop a healthy dietary lifestyle, and increasing physical activity, can reduce the risk of type 2 diabetes.

There've been a number of studies, including systematic reviews, that have shown that it doesn't matter which country the study has come from, the risk reduction is about 50% in those people who are at high risk and given intensive lifestyle interventions to prevent diabetes.

There are differences in terms of the risk reductions because the progression rates by IFG and IGT combined, IGT or IFT on its own, or HbA1c, are slightly different. But no matter what the criteria used to diagnose diabetes, we know that all these intensive lifestyle interventions can lead to weight loss, and which would subsequently lead to prevention of diabetes.

Evidence

The longest running study is the Da Qing study, which showed that at 6 years there was a 50% reduction in risk of developing diabetes in those with the IGT. And the 20 year follow up showed that there was still a 43% reduction in the incidence of developing type 2 diabetes. These were all efficacy trials where the participants had volunteered to go into a trial, and they had very, very intensive lifestyle interventions, which are not really applicable, or even sustainable, in a real-world setting.

So there's been a number of what we call effectiveness studies that have been conducted to reduce the incidence of diabetes and those at high risk. And again, a number of studies have been carried out what we call translational studies - and all of them have showed that we can improve risk factors in terms of developing diabetes.

And also, some studies, meta analyses, are showing that we can reduce the risk of diabetes in a real- world setting, and more data, even more data, for reductions in weight.

Guidelines

So on the basis of this a number of guidelines have been published. In the UK we have the National Institute for Health and Care Excellence (NICE) guidelines for England, which have said that we should be using some form of a risk score to identify those people who are at high risk.

And then those who are high on the risk score would have a blood test to see if they're high in terms of the blood test, and this could be a fasting glucose, or glucose tolerance test, or HbA1c.

Since the HbA1c criteria have come out, the majority of people are opting for an HbA1c. And people with an HbA1c of anything from 6% to 6.4% would be classed as being at high risk of diabetes, and they would be appropriately referred to a national Diabetes Prevention Programme. That started in England many years ago.

And this has led to a number of benefits over the years which I'll come to shortly.

In terms of the NICE guidance, NICE guidance showed that it was cost effective to identify people using the two-step approach where one would use a risk score and then refer patients to a diabetes prevention programme.

And indeed, they showed that in certain populations, such as those of ethnic minority populations, not just referring patients from age 40 to 74 as is the case in England, but referring patients from 25 to 39 of Black and Minority Ethnic (BAME) health groups would not only be cost effective, but also cost saving as well.

Recently, there's been a publication of the national programme. England has had the only national programme for diabetes prevention, and this showed that by December 2018 over 150,000 people have been referred and attended the initial assessment, of which 96,000 have attended at least one of the 13 group-based intervention sessions.

And intention to treat analysis within this observational study showed there was a 2.3 kg reduction in weight, and a 1.26 mmol/mol reduction in HbA1c. In terms of complete case analysis, there was about a 3.3 kg reduction in weight.

COVID-19

However, in view of COVID there's been a lot of debate - what should we be doing in terms of the prevention programme? Because first of all, it's been difficult to see these patients in clinical practice to have their blood tests done, HbA1c, for example, and then having the face-to-face group face education programmes.

Diabetes has really been heightened in the era of COVID because we know diabetes is one of the strong risk factors for severe COVID and hospitalisation. There's been a number of meta-analyses that have been done, all of them showing that people with diabetes have a two-fold increased risk of being hospitalised with COVID, and a two-fold increased risk of dying from COVID as well.

There's also now some good quality data showing that glycaemic control is associated with severe COVID and mortality. So, well-controlled people have a lower risk of mortality in those people who have diabetes. Whether the risk in people with pre-diabetes holds, we are still awaiting some results.

However, glucose does seem to be a risk factor for people with COVID and severe COVID.

So, our inclination would be to try and prevent diabetes. Also, physical fitness is important, so losing weight would be also beneficial in terms of contracting COVID and the risk associated with COVID.

In view of COVID, those people at high risk are now being given advice that they can have a risk score done. This is the Leicester Diabetes Risk Score, that is an online risk score available on the Diabetes UK website. Patients can assess their risk and those who are at high risk can enrol themselves to a risk reduction programme, the diabetes prevention programme, which is again an online programme.

This is a major announcement that was made by Sir Simon Stephens [NHS England chief executive], that the programme was now rolled out, all virtually, both in terms of risk assessment and for the prevention programme.

Unprecedented Times

This is exciting times, worrying times, but at the same time exciting times, because we do now work in an era of virtual consultations, and now also virtual programmes.

And we know that there are a number of digital support programmes globally that can be available, including wearable technologies. In the longer term, we will need to see if these programmes are effective, but we are working in unprecedented times.

We know diabetes is a risk factor for COVID severity and mortality, and whether people with diabetes in the pre-diabetes range are at high risk is to be asserted. But current data are showing that hyperglycaemia is a risk factor, and we need to continue all the efforts to reduce the risk of people developing diabetes, and hopefully reduce their risk of getting COVID, and severe outcomes from COVID.

Thank you very much for listening.

Here is the original post:
Diabetes Prevention in the COVID-19 Era - Medscape

When a famous rapper loses a leg to diabetes there is little to do but offer sympathy and give to the GoFundMe page set up for his treatment and follow up care. Today, as we listened to The Beet's Creative AdvisorJermaine Dupri being interviewedfor HipHopDX,we learned that Andre "Doctor Dre" Brown, most known for having starred in the MTV show Yo! MTV Raps with Ed Lover inthe late 80s to mid-90s, had his leg amputated earlier this summer, the result of complications from diabetes.

Doctor Dre not to be confused with the west coast producer, co-founder of Beats by Dre headphones and former member of NWA Dr. Dreis credited as having "exposedHip Hop to a whole new audience while introducing the genre into living rooms across the United States," according to a story in HipHopDX.

Doctor Dre made his mark in radio, television, movies and had worked as a DJ, composer, talent scout, program host, actor, critic, and author. But he is best known as co-host with Ed Lover of Yo! MTV Raps,"theTV show that did more than any other to make rap music and hip-hop culture global phenomena," according to ABCnews.com

From 1989 to 1995, Doctor Dr andEd Loverwere the co-hosts of Yo! MTV Raps. Dr had already teamed up with Lover in the early 1990s to co-host a morning radio show as part of the re-launch of Hot 97in New York City.

The pair starred in the1993filmWho's the Man?, directed byYo! MTV Rapsco-creator and co-directorTed Demme. Dr and Ed Lover also recorded an album in 1994 titledBack up off Me! Dr also served as a DJ for theBeastie Boys.Hehad his own clothing line calledBigga Stuffin the early 1990s. In 2003 Dr and Ed Lover participated in theComedy Central Roastof theirWho's the Man?co-star, comedianDenis Leary.

Dre also guest-starred onThe Fresh Prince of Bel-Airin the episode "Ill Will" as a figment ofWill Smith's nightmare of bad doctors. He then appeared on an episode ofThe People's Courtwith JudgeMarilyn Milianas a witness for a talent director suing former colleagues of his.

HisGoFundMe page reads:

Friends,

All of us who lived through the Nineties and care about music know and love Andre "Doctor Dre" Brown. He has made his mark on radio and television, in the movies and in print, working successively as a recording artist (as a founding member of Def Jam's Original Concept), hip-hop DJ (he was the Beastie Boys's DJ during the Raising Hell Tour in 1986) , composer, talent scout, on-air personality, actor, author, and critic. He's undoubtedly best-known as the co-host with Ed Lover of "Yo! MTV Raps" (1989-1995), the tv show that did more than any other to make rap music and hip-hop culture global phenomena. After "Yo!", Dre and Ed duo funneled their chemistry into major market radio. They held down the morning show on New York's Hot 97 (1993-1998), then on L.A.'s The Beat (2000-2001), and finally on New York's Power 105 (2003-2006).

Fans of Doctor Dre (whose real name is Andre Brown) is a big personality and well-loved in the Hip Hop community. He has suffered from type 2 diabetes for years, and when diabetes gets advanced it can cut off circulation to the capillaries that supply oxygen to the toes, eyes and other areas of the body that when damaged can not heal properly. One way to prevent and even reverse symptoms of type 2 diabetes is a plant-based diet, which lowers inflammation and helps keep blood sugar under control.

Even as recently as 10 months ago, he was trying to turn things around.Brownexplained that hes not completely blind, and has undergone retina reattachment surgery. The resulting scar tissue causes his vision to fluctuate. As a result, hes currently more focused on higher factors.

Im learning its better what you put in your mouth to help treat the situation, Brown said. But Ive learned to say I believe in a higher spirit, and he speaks to me all the time.

Now he is alsomostly blinddue to complications stemming from his condition. Back in 2016 when he was awaiting weight loss surgery to help him treat his condition, Doctor Dretold The New York Times:

My stubbornness put me where Im at. Now my energy is going to change that. We got young people, grown people, old, all having this. We can prevent this. We can cure this. I have an idea of how to do it.

Diet and lifestyle changes can help reverse and reduce symptoms of diabetes, as Eric Adams, Brooklyn Borough President, found out when he started to experience declining vision. He was overweight and in poor health until he switched to a vegan diet, lost 35 pounds and got healthier. He's recently written a book about his transformation, due out this fall, called Healthy at Last: A Plant-Based Approach to Preventing and Reversing Diabetes and Other Chronic Illnesses.

A new study just published this month found that a plant-based diet controls blood sugar and helps your body naturally metabolize carbs and fat, to help avoid diabetes. And another review study of diets showed that you reap the benefits of eating more plants when avoiding diabetes is the goal.Thisstudyfound that the more plants, the better.

Our thoughts and prayers are with Doctor Dre and his family. To contribute to his GoFundMe Page click here.

Read the rest here:
Rapper Loses Leg to Diabetes and Friends Rally to Support - The Beet

The keto diet is a high-fat, low-carb eating plan. On the keto diet, you're usually eating around 80% fewer carbs than what national guidelines typically advise.

Research shows that the diet's approach to limiting carbs may help people with type 2 diabetes manage their condition. Here's a closer look at how the keto diet works and what people with type 2 diabetes should know before giving it a try.

Type 2 diabetes means that the body doesn't respond to insulin like it should. The hormone insulin helps your body utilize blood sugar, or glucose, for energy. For patients with insulin resistance or type 2 diabetes, insulin is still produced but it may be in insufficient amounts or the body doesn't use insulin properly. This causes blood sugar levels to increase. If not treated, chronically high blood sugar levels can lead to heart disease, kidney damage, nerve damage, eye damage, sleep apnea, and more.

To keep diabetes under control, you want to keep your blood sugar levels as close to normal as possible, says Osama Hamdy, MD, an associate professor at Harvard Medical School and senior staff physician at the Joslin Diabetes Center in Boston.

"Glucose in blood comes predominantly from carbohydrates, so eating more carbohydrates increases blood glucose and reducing carbohydrates reduces blood glucose."

That's why the carb-cutting keto diet might be helpful for people with type 2 diabetes.

As far as research goes, Hamdy says that there haven't been long-term studies on whether the keto diet can actually prevent type 2 diabetes. But there is some research on the diet's effect on those who already have the condition. Although many of the studies have been done on small groups of participants, the results seem promising.

For example, in a small study published in Nutrition & Metabolism in 2005, overweight adults (mostly men) with type 2 diabetes followed a keto diet, where they aimed to keep carbs at or below 20 grams per day for four months. At the end of the study, participants were able to reduce or completely stop taking their diabetes medications. Moreover, they also experienced a 16% reduction in their A1c levels, which is the average amount of glucose in the blood in the last 3-months.

"Reduction in A1C indicates improvement in diabetes control, which in turn reduces the chances of diabetes complications on eyes, kidneys, nerves and the cardiovascular system," says Handy

Another study, published in Nutrition & Metabolism in 2008, looked at how overweight adults with type 2 diabetes fared following a keto diet versus a low-cal-low-glycemic diet for 6 months. (Low-glycemic refers to foods with a low glycemic index that are less likely to spike blood sugar levels.) The study found that while both groups did well, results for the keto group were better in certain areas, including the reduction of A1C levels. Plus: 95% of those on a keto diet were able to cut or lower their medication use, compared to 62% in the low-calorie-low glycemic group.

A study published in Nutrition & Diabetes in 2017, found that after a year, overweight adults with prediabetes or type 2 diabetes who adhered to a keto diet saw a reduction in their A1C levels and also reduced medication more than those who ate a moderate carb and low-calorie/low-fat diet. Plus, the keto dieters had an average weight loss of 8.3% compared with 3.8% in the low-calorie group. Weight loss can also help keep blood sugar levels in check.

Generally, if you're following the keto diet, you're eating less than 50 grams of carbs per day. Some versions of the diet call for an even smaller amount around 20 or 30 grams of carbs per day, says Hamdy. For comparison, the Dietary Guidelines for Americans puts the recommended daily carb intake at somewhere between 225 and 325 grams per day.

The Academy of Nutrition and Dietetics describes how the diet works like this: With carbs pretty much out of the picture, the body needs another way to fuel itself. So, it uses fat, which is broken down into ketones and these ketones become the body's primary energy source. Once that happens, your body enters ketosis. "Ketosis indicates that the body switched its fuel source to stored fat," says Hamdy.

The good news for diabetes? Since blood glucose levels are lower when carb intake is less and ketones don't increase blood glucose levels, diabetes is better kept in control, says Hamdy.

The fact that ketones don't increase blood glucose levels, combined with eating a low-carb diet that also helps keep glucose levels lower, could help explain why research points to the positive effect of the keto diet on type 2 diabetes.

But if you do have type 2 diabetes, your doctor should be monitoring you while you're on the keto diet. That's in part because ketone levels that are too high can be dangerous changing the degree of blood acidity, and possibly leading to conditions like cardiac arrhythmia, says Hamdy.

Hamdy says the keto diet isn't harmful for the majority of type 2 diabetes patients. However he also says that "replacing carbohydrates in the diet with any type of fat, like meat or bacon, can result in a significant increase in bad cholesterol." That's why substituting the carbs you're cutting from your diet with healthier proteins and fats, like olive oil, avocados, and nuts, is better than gorging on bacon, steak, and barbecue.

The keto diet also comes with a series of side effects sometimes referred to as the keto flu like headache, constipation, and bad breath. Other risks include eventually developing conditions like kidney stones and vitamin deficiencies, according to the Academy of Nutrition and Dietetics. The academy also advises against the diet for people with certain conditions, like eating disorders or those with pancreatic disease, because of the detrimental effect on the pancreas from the high intake of fat.

For his part, Hamdy recommends a modified version of the keto diet for overweight or obese type 2 diabetes patients one where carb intake is specific to the individual, unhealthy fats like saturated fat in red meat is limited, and protein intake, particularly plant-based protein like beans, is higher.

"What most people don't know is that with the keto diet, you are not only losing fat but also losing muscle mass, which is dangerous. The capacity to regain muscle mass again is limited," Hamdy says. "So, replacing carbohydrates with protein instead of fat is a better idea, especially in conjunction with strength training."

In his clinic, Hamdy says that type 2 diabetes patients have had success with weight loss and diabetes control by cutting carbs to 40% of their diet, stick with low-glycemic index carbohydrates, and increased protein.

"For example, Joslin's Why WAIT program, which implements this structured nutrition plan along with exercise and behavioral changes, helped participating patients with diabetes to maintain a 6.9% weight loss for 10 years. They also cut their medications significantly, and many had partial or complete remission from type 2 diabetes," he says.

If you're suffering from prediabetes or type 2 diabetes, the keto diet may be worth considering. Consult with a doctor before trying any type of extreme diet.

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Why the ketogenic diet can help with diabetes and how to tell if it's right for you - Insider - INSIDER

Rationale, aims and objectives:Clinical inertia, defined as a delay in treatment intensification, is prevalent in people with diabetes. Treatment intensification rates are as low as 37.1% in people with haemoglobin A1c (HbA1c) values >7%. Intensification by addition of medication therapy may take 1.6 to more than 7 years. Clinical inertia increases the risk of cardiovascular events. The primary objective was to evaluate rates of clinical inertia in people whose diabetes is managed by both pharmacists and primary care providers (PCPs). Secondary objectives included characterizing types of treatment intensification, HbA1c reduction, and time between treatment intensifications.

Method:Retrospective chart review of persons with diabetes managed by pharmacists at an academic, safety-net institution. Eligible subjects were referred to a pharmacist-managed cardiovascular risk reduction clinic while continuing to see their PCP between October 1, 2016 and June 30, 2018. All progress notes were evaluated for treatment intensification, HbA1c value, and type of medication intensification.

Results:Three hundred sixty-three eligible patients were identified; baseline HbA1c 9.6% (7.9, 11.6) (median interquartile range [IQR]). One thousand one hundred ninety-two pharmacist and 1739 PCP visits were included in data analysis. Therapy was intensified at 60.5% (n = 721) pharmacist visits and 39.3% (n = 684) PCP visits (P < .001). The median (IQR) time between interventions was 49 (28, 92) days for pharmacists and 105 (38, 182) days for PCPs (P < .001). Pharmacists more frequently intensified treatment with glucagon-like peptide-1 agonists and sodium glucose cotransporter-2 inhibitors.

Conclusion:Pharmacist involvement in diabetes management may reduce the clinical inertia patients may otherwise experience in the primary care setting.

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Assessment of Clinical Inertia in People With Diabetes Within Primary Care - DocWire News