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Evan M. Benjamin; Case Study: Treating Hypertension in Patients With Diabetes. Clin Diabetes 1 July 2004; 22 (3): 137–138. https://doi.org/10.2337/diaclin.22.3.137

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L.N. is a 49-year-old white woman with a history of type 2 diabetes,obesity, hypertension, and migraine headaches. The patient was diagnosed with type 2 diabetes 9 years ago when she presented with mild polyuria and polydipsia. L.N. is 5′4″ and has always been on the large side,with her weight fluctuating between 165 and 185 lb.

Initial treatment for her diabetes consisted of an oral sulfonylurea with the rapid addition of metformin. Her diabetes has been under fair control with a most recent hemoglobin A 1c of 7.4%.

Hypertension was diagnosed 5 years ago when blood pressure (BP) measured in the office was noted to be consistently elevated in the range of 160/90 mmHg on three occasions. L.N. was initially treated with lisinopril, starting at 10 mg daily and increasing to 20 mg daily, yet her BP control has fluctuated.

One year ago, microalbuminuria was detected on an annual urine screen, with 1,943 mg/dl of microalbumin identified on a spot urine sample. L.N. comes into the office today for her usual follow-up visit for diabetes. Physical examination reveals an obese woman with a BP of 154/86 mmHg and a pulse of 78 bpm.

What are the effects of controlling BP in people with diabetes?

What is the target BP for patients with diabetes and hypertension?

Which antihypertensive agents are recommended for patients with diabetes?

Diabetes mellitus is a major risk factor for cardiovascular disease (CVD). Approximately two-thirds of people with diabetes die from complications of CVD. Nearly half of middle-aged people with diabetes have evidence of coronary artery disease (CAD), compared with only one-fourth of people without diabetes in similar populations.

Patients with diabetes are prone to a number of cardiovascular risk factors beyond hyperglycemia. These risk factors, including hypertension,dyslipidemia, and a sedentary lifestyle, are particularly prevalent among patients with diabetes. To reduce the mortality and morbidity from CVD among patients with diabetes, aggressive treatment of glycemic control as well as other cardiovascular risk factors must be initiated.

Studies that have compared antihypertensive treatment in patients with diabetes versus placebo have shown reduced cardiovascular events. The United Kingdom Prospective Diabetes Study (UKPDS), which followed patients with diabetes for an average of 8.5 years, found that patients with tight BP control (< 150/< 85 mmHg) versus less tight control (< 180/< 105 mmHg) had lower rates of myocardial infarction (MI), stroke, and peripheral vascular events. In the UKPDS, each 10-mmHg decrease in mean systolic BP was associated with a 12% reduction in risk for any complication related to diabetes, a 15% reduction for death related to diabetes, and an 11% reduction for MI. Another trial followed patients for 2 years and compared calcium-channel blockers and angiotensin-converting enzyme (ACE) inhibitors,with or without hydrochlorothiazide against placebo and found a significant reduction in acute MI, congestive heart failure, and sudden cardiac death in the intervention group compared to placebo.

The Hypertension Optimal Treatment (HOT) trial has shown that patients assigned to lower BP targets have improved outcomes. In the HOT trial,patients who achieved a diastolic BP of < 80 mmHg benefited the most in terms of reduction of cardiovascular events. Other epidemiological studies have shown that BPs > 120/70 mmHg are associated with increased cardiovascular morbidity and mortality in people with diabetes. The American Diabetes Association has recommended a target BP goal of < 130/80 mmHg. Studies have shown that there is no lower threshold value for BP and that the risk of morbidity and mortality will continue to decrease well into the normal range.

Many classes of drugs have been used in numerous trials to treat patients with hypertension. All classes of drugs have been shown to be superior to placebo in terms of reducing morbidity and mortality. Often, numerous agents(three or more) are needed to achieve specific target levels of BP. Use of almost any drug therapy to reduce hypertension in patients with diabetes has been shown to be effective in decreasing cardiovascular risk. Keeping in mind that numerous agents are often required to achieve the target level of BP control, recommending specific agents becomes a not-so-simple task. The literature continues to evolve, and individual patient conditions and preferences also must come into play.

While lowering BP by any means will help to reduce cardiovascular morbidity, there is evidence that may help guide the selection of an antihypertensive regimen. The UKPDS showed no significant differences in outcomes for treatment for hypertension using an ACE inhibitor or aβ-blocker. In addition, both ACE inhibitors and angiotensin II receptor blockers (ARBs) have been shown to slow the development and progression of diabetic nephropathy. In the Heart Outcomes Prevention Evaluation (HOPE)trial, ACE inhibitors were found to have a favorable effect in reducing cardiovascular morbidity and mortality, whereas recent trials have shown a renal protective benefit from both ACE inhibitors and ARBs. ACE inhibitors andβ-blockers seem to be better than dihydropyridine calcium-channel blockers to reduce MI and heart failure. However, trials using dihydropyridine calcium-channel blockers in combination with ACE inhibitors andβ-blockers do not appear to show any increased morbidity or mortality in CVD, as has been implicated in the past for dihydropyridine calcium-channel blockers alone. Recently, the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) in high-risk hypertensive patients,including those with diabetes, demonstrated that chlorthalidone, a thiazide-type diuretic, was superior to an ACE inhibitor, lisinopril, in preventing one or more forms of CVD.

L.N. is a typical patient with obesity, diabetes, and hypertension. Her BP control can be improved. To achieve the target BP goal of < 130/80 mmHg, it may be necessary to maximize the dose of the ACE inhibitor and to add a second and perhaps even a third agent.

Diuretics have been shown to have synergistic effects with ACE inhibitors,and one could be added. Because L.N. has migraine headaches as well as diabetic nephropathy, it may be necessary to individualize her treatment. Adding a β-blocker to the ACE inhibitor will certainly help lower her BP and is associated with good evidence to reduce cardiovascular morbidity. Theβ-blocker may also help to reduce the burden caused by her migraine headaches. Because of the presence of microalbuminuria, the combination of ARBs and ACE inhibitors could also be considered to help reduce BP as well as retard the progression of diabetic nephropathy. Overall, more aggressive treatment to control L.N.'s hypertension will be necessary. Information obtained from recent trials and emerging new pharmacological agents now make it easier to achieve BP control targets.

Hypertension is a risk factor for cardiovascular complications of diabetes.

Clinical trials demonstrate that drug therapy versus placebo will reduce cardiovascular events when treating patients with hypertension and diabetes.

A target BP goal of < 130/80 mmHg is recommended.

Pharmacological therapy needs to be individualized to fit patients'needs.

ACE inhibitors, ARBs, diuretics, and β-blockers have all been documented to be effective pharmacological treatment.

Combinations of drugs are often necessary to achieve target levels of BP control.

ACE inhibitors and ARBs are agents best suited to retard progression of nephropathy.

Evan M. Benjamin, MD, FACP, is an assistant professor of medicine and Vice President of Healthcare Quality at Baystate Medical Center in Springfield, Mass.

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Case Studies: BP Evaluation and Treatment in Patients with Prediabetes or Diabetes

—the new acc/aha blood pressure guidelines call for a more aggressive diagnostic and treatment approach in most situations..

By Kevin O. Hwang, MD, MPH, Associate Professor, McGovern Medical School, Houston, TX

The following case studies illustrate how the new ACC/AHA guideline specifies a shift in the definition of BP categories and treatment targets.

A 59-year-old man with type 2 diabetes presents with concerns about high blood pressure (BP). At a recent visit to his dentist he was told his BP was high. He was reclining in the dentist’s chair when his BP was taken, but he doesn’t remember the exact reading. He has no symptoms. He has never taken medications for high BP. He takes metformin for type 2 diabetes.

His BP is measured once at 146/95 mm Hg in the left arm while sitting. Physical exam is unremarkable except for obesity. EKG is unremarkable.

BP Measurement

Controlling BP in patients with diabetes reduces the risk of cardiovascular events, but the available data are not sufficient to classify this patient with respect to BP status. The reading taken while reclining in the dentist’s chair was likely inaccurate. A single reading in the medical clinic, even with correct technique, is not adequate for clinical decision-making because individual BP measurements vary in unpredictable or random ways.

The accuracy of BP measurement is affected by patient preparation and positioning, technique, and timing. Before the first reading, the patient should avoid smoking, caffeine, and exercise for at least 30 minutes and should sit quietly in a chair for at least 5 minutes with back supported and feet flat on the floor. An appropriately sized cuff should be placed on the bare upper arm and with the arm supported at heart level. For the first encounter, BP should be recorded in both arms. The arm with the higher reading should be used for subsequent measurements.

It is recommended that one use an average of 2 to 3 readings, separated by 1 to 2 minutes, obtained on 2 to 3 separate visits. Some of those readings should be performed outside of the clinical setting, either with home BP self-monitoring or 24-hour ambulatory BP monitoring, especially when confirming the diagnosis of sustained hypertension. Note that a clinic BP of 140/90 corresponds to home BP values of 135/85. Multiple BP readings in the clinic and at home allow for classification into one of the following categories.

The BP is measured in the office with the correct technique and timing referenced above. The patient is educated on how on to measure BP at home with a validated monitor. He should take at least 2 readings 1 minute apart in the morning and in the evening before supper (4 readings per day). The optimal schedule is to measure BP every day for a week before the next clinic visit, which is set for a month from now. Obtaining multiple clinic and home BP readings on multiple days will support a well-informed assessment of the patient’s BP status and subsequent treatment decisions.

A 62 year old African-American woman with prediabetes presents for her annual physical. She has no complaints. The average of 2 BP readings in her right arm is BP 143/88. Her physical exam is unremarkable except for obesity. She has no history of myocardial infarction, stroke, kidney disease, or heart failure. After the visit, she measures her BP at home and returns 1 month later. The average BP from multiple clinic and home readings is 138/86.

Her total cholesterol is 260 mg/dL, HDL 42 mg/dL, and LDL 165 mg/dL. She does not smoke.

Stage 1 Hypertension

Under the 2017 ACC/AHA guideline, she has stage 1 hypertension (HTN). This guideline uses a uniform BP definition for HTN without regard to patient age or comorbid illnesses, such as diabetes or chronic kidney disease.

In patients with stage 1 HTN and no known atherosclerotic cardiovascular disease (ASCVD) , the new guideline recommends treating with BP-lowering medications if the 10-year risk for ASCVD risk is 10% or greater. With input such as her age, gender, race, lipid profile, and other risk factors, the ACC/AHA Pooled Cohort Equations tool estimates her 10-year risk to be approximately 10.5%.

With stage 1 HTN and 10-year ASCVD risk of 10% or higher, she would benefit from a BP-lowering medication. Thiazide diuretics, angiotensin converting enzyme (ACE) inhibitors, angiotensin II receptor blockers (ARBs), and calcium channel blockers are first-line agents for HTN because they reduce the risk of clinical events. In African-Americans, thiazide diuretics and calcium channel blockers are more effective for lowering BP and preventing cardiovascular events compared to ACE inhibitors or ARBs.

Patient-specific factors, such as age, comorbidities, concurrent medications, drug adherence, and out-of-pocket costs should be considered. Shared decision making should drive the ultimate choice of antihypertensive medication(s).

Nonpharmacologic strategies for prediabetes and HTN include dietary changes, physical activity, and weight loss. If clinically appropriate, she should also avoid agents which could elevate BP, such as NSAIDs, oral steroids, stimulants, and decongestants.

A goal BP of 130/80 is recommended. After starting the new BP medication, she should monitor BP at home and return to the clinic in 1 month. If the BP goal is not met at that time despite adherence to treatment, consideration should be given to intensifying treatment by increasing the dose of the first medication or adding a second agent.

A 63 year old man with type 2 diabetes has an average BP of 151/92 over the span of several weeks of measuring at home and in the clinic. He also has albuminuria.

Stage 2 Hypertension:

The BP treatment goal patients with diabetes and HTN is less than 130/80. While some patients can be effectively treated with a single agent, serious consideration should be given to starting with 2 drugs of different classes, especially if BP is more than 20/10 mm Hg above their BP target. Giving both medications as a fixed-dose combination may improve adherence.

In this man with diabetes and HTN, any of the first-line classes of antihypertensive agents (diuretics, ACE inhibitors, ARBs, and CCBs) would be reasonable choices. Given the presence of albuminuria, an ACE inhibitor or ARB would be beneficial for slowing progression of kidney disease. However, an ACE inhibitor and ARB should not be used simultaneously due to an increase in cardiovascular and renal risk observed in clinical trials.

He is started on a fixed-dose combination of an ACE-inhibitor and thiazide diuretic. He purchases a validated BP monitor which can transmit BP readings to his provider’s electronic health records system. Direct transmission of BP data to the provider has been shown to help patients achieve greater reductions in BP compared to self-monitoring without transmission of data. One month follow-up is recommended to determine if the treatment goal has been met.

Published: April 30, 2018

• 2. Final Recommendation Statement: High Blood Pressure in Adults: Screening. U.S. Preventive Services Task Force. September 2017.

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• Published: 25 February 2021

The interaction on hypertension between family history and diabetes and other risk factors

• An-le Li 1 ,
• Qian Peng 1 ,
• Yue-qin Shao 1 ,
• Xiang Fang 1 &
• Yi-ying Zhang 1  

Scientific Reports volume  11 , Article number:  4716 ( 2021 ) Cite this article

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To explore the individual effect and interaction of diabetes and family history and other risk factors on hypertension in Han in Shanghai China. The method of case–control study with l:l matched pairs was used, 342 cases of hypertension and 342 controls were selected and investigate their exposed factors with face-to-face. The method of epidemiology research was used to explore the individual effect and interaction of diabetes and family history and other risk factors on hypertension. The individual effect of family history (OR = 4.103, 95%CI 2.660–6.330), diabetes (OR = 4.219, 95%CI 2.926–6.083), personal taste (OR = 1.256, 95%CI 1.091–1.593), drinking behavior (OR = 1.391, 95%CI 1.010–1.914) and smoking behavior (OR = 1.057, 95%CI 1.00–1.117) were significant (p < 0.05). But individual effect of sex, education, occupation, work/life pressure, environmental noise, sleeping time and sports habit were not significant (p > 0.05). The OR of interaction between FH and DM to hypertension was 16.537 (95%CI 10.070–21.157), between FH and drinking behavior was 4.0 (95%CI 2.461–6.502), FH and sport habit was 7.668 (95%CI 3.598–16.344), FH and personal taste was 6.521 (95%CI 3.858–11.024), FH and smoking behavior was 5.526 (95%CI 3.404–8.972), FH and work/life pressure was 4.087 (95%CI 2.144–7.788). The SI of FH and DM was 2.27, RERI was 8.68, AP was 52.48% and PAP was 55.86%. FH and DM, personal taste, smoking behavior had positive interaction on hypertension, but FH and sport habits, drinking behavior, work/life pressure had reverse interaction on hypertension. FH and diabetes were very important risk factors with significant effect for hypertension. FH and diabetes, personal taste, smoking behavior had positive interaction on hypertension, but FH and sport habits, drinking behavior, work/life pressure had reverse interaction on hypertension.

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Introduction.

Hypertension is a multifactorial disease caused by genetic and acquired environmental factors, the role of gene and gene, as well as between gene and acquired environmental factors, including among acquired risk factors, leads to increased risk of hypertension and disease among different populations. Unhealthy lifestyle including obesity and lack of exercise can significantly increase hypertension incidence 1 , 2 , 3 , 4 . The study result from familial aggregation showed that the prevalence rate of brothers and sisters in offspring was from 20 to 66% in positive population of parents, the estimated possibility of hereditary was over 50% in a plurality of twin studies 5 , 6 . It showed that more than half of the blood pressure changed could be attributed to the accumulation of genetic effects.

In fact, the individual effect of single factor on the result could not truly reflect the actual effect of factors, because there was interaction between risk factors, which might weaken the role of single factor, or enhance the role of single factor. Therefore, the interaction between factors may be more able to reflect the real relationship between factors and results. There were many studies on the influencing factors of hypertension at home and abroad, but there were few reports on the interaction between the influencing factors of hypertension.

There were two models to analyze biological interaction: addition and multiplication. To explore the risk factors and etiological factors of hypertension from the perspective of environment and genetics, and to carry out effective etiological prevention are the fundamental countermeasures and measures to reduce the incidence of hypertension. It might be of public health significance to explore the interaction with additive model. Therefore, this study selected additive model to analyze the interaction between family history of hypertension and diabetes and other exposed factors on the incidence of hypertension.

Source of cases and controls

All cases were randomly selected from hypertension registry and follow-up management system in Jiading district in Shanghai, and all controls were randomly selected from the community population. The cases of this study were patients with hypertension who have been definitely diagnosed in the hospital and had been using antihypertensive drugs (SBP ≥ 140 mmHg or DBP ≥ 90 mmHg without using antihypertensive drugs). The controls of this study was non hypertensive patients, their blood pressure were SBP < 140 mmHg and DBP < 100 mmHg and unused antihypertensive drugs. According to l:l matched pairs design, all controls had no hypertension, and controls were required the same sex, same race, living in the same community as cases, and the difference of age was not more than 5 years old and at the same age group. Every case or control gave informed consent to participate in the study which was approved by the local ethics committee (JD-2016-KY-18). They were able to correctly respond to the investigators for the health information of themselves and their nuclear family members.

Investigation method and content

Investigation was conducted by trained public health investigators, using a unified questionnaire. Using direct survey method, the contents of the questionnaire mainly include: age, sex, age of onset, diagnosis time, hospital name, diabetes history and so on. The criteria for judging whether all the respondents had essential hypertension and diabetes (all relatives of cases and controls): whether they had been diagnosed with essential hypertension or diabetes in the hospital before this investigation. If they had been diagnosed with essential hypertension in the hospital, it is “Yes”; if they had not been diagnosed, it is “No”.

Statistical analysis

Statistical analyses were performed using the statistical software package (IBM SPSS statistics version 21). When  P  values < 0.05, the difference was considered statistically significant. Mean and standard deviation (SD) were used to compute for quantitative variables (age and so on), and comparisons between groups were performed by t-test. Number (n) and percentage (%)) were computed for the categorical data, comparisons between groups were performed by the chi-square ( χ 2 ) test. Multivariate logistic regression analyses were conducted for investigated risk factors, odds ratios (OR) and 95% confidence intervals (CI) were calculated. In multivariate analysis, OR were adjusted by sex. The additive model was used by cross analysis to calculate the additive interaction effect 1 . The calculated indicators were SI (synergistic effect index), RERI (relative excess risk due to interaction), AP (attributable proportion due to interaction) and PAP (the percentage of the interaction between the pure factors).

All methods were carried out in accordance with relevant guidelines and regulations. The investigated object, content and methods of study were implemented according to the design scheme and technical route.

Ethics approval and consent to participate

Ethical approval was granted by Jiading district center for disease control and prevention research ethics committee. All subjects gave informed consent to participate in the study, they would like to participate in investigation and answer all the related questions in the questionnaire.

Individual effect analysis

Among 684 investigated participants (342 hypertension cases and 342 controls) aged 28–87 years old in this study, male was 50.73%, female was 49.27%. 76.17% participants had family history of hypertension, 23.83% had not. Between case group and control group, the statistical test results showed that the difference of sex, education level, work and life pressure, living environmental noise, person's taste, sleeping time, sports habit, drinking behavior and smoking behavior was no significant (p > 0.05). But the difference of family history (FH) and occupation between case group and control group was significant (p < 0.05). The difference of mean age between case group and control group was no significant (t = 0.894, p = 0.372).

The result of logistic regression analysis showed that individual effect of family history of hypertension, diabetes history, personal taste, drinking behavior and smoking behavior were significant (p < 0.05). But individual effect of sex, education, occupation, work/life pressure, environmental noise, sleeping time and sports habit were not significant (p > 0.05). See Table 1 .

The OR result showed that family history of hypertension, diabetes history, drinking behavior and smoking behavior were important risk factors to hypertension. The OR between family history and hypertension was 4.103 (95%CI 2.660–6.330); the OR between diabetes history and hypertension was 4.219 (95%CI 2.926–6.083); the OR between drinking behavior and hypertension was 1.391 (95%CI 1.010–1.914); the OR between smoking behavior and hypertension was 1.057 (95%CI 1.000–1.117). The OR between personal taste and hypertension was 1.256 (95%CI 1.091–1.593). See Table 1 .

Interaction analysis

Interaction of family history and diabetes.

The effect of risk factors on the occurrence of hypertension was often not independent; they often interacted with each other and promoted the occurrence of hypertension through interaction. In order to explore the interaction between family history and diabetes, the methods of interactive effects analysis was used. Table 2 shows the result of interactive effects between family history and diabetes. The OR of interaction between family history and diabetes to hypertension was 16.537, the OR of family history and diabetes to hypertension were respectively 4.505 and 4.354. OR (FH+DM)  > OR FH  + OR DM . It was showed that family history and diabetes have positive interaction with hypertension.

According the result of Table 3 , additive model was used to calculate the additive interaction effect: the synergistic effect index (SI) of family history and diabetes to hypertension was 2.27; relative excess risk due to interaction (RERI) was 8.68; Attributable proportion due to interaction (AP) was 52.48%; and the percentage of the interaction between the pure factors (PAP) was 55.86%. The result of PAP indicated that 55.86% of hypertension was attributable to the interaction of them, when exposed to both family history and diabetes risk factors.

Interaction of FH and other risk factors

In order to better observe the interaction between family history (FH) and other risk factors (drinking behavior, sport habits, personal taste, smoking behavior and work/life pressure), we changed behavior from three categories (no, occasionally and regular) to two categories (yes or no), the combination of occasionally drinking and regular drinking was yes (have drink behavior); occasionally sport and regular sport was yes (have sport habits). The same as, balance taste and light taste was no salty; occasionally and regular smoking behavior was yes (smoking), little and more work/life pressure was have pressure.

The result showed OR of interaction between FH and drinking behavior to hypertension was 4.0, the OR of family history and smoking behavior to hypertension were respectively 4.942 and 0.741. OR (FH+Dr)  < OR FH  + OR Dr . It was showed that FH and drinking behavior have reverse interaction on hypertension. See Table 3 .

The result showed OR of interaction between FH and sport habits to hypertension was 7.668, the OR of family history and sport habits to hypertension were respectively 8.571 and 1.773. OR (FH+S)  < OR FH  + OR S . It was showed that FH and sport habits have reverse interaction on hypertension. See Table 4 .

The result showed OR of interaction between FH and taste preference to hypertension was 6.521, the OR of family history and taste preference to hypertension were respectively 4.840 and 1.386. OR (FH+T)  > OR FH  + OR T . It was showed that FH and taste preference (salty) have positive interaction on hypertension. See Table 5 .

The result showed OR of interaction between FH and smoking behavior to hypertension was 5.526, the OR of family history and smoking behavior to hypertension were respectively 4.359 and 0.871. OR (FH+Sm)  > OR FH  + OR Sm . It was showed that FH and smoking behavior have positive interaction on hypertension. See Table 6 .

The result showed OR of interaction between FH and work/life pressure to hypertension was 4.087, the OR of family history and work/life pressure to hypertension were respectively 5.217 and 2.229. OR (FH+P)  < OR FH  + OR P . It was showed that FH and work/life pressure have reverse interaction on hypertension. See Table 7 .

The factors influencing the occurrence of hypertension include congenital factors and natural factors, congenital factors refer to hereditary factors such as genes or family history, acquired factors mainly include bad living habits, overweight / obesity, etc. Body mass index was a comprehensive indicator of the outcome of acquired lifestyle, and closely related to the occurrence of hypertension 7 , 8 , 9 , 10 , 11 , 12 . Family history of hypertension was an important marker of genetic factors, it was often used as an alternative indicator to study the relationship between genetic factors and diseases 13 , 14 , 15 , 16 . Previous studies had not been considered the interaction between genetic and environmental factors. More attention needed to pay to the related research, to evaluate the relationship between polymorphic gene and exposed factors. Hypertension and diabetes usually occurred successively, due to the hardening of blood vessels diabetes could induce hypertension 17 , 18 , 19 .

There were many risk factors for hypertension, such as smoking, drinking, mental tension, lack of exercise, family genetics and so on 20 , 21 . In this study, the result showed that effect of family history of hypertension, diabetes history, personal taste, drinking behavior and smoking behavior were significant (p < 0.05). But effect of education, occupation, work/ life pressure, environmental noise, sleeping time and sports habit were not significant (p > 0.05). The OR result showed that family history of hypertension, diabetes history and drinking behavior were important risk factors to hypertension. The OR of family history of hypertension was 4.103, diabetes history was 4.219, drinking behavior was 1.391, smoking behavior was 1.057. This result showed that family history of hypertension, diabetes history, drinking behavior and smoking behavior were important factors of hypertension, especially family history and diabetes.

Hypertension was one of the common complications of diabetes; the incidence of hypertension in domestic diabetes patients with hypertension was 20–30% 22 , 23 . The OR of interaction between family history and diabetes to hypertension was 16.537. It was showed that FH and DM have positive interaction with hypertension. The percentage of the interaction between the pure factors (PAP) was 55.86%, it indicated that 55.86% of hypertension was attributable to the interaction of them, when exposed to both family history and diabetes risk factors. Because the disorder of glucose metabolism could accelerate the hardening of renal artery and systemic arteriole, increase the peripheral resistance and blood pressure, hyperglycemia can increase blood volume, overload the kidneys, retention of water and sodium, and eventually raise blood pressure. The increase of blood pressure was related to cardiac output and peripheral resistance. The increase of cardiac output without peripheral change could lead to the rise of blood pressure; the increase of peripheral resistance without the change of cardiac output or blood volume could also lead to the rise of blood pressure, and both changes of diabetic patients led to the rapid rise of blood pressure and serious complications.

Alcohol was one of the risk factors of hypertension 24 , 25 , 26 , 27 . Long term small amount of alcohol could increase blood pressure; small amount of alcohol could increase blood pressure, heart rate and heart load of patients with hypertension. In this study, the result of the logistic regression analysis showed that drinking behavior were risk factor to hypertension, the OR of drinking behavior was 1.391. Family history of hypertension and drinking behavior had reverse interaction on hypertension. This might be due to the interference of occasional drinking behavior, small amount of alcohol may have vascular protection. It needs further study.

High salt intake in salt sensitive individuals could lead to elevated blood pressure by affecting water and sodium metabolism, vascular function and sympathetic nervous system 28 , 29 . In this study, the result of the logistic regression analysis showed that personal taste were risk factor to hypertension, the OR of personal taste was 1.256. Family history of hypertension and drinking behavior had positive interaction on hypertension. The lower of 95%CI of OR < 1 (0.991), perhaps it was related to the fact that Shanghai residents generally like light food. It needs further study.

Smoking is a risk factor for cardiovascular disease, and smoking is associated with hypertension 30 , 31 . In this study, the result of the logistic regression analysis showed that smoking behavior were risk factor to hypertension, the OR of personal taste was 1.256. Family history and smoking behavior had positive interaction on hypertension.

Due to space limitation, only the interaction between family history and several common acquired factors were analyzed in this study. In fact, there were also interactions among acquired factors, and the interaction among multiple factors may be even more different. In short, the individual effect of single factor was strong did not mean that it must be very important role in the outcome of disease, the individual effect of single factor was weak did not mean that it must be very unimportant role in the outcome of disease. Pay attention to the interaction between factors, and expect more and better research results appear.

Family history and diabetes were very important risk factors with significant effect for hypertension. FH and DM, taste preference, smoking behavior had positive interaction with hypertension, but FH and sport habits, drinking behavior, work/life pressure had reverse interaction with hypertension.

Data availability

The questionnaire and database supporting the conclusions of this article are available, through contact with [email protected].

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Acknowledgements

Heartfelt thanks to all doctors, nurses and public health workers in 13 community health service centers in Jiading district in Shanghai for their hard work. Thank for some advice of the experts!

This study was funded by Jiading district health and family planning commission research project in Shanghai (No: 2016-KY-18).

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The original idea for the project was conceived by A.L.; Q.P., X.F. and Y.Z. participated in the collection of early data, quality control and gave a lot of administrative support. A.L. conceptualized the paper, analyzed data and wrote a first draft of the manuscript. All authors contributed to subsequent drafts and approved the final manuscript.

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Li, Al., Peng, Q., Shao, Yq. et al. The interaction on hypertension between family history and diabetes and other risk factors. Sci Rep 11 , 4716 (2021). https://doi.org/10.1038/s41598-021-83589-z

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Developing a toolkit for implementing evidence-based guidelines to manage hypertension and diabetes in Cambodia: a descriptive case study

Affiliations.

• 1 Programme in Health Services and Systems Research, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore. [email protected].
• 2 Programme in Health Services and Systems Research, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore.
• 3 Duke University Medical Center, Duke University, Durham, NC, United States of America.
• 4 KHANA Center for Population Health Research, Phnom Penh, Cambodia.
• 5 Department of Preventive Medicine, Ministry of Health, Phnom Penh, Cambodia.
• 6 Case Western Reserve University, Cleveland, OH, United States of America.
• 7 Center for Global Health Research, Touro University California, Vallejo, CA, United States of America.
• 8 Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
• PMID: 36443781
• PMCID: PMC9706829
• DOI: 10.1186/s12961-022-00912-4

Background: In Cambodia, economic development accompanied by health reforms has led to a rapidly ageing population and an increasing incidence and prevalence of noncommunicable diseases. National strategic plans recognize primary care health centres as the focal points of care for treating and managing chronic conditions, particularly hypertension and type 2 diabetes. However, health centres have limited experience in providing such services. This case study describes the process of developing a toolkit to facilitate the use of evidence-based guidelines to manage hypertension and type 2 diabetes at the health-centre level.

Methods: We developed and revised a preliminary toolkit based on the feedback received from key stakeholders. We gathered feedback through an iterative process of group and one-to-one consultations with representatives of the Ministry of Health, provincial health department, health centres and nongovernmental organizations between April 2019 and March 2021.

Results: A toolkit was developed and organized according to the core tasks required to treat and manage hypertension and type 2 diabetes patients. The main tools included patient identification and treatment cards, risk screening forms, a treatment flowchart, referral forms, and patient education material on risk factors and lifestyle recommendations on diet, exercise, and smoking cessation. The toolkit supplements existing guidelines by incorporating context-specific features, including drug availability and the types of medication and dosage guidelines recommended by the Ministry of Health. Referral forms can be extended to incorporate engagement with community health workers and patient education material adapted to the local context. All tools were translated into Khmer and can be modified as needed based on available resources and arrangements with other institutions.

Conclusions: Our study demonstrates how a toolkit can be developed through iterative engagement with relevant stakeholders individually and in groups to support the implementation of evidence-based guidelines. Such toolkits can help strengthen the function and capacity of the primary care system to provide care for noncommunicable diseases, serving as the first step towards developing a more comprehensive and sustainable health system in the context of population ageing and caring for patients with chronic diseases.

Keywords: Asia; Low- and middle-income countries; Noncommunicable diseases; Primary care; Toolkits.

© 2022. The Author(s).

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Interactive case study: Managing hypertension in diabetes – tricky cases

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Diabetes & Primary Care ’s series of interactive case studies is aimed at all healthcare professionals in primary and community care who would like to broaden their understanding of type 2 diabetes.

The three mini-case studies created for this issue of the journal cover various aspects relating to the management of complex cases of hypertension with type 2 diabetes.

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Working through the case studies will improve our knowledge and problem-solving skills in type 2 diabetes by encouraging us to make evidence-based decisions in the context of individual cases.

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By actively engaging with these case histories, I hope you will feel more confident and empowered to manage such presentations effectively in the future.

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What would be your concerns over intensifying Lily’s antihypertensive therapy?

Mar k, 47 years old, has persistent genital thrush. His blood glucose is 14.3 mmol/L, his BMI is 36.4 kg/m 2 and there is a family history of type 2 diabetes. BP of 194/126 mmHg is recorded.

How would you respond to Mark’s raised BP?

By working through these interactive cases, we will consider the following issues and more:

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• When to refer an individual with high blood pressure immediately to hospital.

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Adherence to the dietary approaches to stop hypertension diet reduces the risk of diabetes mellitus: a systematic review and dose-response meta-analysis

• Meta-Analysis
• Open access
• Published: 30 May 2024

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• Xiyan Quan 1 ,
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Despite several epidemiological studies reporting a significant association between adherence to the Dietary Approaches to Stop Hypertension (DASH) diet and the risk of diabetes mellitus, the results remain controversial. In this systematic review and meta-analysis, we aimed to summarize the existing evidence from published observational studies and evaluate the dose-response relationship between adherence to the DASH diet and diabetes mellitus risk.

We performed a systematic search for relevant articles published up to September 2023 using electronic databases of PubMed, Embase, Scopus, and China National Knowledge Infrastructure (CNKI). A random-effects model was applied to calculate the combined relative risks (RR) with 95% confidence intervals (CIs) for the highest compared to the lowest categories of DASH score in relation to diabetes mellitus risk. Heterogeneity among the included studies was assessed using the Cochran’s Q test and I-squared ( I 2 ) statistic. Literature search, study selection, data extraction, and quality assessment were performed by two independent reviewers.

Fifteen studies involving 557,475 participants and 57,064 diabetes mellitus cases were eligible for our analyses. Pooled analyses from included studies showed that high adherence to the DASH diet was significantly associated with a reduced risk of diabetes mellitus (RR: 0.82; 95% CI: 0.76–0.90, P  < 0.001). Moreover, the dose-response meta-analysis revealed a linear trend between adherence to the DASH diet and diabetes mellitus (RR:0.99; 95%CI: 0.97–1.02, P dose-response  = 0.546, P nonlinearity  = 0.701). Subgroup analyses further revealed a significant inverse association between adherence to the DASH diet and diabetes mellitus risk in case-control studies (RR: 0.65; 95%CI: 0.29–1.43, P  < 0.001), with a marginal inverse association in cohort studies (RR:0.83; 95%CI: 0.76–0.91, P  < 0.001). Additionally, we conducted analyses separately by comparison and found a significant inverse association between DASH diet and diabetes mellitus risk in T3 vs T1 comparison studies (RR = 0.74; 95%CI: 0.64–0.86, P  = 0.012).

The findings of this study demonstrate a protective association between adherence to the DASH diet and risk of diabetes mellitus. However, further prospective cohort studies and randomized controlled trials are needed to validate these findings.

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Introduction

Type 2 diabetes mellitus (DM) represents a major global health problem, which has become a severe, chronic, non-communicable disease after cardio-cerebrovascular diseases [ 1 ]. According to the statistics reported by the International Diabetes Federation (IDF) in 2021, approximately 537 million adults aged 20–79 years worldwide were affected by Type 2 DM, and this number is projected to 784 million by 2045 [ 2 ]. Additionally, the latest release global DM map reveals that China alone had 140.9 million people with DM in 2021, accounting for a quarter of the total global DM patients [ 3 ]. It is well-known that lifestyle risk factors, such as obesity, cigarette smoking, sedentary behavior, and unhealthy eating habits, play an important role in the prevention of DM [ 4 ]. Therefore, making appropriate food choices and adopting a healthy diet are essential strategies to prevent or delay the onset of DM [ 5 ].

In the past several decades, a growing body of evidence supports the potential role of diet in the prevention of DM [ 6 , 7 , 8 ]. Many epidemiological studies have specifically reported the links between the intakes of specific nutrients or foods, such as vegetables, fruit, and whole grains, and the risk of DM [ 9 , 10 ]. However, due to the complex interactions between dietary components and the potential synergistic effects of nutrients and foods consumed together, previous studies have revealed only a limited influence of diet on DM risk [ 11 ]. Given that, dietary pattern analysis, which takes into account the complexity of whole diet, has been widely applied in nutritional epidemiology [ 12 ]. Consistently, the results of dietary pattern analysis could be more easily translated into national dietary guidelines [ 7 ].

For instance, the Dietary Approaches to Stop Hypertension (DASH) diet, originally developed for the management of high blood pressure, has been proposed to potentially lower blood glucose levels [ 13 ]. The DASH diet, a well-established healthy dietary pattern, emphasizes high consumption of fruits, vegetables, whole grains, nuts, and legumes, moderate consumption of low-fat dairy products, as well as low consumption of sodium, sweetened beverages, and red and processed meats [ 14 ]. Notably, the DASH diet has been recommended as one of three healthy dietary patterns in the 2020–2025 United States Dietary Guidelines for the public [ 15 ].

To date, many epidemiological studies have found that adherence to the DASH diet is significantly associated with several non-communicable diseases, such as hypertension, cardiovascular disease, chronic kidney disease, and certain types of cancer [ 16 , 17 , 18 ]. Notably, less is known about the influence of the DASH diet on the risk of DM. In 2009, Liese et al. published the first study reporting the association between adherence to the DASH diet and type 2 DM in the multiethnic Insulin Resistance Atherosclerosis Study [ 8 ]. Since then, there have been considerable attentions in medical research on the relationship between greater adherence to the DASH diet and risk of DM [ 7 , 8 , 19 , 20 ]. However, the results are not entirely consistent. Although some epidemiological studies have shown the protective role of the DASH diet in the development of DM [ 7 , 8 , 19 , 21 ], others have found no association [ 20 , 22 ]. Moreover, an earlier meta-analysis of 20 randomized controlled trials (RCTs) on the DASH diet and its impact on type 2 DM risk demonstrated that the DASH diet could significantly reduce fasting insulin concentration [ 23 ]. Furthermore, to the best of our current knowledge, there is no published systematic review and dose-response meta-analysis evaluating the effect of adherence to the DASH diet on DM risk. Therefore, we performed a systematic review and dose-response meta-analysis of observational studies published from inception until September 2023 to assess the potential impact of adherence to the DASH diet on DM risk.

Materials and methods

We followed the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for reporting this study [ 24 ]. A protocol has been registered in the International Prospective Register of Systematic Reviews (CRD42023465848).

Search strategy

An electronic literature search via four databases, including PubMed, Embase, Scopus, and CNKI was performed to find related publications written in the English or Chinese languages published from their dates of inception up to September 2023, with the following terms: {[“diabetes” (all fields) OR “diabetes mellitus” (all fields)] AND [“DASH score” (all fields) OR “DASH diet” (all fields) OR “DASH” (all fields) OR “Dietary Approaches To Stop Hypertension” (all fields) OR “Dietary Approaches To Stop Hypertension” (MeSH)]}. Moreover, we also found potentially related articles by manually searching the reference lists of retrieved articles and previously published reviews. The current search strategy was carried out by two independent reviewers (B.-F.C and X.-Y.Q).

Study selection

Two reviewers (B.-F.C and X.-Y.Q) independently screened the titles and abstracts of potential articles retrieved during the initial search, and ascertained studies exploring the relationship between adherence to the DASH diet and DM risk. After all reviewers agreed, the full-text versions of articles were reviewed according to the inclusion and exclusion criteria for the present systematic review and meta-analysis. The following studies were eligible for our analyses: (1) observational studies (e.g., case-control, cohort, or cross-sectional studies) performed in participants aged ≥18 years; (2) reported the data on the association between adherence to the DASH diet and DM risk; (3) provided the multivariable adjusted RRs, HRs, ORs with their corresponding 95%CIs of DM for the highest versus the lowest categories of DASH diet score (or sufficient data to calculate them); (4) If the data in the retrieved article lacked sufficient detail, the corresponding author of the original study is contacted via email; (5) DM diagnoses were confirmed by clinical interviews or self-report on a previous physician- made diagnosis of DM. Also, studies were excluded if they met one of the following criteria: (1) non-observational studies, e.g., letters, editorials, conference abstracts, reviews, or case reports; (2) studies written in non-English or non-Chinese; (3) irrelevant articles; (4) HRs, RRs or ORs with 95%CIs were not reported in the study. Any disagreements were settled by discussion or in consultation with the third reviewer (X.-M.S). The study population, exposure, comparison, outcome, and study design (PEICO) are shown in Table S1 .

Data extraction

Two independent reviewers (B.-F.C and X.-Y.Q) extracted the following information from the selected studies: the first author’s last name, publication year, location, study design, total number of participants, number of DM cases, mean age/age range for cases and participants, dietary assessment method, the factors that were adjusted or matched for in analyses, and reported risk estimates (HRs/ ORs/RRs) and their corresponding 95%CIs for highest versus lowest categories of DASH diet score.

Quality assessment

The Newcastle-Ottawa Quality Scale(NOS) was applied by two reviewers to independently assess the quality of included studies in this meta-analysis [ 25 ]. According to this scale, each study could be assigned a maximum score of 9 points for three main domains: selection(range 0–4 points), comparability (range 0–2 points), and assessment of outcomes (range 0–3 points). Studies scoring 7–9 points, 4–6 points, and 0–3 points, were identified as being high, medium, and low quality, respectively [ 26 ]. Disagreements were resolved by discussion to reach a consensus.

Statistical analysis

In this meta-analysis, we used RRs and 95%CIs as the effect size for the main analyses. Besides, ORs were converted into RRs using the following formula: RR = OR/[(1 − P 0 ) + (P 0 *OR)], in which P 0 shows the incidence of DM in the non-exposed group [ 27 ]. Subsequently, the RRs and corresponding 95% CIs for comparing incident DM between the highest and the lowest categories of DASH diet scores, were used to calculate log-transformed RRs with their corresponding standard errors (SEs). The Cochran’s Q test and I 2 statistic were utilized to evaluate the potential sources of between-study heterogeneity. P -values of Cochran’s Q test >0.10 or I 2  > 50% were considered to indicate high heterogeneity among the included studies, and then a random-effects model(DerSimonnian and Laird method) was used to pool the RRs. Otherwise, a fixed-effects model was used to calculate the pooled RRs [ 28 ]. If high heterogeneity was present, sensitivity and subgroup analyses were used to explore potential sources of heterogeneity. Subgroup analyses were performed based on comparison (Q5 vs Q1/Q4 vs. Q1/T3 vs. T1), mean age (>50 y/<50 y), country (Western countries/Asian countries), and study design (cohort/case-control studies). If more than 10 studies were available, publication bias was assessed through the visual inspection of the funnel plots and quantified by both Begg’s and Egger’s tests [ 29 ]. Sensitivity analyses were performed to explore the extent to which the pooled RRs might be affected by a single study or a group of studies. More importantly, we also performed a dose-response meta-analysis to estimate the RRs for each 1-score increase in DASH diet adherence. A two-stage GLST model based on generalized least squares was used to examine potential linear or non-linear dose-response relationship between adherence to the DASH diet and DM risk. All data analyses were carried out using STATA, version 11.2 (Stata Corp, College Station, TX). A 2-sided P value < 0.05 was considered statistically significant.

Search results

In the initial search, we retrieved 1373 articles through four database search and the reference lists of included studies and previously published reviews. After removing 563 duplicates, 810 articles were left for further screening. Consistently, reading titles and abstracts of these articles led to the exclusion of 773 articles because they didn’t report the relationship between adherence to DASH diet and risk of DM. Accordingly, the remaining thirty-seven full-text articles were examined for eligibility, and 22 were excluded for the following reasons: 5 did not evaluated DM risk; 1 lacked sufficient data and the corresponding author of this study could not be contacted; 16 did not mention DASH diet score. Finally, fifteen studies(13 cohort and 2 case-control studies) with 557,475 participants and 57,064 cases of DM were included for this systematic review and meta-analysis [ 6 , 7 , 8 , 19 , 21 , 30 , 31 , 32 , 33 , 22 , 20 , 34 , 35 , 36 , 37 ]. Figure 1 . indicated the flow chart of article selection process.

Flow chart of the process of study selection

Study characteristics

Characteristics of each eligible study are shown in Table 1 . Of these included studies, thirteen were cohort studies [ 7 , 8 , 19 , 21 , 30 , 31 , 32 , 33 , 22 , 20 , 34 , 35 , 36 ], and two were case-control studies [ 6 , 37 ]. The publication dates of these studies mentioned above varied between 2009 and 2023. Age of study participants ranged from 18 to 84 years. Sample size ranged from 334 to 166500. Eight of the included studies were performed in the United States [ 7 , 8 , 19 , 21 , 30 , 22 , 32 , 33 ], three in Iran [ 6 , 20 , 34 ], one in Taiwan China [ 35 ], one in Singapore [ 31 ], one in Brazil [ 36 ], and one study in Europe [ 37 ]. Fourteen of included studies used food frequency questionnaires(FFQs) to collect dietary data [ 7 , 8 , 19 , 21 , 30 , 31 , 32 , 33 , 22 , 20 , 34 , 35 , 36 , 37 ], and remaining one study used 24-h dietary recall [ 6 ]. In addition, all included articles used methods designed by Fung et al. (7 food groups and sodium) [ 7 , 30 , 31 , 32 , 33 , 22 , 20 , 34 , 36 ], Dixon et al. (7 food groups, saturated fat and alcohol) [ 6 ], Günther et al. (8 food groups) [ 16 ], Sacks et al. (7 food groups and sodium) [ 19 , 21 , 35 , 37 ] to extract DASH diet. Finally, based on the NOS scores, all of the included studies were considered to be of high-quality studies [ 6 , 7 , 8 , 19 , 21 , 30 , 31 , 32 , 33 , 22 , 20 , 34 , 35 , 36 , 37 ].

Adherence to the DASH diet and DM

Combining 17 effect sizes fSacksrom fifteen articles (557,475 participants and 57,064 cases of DM) were included to evaluate the relationship between adherence to the DASH diet and risk of DM in this study. Figure 2 showed the evidence of a reduced risk of DM in the highest compared with lowest categories of DASH diet (RR:0.82; 95% CI: 0.76, 0.90, P  < 0.0001). The high heterogeneity was found among the included studies ( P  < 0.0001; I 2  = 89.1%) and hence the effect was assessed using a random-effects model.

Forest plot of the association between adherence to the DASH diet and risk of DM

Dose-response analysis

Twelve studies [ 6 , 8 , 19 , 20 , 21 , 31 , 32 , 34 , 35 , 36 , 37 ] involving 11 cohort studies with DASH diet scores, were included in this dose-response analysis for DM risk. The dose-response meta-analysis showed a linear trend association between adherence to the DASH diet and DM risk (RR:0.99; 95%CI: 0.97–1.02, P dose-response  = 0.546, P nonlinearity  = 0.701) (Fig. 3 ). There was also a linear trend association between DASH diet and DM risk in the analysis of cohort studies ( P nonlinearity  = 0.482, P dose-response  = 0.599) (Fig. 4 ).

Dose-response association between adherence to the DASH diet and risk of DM in the analysis of twelve studies

Dose-response association between adherence to the DASH diet and risk of DM in the analysis of cohort studies

Subgroup analyses

Considering the high heterogeneity of the present study ( P  < 0.0001; I 2  = 89.1%), subgroup analyses were performed to further explore the potential sources of heterogeneity among included studies (Table 2 ). Subgroup analyses was stratified basing on study design (cohort vs. case-control studies), country (Western vs. Asian countries), age (>50 y vs. <50 y), and comparison (Q5 vs.Q1/Q4 vs.Q1/T3 vs.T1). When we conducted analyses separately by study design (Fig. 5 ), we found a significant inverse association between adherence to the DASH diet and DM risk in case-control studies (RR = 0.65; 95% CI: 0.29–1.43, P  < 0.001). In the cohort studies, there was a marginally significant association between the DASH diet and risk of DM (RR = 0.83; 95% CI: 0.76–0.91, P  < 0.001). The stratified association between the DASH diet and risk of DM according to country (based on the random-effects model) is provided in Fig. 6 . There was significant heterogeneity in Asian countries, where a decreased risk of DM was shown (RR = 0.83; 95% CI: 0.66–1.05, P  < 0.001). Similarly, in Western countries, the risk is similarly reduced (RR = 0.81; 95% CI: 0.77–0.85, P  = 0.011). When we conducted analyses separately by age (Fig. 7 ), we found a significant inverse association between the DASH diet and DM risk in all age groups (<50 y: RR = 0.80; 95% CI: 0.66–0.97, P  < 0.001 and >50 y: RR = 0.81; 95% CI: 0.77–0.86, P  = 0.003). Similarly, we also performed stratified analysis based on comparison in Fig. 8 . Among comparison studies, there was more heterogeneity in T3 vs T1 ( P  = 0.012, I 2  = 72.5%), and a significantly decreased risk of DM was shown (RR = 0.74; 95%CI: 0.64, 0.86; P  = 0.012). In Q5 vs Q1 and Q4 vs Q1, a marginally significant association between DASH diet and risk of DM was shown.

Subgroup analyses of DASH diet and risk of DM according to study design

Subgroup analyses of DASH diet and risk of DM according to country

Subgroup analyses of DASH diet and risk of DM according to age

Subgroup analyses of DASH diet and risk of DM according to comparison

Publication bias

The funnel plots showed little evidence of asymmetry (Supplementary Fig. 1 ), and there is no evidence of publication bias (highest versus lowest categories of DASH diet score: Egger’s test, P  = 0.088; Begg’s test, P  = 0.711).

The quality of included studies was shown in Appendix 1. All included studies received an NOS score ≥7, and they were considered to be of high-quality studies [ 6 , 7 , 8 , 19 , 30 , 31 , 32 , 33 , 22 , 20 , 34 , 35 , 36 , 37 ].

Sensitivity analyses

Based on the results of sensitivity analysis (Supplementary Fig. 2 ), a cohort study performed by Mirmiran et al. [ 10 ] was outside the limit and might be the source of heterogeneity. When this study was excluded in the repeat analysis (Supplementary Fig. 3 ), the results showed a slight reduction in the pooled RRs of the association between adherence to the DASH diet and DM risk (RR = 0.81; 95%CI: 0.76–0.86, P  < 0.001). Meanwhile, heterogeneity of included studies has decreased from 89.1% to 71.0%.

To our current knowledge, this is the first systematic review and meta-analysis to quantitatively summarize the published evidence on the association between greater adherence to the DASH diet and the risk of developing DM. Our analysis includes data from fifteen studies, involving 57,064 diabetes cases and 557,475 participants. The results demonstrated that high adherence to the DASH diet was associated with an 18% reduction in the risk of DM. Furthermore, the dose-response analysis showed a linear trend between DASH diet and DM risk. Sensitivity analysis also showed that excluding a certain study did not significantly alter the pooled effect of DASH diet adherence on DM risk. Overall, our findings add to the evidence of an inverse association between adherence to the DASH diet and DM risk and support the adoption of DASH diet adherence as a primary prevention strategy for DM.

Over the past four decades, the global epidemic of DM has continued to escalate [ 38 ]. As of 2021, an estimated 537 million adults worldwide are living with diabetes, with 80% of them residing in low- and middle-income countries [ 3 ]. This continued upward trend underscores the urgency of preventive measures. Among the various risk factors for DM, dietary factors, especially overall dietary patterns have garnered considerable attention [ 5 ]. Researchers have identified the whole dietary patterns using either a priori or a posteriori methods in nutritional epidemiology studies [ 12 ]. For example, the DASH diet, as a priori dietary pattern, emphasizes high consumption of fruits, vegetables, whole grains, nuts, and legumes, moderate consumption of low-fat dairy products, as well as low consumption of sodium, sweetened beverages, and red and processed meats [ 14 ]. Despite numerous epidemiological studies have investigated the relationship between adherence to the DASH diet and DM risk [ 20 , 22 , 33 , 34 , 35 , 36 , 37 ], the findings remain inconsistent. For instance, the Iran cohort study by Esfandiar and colleagues reported a 13% increased risk of DM associated with adherence to the DASH diet (RR = 1.13, 95%CI: 0.88–1.46) [ 34 ]. Conversely, the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) found that greater adherence to the DASH diet was not significantly related to the risk of newly diagnosed DM (OR:1.07; 95%CI: 0.84–1.36) [ 36 ]. The observed inconsistencies in published studies might be attributed to sociodemographic disparities among study populations and differences in the adjustment for potential confounders. For instance, in a previous systematic literature review and meta-analysis, Jannasch et al found that adherence to the DASH diet may change over time due to shifts in socioeconomic factors including poverty, globalization, and increased access to high-calorie foods [ 39 ]. Among the 15 studies included, most adjusted for critical confounding variables, such as age, BMI, physical activity, smoking status, and energy intake. Besides, some studies also adjusted for family history of DM and sex [ 39 ]. In our study, nine of the included studies were conducted in Western countries, while six studies were conducted in Asian countries and elsewhere. It is widely recognized that there exist significant differences between Eastern and Western diets [ 40 ]. Furthermore, to date, the relationship between adherence to the DASH diet and risk of DM has not yet been studied in a systematic review and dose-response meta-analysis. In this context, identifying the association between adherence to the DASH diet and DM risk through a systematic review and meta-analysis would seem to hold significant value.

Our analyses revealed a significant inverse association between adherence to the DASH diet and DM risk. Consitent with our results, several prior studies have reported an inverse association between “healthy” dietary patterns, which share some similar components with the DASH diet, and the risk of DM [ 41 ]. Consistently, a previous meta-analysis of 10 prospective cohort studies, involving 404,528 participants, found that high adherence to “healthy” dietary patterns significantly reduced the risk of type 2 DM (RR:0.86; 95%CI:0.82–0.90) [ 42 ]. However, contrary to our results, another studies did not find a significant association between adherence to the DASH diet and DM risk [ 35 , 36 ]. Although evidence associating DASH diet to DM remains somewhat inconsistent, some possible mechanisms have been reported to explain the observed inverse association. First, the DASH diet emphasizes increased consumption of fruits, vegetables, whole grains, and legumes, all of which are rich sources of dietary fiber. Increasing evidence has suggested that higher intake of dietary fiber was associated with a reduced risk of type 2 DM [ 43 ]. Moreover, Lattimer and Haub’s findings underscored that high intake of dietary fiber, particularly soluble fiber, could delay gastric emptying and limit carbohydrate absorption, reducing in lower postprandibular blood glucose and insulin levels [ 44 ]. Second, vegetables and whole grains are the most important foods in the DASH diet, both of which have a low glycaemic index and load. A recent systematic review and updated meta-analyses of prospective cohort studies showed a robust association between diets high in glycemic index and glycemic load and reduced risk of type 2 DM [ 45 ]. Third, whole grains consumption has been linked to a decrease the risk of overweight and obesity [ 46 ], both of which are recognized risk factors for DM [ 47 ]. Fourth, numerous studies have suggested that antioxidants, including vitamin C and carotenoids abundant in vegetables and fruits, are associated with the lower risk of obesity and hypertension, all of which are critical risk factors for type 2 DM [ 48 , 49 ]. Notably, Evans and colleagues proposed that dietary antioxidants could counteract oxidative stress, thereby enhancing insulin sensitivity and improving insulin secretion [ 50 ]. Fifth, adherence to the DASH diet’s beneficial effect on DM risk may stem from the consumption of low-fat dairy products. Prori studies demonstrated that higher dairy intake, particularly low-fat dairy, were correlated with a reduced risk of type 2 DM in men [ 51 ]. Lastly, the DASH diet, characterized by its emphasis on minimizing red and processed meats consumption, has been associated with DM risk. Shu et al. reported that high consumption of red meat was associated with an increased risk of type 2 DM [ 40 ]. As far as we know, red and processed meats are also rich sources of dietary iron. Epidemiological studies have shown that excessive iron stores in the body can promote insulin resistance, thereby increasing the risk of type 2 DM [ 52 ]. Additionally, processed meats often contain high levels of nitrates, nitrites, and nitrosamines, which are suspected to increase the risk of type 2 DM [ 40 ]. In summary, the aforementioned these mechanisms support the beneficial association between high adherence to the DASH diet and reduced risk of DM.

While a significant inverse relationship existed between adherence to the DASH diet and DM, our study also found a high heterogeneity (I 2  = 89.1%; P  < 0.001). To address this, we performed subgroup analyses based on study design(cohort vs case-control studies), country (Western vs Asian countries), age groups (>50 y vs <50 y), and comparison (Q5 vs.Q1/Q4 vs.Q1/T3 vs.T1). The results suggested that significant heterogeneity might be mainly due to the differences in age and country. Specifically, when the results were stratified by age and country, the heterogeneity decreased from 89.1% to 64.6%, and 55.0%, respectively. Although significant heterogeneity cannot be fully explored, there are several possible explanations for the high heterogeneity. First, Considering the variations in DASH diet between Eastern and Western populations, it is important to note that while RRs/HRs/ORs were calculated using the highest category of DASH diet (with the lowest category as a reference), the definition of DASH diet may exhibit slight differences across different studies. These variations contribute to the observed significant heterogeneity. Second, the results were pooled from different populations with different dietary habits, which might result in significant heterogeneity. Additionally, fourteen of included studies used FFQs to collect dietary data. Thus, recall bias about dietary intakes is inevitable. Third, despite adjustments for potential confounders have been included in all the included studies, a certain degree of residual or unmeasured confounding factors may exist. Ultimately, substantial heterogeneity remained in the subgroup analyses, indicating the existence of unmeasured confounding factors.

Strengths and limitations

This systematic review and meta-analysis has several strengths and limitations. First, to our knowledge, this is the first comprehensive systematic review and dose-repsonse meta-analysis to clarify the association between adherence to the DASH diet and risk of DM. Our findings add the existing evidence regarding the beneficial effect of high adherence to the DASH diet on DM. Second, the rigorous selection of articles was carried out according to the pre-determined inclusion and exclusion criteria. Third, there were no obvious signs of publication bias in the funnel plot, and the Egger’s and Begg’s tests for publication bias were non-significant. Fourth, the quality assessment showed that the majority of studies included in the present meta-analysis were of high quality. Fifth, subgroup and sensitivity analyses were used to further explore the potential sources of heterogeneity, thereby improving the accuracy of the study results. Finally, the reported ORs/RRs /HRs were multivariate and all included studies had adjusted for some potential confounders, including age, physical activity, and total energy intake, which can affect the relationship between adherence to the DASH diet and DM risk. Despite these strengths, several limitations should be taken into account when interpreting our findings. First, in our analyses, two of the included studies used a case-control design, which is more susceptible to recall and selection bias than a cohort design. Additionally, potential confounding by pre-existing and undiagnosed diseases should be acknowledged. Thus, prospective cohort studies or randomized controlled trials are needed to further confirm the exact association between DASH diet and DM risk. Second, fourteen of the included studies used a FFQ to collect dietary intake data. However, this method may have caused misclassification and resulted in the under-or overestimation of DASH diet consumption. Furthermore, dietary intake data were self-reported, which might also lead to recall and selection biases. Third, the levels of the highest and the lowest categories of DASH diet scores were inconsistent across the included studies, which might have attenuated the true association between adherence to the DASH diet and DM risk. Also, adherence to the DASH diet may have altered over the follow-up period. Studies have reported that adherence to the DASH diet can fluctuate due to changes in socioeconomic factors such as poverty, globalization, and increased access to energy-dense foods. Fourth, significant heterogeneity was found in this study. Although we performed subgroup and sensitivity analyses to explore potential sources of heterogeneity, we could not fully ascertain and explain the sources of inter-study heterogeneity. Fifth, although high adherence to the DASH diet was associated with a decreased risk of DM, the results should be interpreted with caution. Meanwhile, there was also inconsistent adjustment for potential confounders in the included studies. As a result, the data included in our analyses may suffer from varying degrees of completeness and accuracy. Finally, it’s important to note that this study had a geographical restriction. The majority of included studies were performed in the United States, where dietary intakes markedly differ from those in Asian countries. This limitation might reduce the heterogeneity of this meta-analysis. Hence, further large prospective studies and randomized controlled trials are needed to confirm our findings across different regions and populations.

Conclusions

In conclusion, our study showed a significant inverse association between adherence to the DASH diet and the risk of DM. Our findings contribute to the available evidence supporting the protective role of adherence to the DASH diet against DM and highlight the importance of greater adherence to the DASH diet in the prevention of DM. However, future research, particularly well-designed prospective studies or randomized controlled trials, is needed to further confirm these findings across different geographic regions.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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The authors’ responsibilities were as follows: B.-F.C and X.-M.S were responsible for study concept and design; B.-F.C conducted the statistical analysis; X.-Y.Q and B.-F.C were responsible for literature search and screening; X.-Y.Q and Y.-Y.L conducted the data extraction; C.L and T.-G.L performed the quality assessment; B.-F.C and X.-Y.Q were responsible for analysis and interpretation of the data. X.-Y.Q drafted the manuscript. B.-F.C and X.-M.S critically revised the manuscript for important intellectual content. All authors have read and agreed to the published version of the manuscript.

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Quan, X., Shen, X., Li, C. et al. Adherence to the dietary approaches to stop hypertension diet reduces the risk of diabetes mellitus: a systematic review and dose-response meta-analysis. Endocrine (2024). https://doi.org/10.1007/s12020-024-03882-5

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Patient Case Presentation

Mr. E.A. is a 40-year-old black male who presented to his Primary Care Provider for a diabetes follow up on October 14th, 2019. The patient complains of a general constant headache that has lasted the past week, with no relieving factors. He also reports an unusual increase in fatigue and general muscle ache without any change in his daily routine. Patient also reports occasional numbness and tingling of face and arms. He is concerned that these symptoms could potentially be a result of his new diabetes medication that he began roughly a week ago. Patient states that he has not had any caffeine or smoked tobacco in the last thirty minutes. During assessment vital signs read BP 165/87, Temp 97.5 , RR 16, O 98%, and HR 86. E.A states he has not lost or gained any weight. After 10 mins, the vital signs were retaken BP 170/90, Temp 97.8, RR 15, O 99% and HR 82. Hg A1c 7.8%, three months prior Hg A1c was 8.0%.  Glucose  180 mg/dL (fasting).  FAST test done; negative for stroke. CT test, Chem 7 and CBC have been ordered.

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Diagnosed with diabetes (type 2) at 32 years old

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Had a cholecystomy at 38 years old

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Mother alive, diagnosed diabetic at 42 years old 

Father alive with Hypertension diagnosed at 55 years old

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Type 2 Diabetes and Hypertension: A Study on Bidirectional Causality

Dianjianyi sun.

1 Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA

2 Department of Public Health Laboratory Sciences, West China School of Public Health, Sichuan University, Chengdu 610041, Sichuan Province, China

Yoriko Heianza

3 Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China

Vivian A. Fonseca

4 Section of Endocrinology and Metabolism, Tulane University School of Medicine, New Orleans, LA

5 Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA

6 Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA

AUTHOR CONTRIBUTIONS

Associated Data

In observational studies, type 2 diabetes (T2D) has been associated with an increased risk of hypertension (HTN), and vice versa; however, the causality between these conditions remains to be determined.

Objectives:

This population-based prospective cohort study sought to investigate the bidirectional causal relations of T2D with HTN, systolic and diastolic blood pressure (SBP and DBP) using Mendelian randomization (MR) analysis.

Methods and Results:

After exclusion of participants free of a history of heart failure, cardiovascular disease, cardiac procedures, and non T2D diabetes, a total of 318,664 unrelated individuals with qualified genotyping data of European descent aged 37–73 from UK Biobank were included. The genetically instrumented T2D and HTN were constructed using 134 and 233 single nucleotide polymorphisms (SNPs), respectively. Seven complementary MR methods were applied, including inverse variance weighted method (IVW), two median-based methods (simple and weighted), MR-Egger, MR-RAPS, MR-PRESSO, and multivariate MR. The genetically instrumented T2D was associated with risk of HTN (OR 1.07 [95% CI, 1.04–1.10], P =3.4×10 −7 ), whereas the genetically determined HTN showed no relationship with T2D (OR 0.96 [0.88–1.04], P =0.34). Our MR estimates from T2D to BP showed that the genetically instrumented T2D was associated with a 0.67 mm Hg higher SBP (95% CI 0.41–0.93, P =5.75×10 −7 ), but not with a higher DBP. There was no clear evidence showing a causal effect of elevated SBP or DBP on T2D risk. Positive pleiotropic bias was indicated in the HTN→T2D relation (OR of MR-Egger intercept 1.010 [1.004–1.016], P =0.001), but not from T2D to HTN (1.001 [0.998–1.004], P =0.556).

Conclusions:

T2D may causally affect HTN, whereas the relationship from HTN to T2D is unlikely to be causal. These findings suggest the importance of keeping an optimal glycemic profile in general populations, and BP screening and monitoring, especially SBP, in patients with T2D.

INTRODUCTION

Type 2 diabetes (T2D) and hypertension (HTN), the two leading components of the global burden of disease, are commonly found to coexist 1 – 3 . The co-existence of T2D and HTN confers a dramatically increased risk (2~4 fold) of cardiovascular disease, end-stage kidney disease, and death, compared to the normotensive and nondiabetic adults 3 . Hence, understanding the bidirectional relations between T2D and HTN is of significant public health importance regarding disease prevention and management of complications.

A large group of prospective studies has associated T2D with an increased HTN risk 4 – 6 , and a similar amount of evidence has been reported on the positive relationship between blood pressure and incident T2D 7 . Nevertheless, these prior observational data were limited for causal inference due to the potential bias introduced by confounding factors and/or reverse causality.

In recent years, Mendelian randomization (MR) analysis, a form of instrumental variable (IV) analysis that leverages the random assortment of genetic variants during gamete formation and therefore minimizes the influence of confounding and reverse causation, has been increasingly used in estimating causal inference between exposures and outcomes 8 . In the present study, we performed bidirectional MR analyses for causal inference between T2D and HTN among 318,664 participants from the UK Biobank, as well as bidirectional MR association analysis of T2D with systolic and diastolic blood pressure (SBP and DBP).

Because of the sensitive nature of the data collected for this study, requests to access the dataset from qualified researchers trained in human subject confidentiality protocols may be sent to UK Biobank & the Access Team at ku.ca.knaboibku@ssecca

Data sources and study participants.

The UK Biobank is a large prospective study of over half a million participants aged 37–73 years living in the United Kingdom. Participant recruitment was conducted in 22 centers across the UK between 2006 and 2010, with a variety of individual-level health information obtained from self-administrated questionnaires, physical measurements, biological sample tests, and linked health records 9 . In the present study, we firstly excluded participants who withdrew from the cohort till Oct 16, 2018 (n=73), had a history of heart disease and procedures (n=61,827) or diabetes other than T2D 10 (n=5989), non-European (n=29815), and without validated genotyping data (n=110,670), leaving 318,664 unrelated European participants for the final analyses (a flow chart of selection of study participants was shown in details in Figure 1 ). Definitions of above-mentioned diseases and heart procedures are presented in the Online Table I and Table II , respectively. All participants provided electronic informed consent, and the study was approved by the NHS National Research Ethics Service (Ref: 11/NW/0382), and Institutional Review Board of Tulane University Health Sciences Center (Study number: 2018–1872).

ASCVD, atherosclerotic cardiovascular disease; T2D, type 2 diabetes; QC, quality control; PCA, principle component analysis.

Ascertainment of T2D and HTN.

Information on prevalent and incident T2D and HTN was regularly collected through cumulative medical records of hospital diagnoses and was supplemented by survey data from questionnaires and physical measures at baseline and in two repeated surveys. The International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) was used in death records; whereas both ICD-10-CM and ICD-9-CM were used in medical records. T2D was defined using a validated algorithm according to self-report diabetes data 10 , as well as ICD-9/10-CM coded hospital records ( Online Table I ). We defined HTN cases using ICD-10-CM code “I10” and ICD 9-CM”250” and “401”, self-reported physician-diagnosed HTN, and the use of blood pressure (BP) lowering medication not due to heart disease (including angiotensin-converting-enzyme inhibitor, angiotensin II receptor blocker, beta blocker, calcium channel blocker, and diuretics). For the BP measures in each survey, systolic BP (SBP) ≥140 mm Hg or diastolic BP (DBP) ≥90 mm Hg was used to define HTN.

Measurements.

Sociodemographic characteristics at recruitment were obtained from local NHS Primary Care Trust registries before arrival at the Assessment Centre, including age (measured in years), sex (female/male), ethnicity (British/Irish/Any other white ethnicity), and Townsend deprivation index (a proxy for the socio-economic position). Information on lifestyles including physical activity (metabolic equivalents (METs) minutes per week calculated on the basis of the International Physical Activity Questionnaire short form 11 ), smoking, and alcohol drinking status (never/previous/current/missing) were obtained using a touch-screen questionnaire. Body mass index (BMI) was derived from weight in kilograms (BC-418MA body composition analyzer) divided by standing height in meters (Seca 202 stadiometer) squared. 9 SBP/DBP (mm Hg) were averaged over two repeated automated measurements (OMRON Healthcare Europe, NA, Hoofddorp) 9 . For participants who reported to be taking BP medication (19.08% of individuals) at baseline and two repeated surveys, we adjusted for medication use by adding 15 and 10 mm Hg to SBP and DBP, respectively. For a small portion (4~5%) of participants who participated in more than one follow-up visits, an average value of above mentioned quantitative measures (including PA METs minutes, BMI, and BP) was used in our analyses. For participants with missing data on BMI (0.29%), SBP (5.46%), DBP (5.46%), Townsend deprivation index (0.12%), and physical activity (0.69%), a “predictive mean matching” multiple imputation approach 12 was applied.

Genotype and imputed data.

UK Biobank genotyping was conducted by the Affymetrix Research Services Laboratory in Santa Clara, California, USA, using two similar custom-designed chips (UK BiLEVE array and UK Biobank Axiom array). General quality control procedures (P for Hardy–Weinberg equilibrium test≥1.0×10 −6 , call rate ≥90%, and imputation R 2 ≥0.3) were employed in the UK Biobank genetic data analysis. Forty genetic principal components (PCs) were calculated, accounting for the effects of the population structure and batch-based genotyping. Genotype imputation was further performed using the 1000 Genomes Phase 3 reference panel, resulting in a dataset of 92,693,895 variants in 487,409 individuals.

Genetic instrument variables for T2D and HTN.

Given the results of four genome-wide association studies (GWAS) in Europeans from DIAGRAM consortium 13 – 16 , we identified 134 out of 193 T2D-related single nucleotide polymorphisms (SNPs) that passed genotyping QC, and restricted to a set of only biallelic SNPs on 22 auto-chromosomes not in a linkage disequilibrium (LD) clumping (R 2 <0.01 within 1 Mb using European population genotype data originated from Phase 3 (Version 5) of the 1000 Genomes Project) as reference. Similarly, 233 out of 262 novel and previously reported SNPs from a recent GWAS of BP conducted by Warren et al 17 were satisfied with our inclusion criteria. Thus, a total of 367 SNPs were selected for genetic IVs ( Online Table III ).

Mendelian randomization analysis.

A diagram for MR was presented by using the genetic variants as IVs ( Online Figure I ). As recommended, six complementary MR approaches were adopted in our analyses to assess the causal effect of the exposure on the outcome and its robustness, including inverse variance weighted method (IVW), two median-based methods (simple and weighted), MR-Egger regression, MR-RAPS (Mendelian randomization using the robust adjusted profile score) 18 , and MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) 19 . We conducted heterogeneity tests in MR analyses using IVW and MR-Egger, sensitivity analyses were performed using the weighted median method and Leave-one-out analysis 20 ( Online Figure II-V ). If there was no evidence of directional pleiotropy ( P for MR-Egger intercept>0.05 8 , 21 ), the estimate from the inverse-variance weighted (IVW) method was considered as the most reliable indicator. Two types of pleiotropy-corrected MR estimates were also reported, including the MR-RAPS estimate (a method for correcting for pleiotropy using robust adjusted profile scores) 18 and the MR-PRESSO estimate (a method for correcting for outliers in IVW) 19 . A consistent MR effect across the six methods might indicate a true causal effect. 22 In addition, multivariable MR (MMR) analysis was performed by considering BMI and dyslipidemia as potential confounders or intermediators ( Online Table IV ).

A multivariate logistic or linear regression model was fitted for binary traits or continuous traits, respectively, as well as odds ratios (OR) and regression coefficients from two models used as the MR estimates. In the IV-exposure association analyses, we controlled for age, sex, Townsend deprivation index, assessment center (22 centers), batch effects (106 batches), and the first ten genetic PCs as covariates (model 1). In the IV-outcome association analyses, we further adjusted for BMI, dyslipidemia, smoking status, alcohol drinking status, and PA METs minutes per week (model 2).

All data analyses were conducted using R version 3.4.4. Missing data imputation was performed using the “ MICE ” R package, and MR analyses were conducted using the “ Two sample Mendelian randomization ”, “ MR-RAPS ” and “ MR-PRESSO ” R packages. The threshold of statistical significance was P<0.05 (2-sided α=0.05).

The average age of the 318,664 UK Biobank participants included was 56.2 years, 44.8% were men, and 93.1% were British ( Table 1 ). Two-thirds of the participants were overweight or obese (BMI≥25.0). Current smokers and drinkers accounted for 10.2% and 93.7%, respectively. There were 13,931 (4.4%) and 172,344 (54.1%) participants with T2D and HTN, respectively. Of note, 85.1% of T2D patients had HTN, while 6.9% of hypertensive participants were diabetic.

Characteristics of Participants from UK Biobank Used in the Analysis

SD, standard deviation; IQR, interquartile range; BMI, body mass index; MET, metabolic equivalent; SBP, systolic blood pressure; DBP, diastolic blood pressure.

In our bidirectional MR analysis, a total of 134 and 233 SNPs were included as genetic IVs for T2D and HTN, respectively ( Figure 1 ). In the T2D→HTN MR analysis by using IVW method, the genetically instrumented T2D increased HTN risk (OR: 1.07; 95% confidence interval [CI], 1.04–1.10, P =3.4×10 −7 ) without detected pleiotropy bias ( P =0.56). In contrast, pleiotropy bias was indicated in the HTN→T2D MR analysis ( P for MR-Egger intercept =0.001), and the IVW estimate showed there was no association between the genetically instrumented HTN and T2D (OR: 0.98; 95% CI, 0.90–1.08, P =0.70). Even after correcting for pleiotropy, our MR results still demonstrated that there was no causal effect of HTN on T2D (OR 0.96 [95%CI: 0.88–1.04], P =0.34 in MR-RAPS; and OR 0.95 [95%CI: 0.88–1.02], P =0.14 in MR-PRESSO). When analyzed using different MR methods, the causal effect of T2D on HTN was quite robust and consistent (ORs ranged from 1.06 to 1.09, all P <0.01), whereas the genetically instrumented HTN was not associated with T2D (ORs ranged from 0.95 to 1.05, all P>0.05 except for MR-Egger). Of note, in our MMR analysis, unbalanced horizontal pleiotropy owing to BMI but not dyslipidemia in the T2D→HTN relation was detected (OR, 0.94 [94% CI, 0.90–0.98], P =0.004), the causal effect of T2D on HTN was further enhanced after controlling for such negative bias (OR: 1.08; 95% confidence interval [CI], 1.05–1.11, P =8.7×10 −8 ) ( Online Table IV ). No horizontal pleiotropy due to BMI or dyslipidemia was detected in our HTN→T2D MR analyses ( Online Table IV ).

Furthermore, our MR analyses showed that the genetically instrumented T2D was consistently associated with a higher SBP (regression coefficients [β] in mm Hg ranged from 0.39 to 0.83, all P <0.01 except for P for MR-Egger =0.066), but not with a higher DBP (β ranged from −0.07 to 0.32) ( Table 2 ). When SBP/DBP→T2D relations were analyzed, neither the genetically instrumented SBP nor DBP increased T2D risk (OR ranged from 0.976 to 1.003 for the SBP→T2D relation, and OR ranged from 0.961 to 1.013 for the DBP→T2D relation) ( Table 3 ).

Mendelian Randomization Associations of Type 2 Diabetes with Systolic and Diastolic Blood Pressure using Genetic Instrument Variables

The effect size was presented as a regression coefficient and its 95% confidence interval. CI, confidence interval; T2D, type 2 diabetes; SBP, systolic blood pressure; DBP, diastolic blood pressure; IVW, the inverse-variance weighted (IVW) method; IV, instrument variables; MR, Mendelian randomization; MR-RAPS, an MR method for correcting for horizontal pleiotropy using robust adjusted profile scores; MR-PRESSO, an MR method for correcting for pleiotropy residual sum and outlier.

Mendelian Randomization Associations of Systolic and Diastolic Blood Pressure with Type 2 Diabetes using Genetic Instrument Variables

The effect size was presented as odds ratio and its 95% confidence interval. CI, confidence interval; T2D, type 2 diabetes; SBP, systolic blood pressure; DBP, diastolic blood pressure; IVW, the inverse-variance weighted (IVW) method; IV, instrument variables; MR, Mendelian randomization; MR-RAPS, an MR method for correcting for horizontal pleiotropy using robust adjusted profile scores; MR-PRESSO, an MR method for correcting for pleiotropy residual sum and outlier.

By using individual-level data for 318,664 UK Biobank participants, our bidirectional MR analyses showed consistent evidence that the genetically instrumented T2D increased HTN risk, whereas the MR estimates for the HTN→T2D relation were unlikely to be causal. In addition, the genetically instrumented T2D was strongly associated with a higher SBP rather than a higher DBP.

Our study is the first to provide strong evidence for a causal relationship of T2D with HTN risk, dominantly driven by the causal effect of T2D on a higher SBP instead of DBP. Goharian et al 23 , however, didn’t find a causal relationship between fasting glucose and SBP/DBP in healthy children and adolescents, likely due to insufficient statistical power (n=1506), narrowed variance of BP in the young population, a lack of cumulative impact of raised glucose level on BP in childhood, or pleiotropy bias. However, our findings are in line with prospective cohort studies showing T2D and hyperglycemia were associated with incident HTN 4 – 6 , as well as two MR studies conducted in general adults showing that higher glucose level 24 and greater genetic predisposition to T2D 25 were associated with increased arterial stiffness, which coincided with the development of HTN. The precise mechanisms for our findings of a causal relationship of T2D with a higher SBP but not a higher DBP are largely unknown. We speculate that an accelerated arterial stiffness resulting from T2D was associated with a greater increase in SBP instead of a higher DBP during the aging process 1 , 26 , 27 . Furthermore, a recent meta-analysis of 49 trials demonstrated that the antihypertensive treatment for lowering SBP rather than DBP in patients with diabetes reduced the risk of all-cause and cardiovascular mortality substantially, as well as incident myocardial infarction, stroke, heart failure, and end-stage renal disease 28 .

The T2D→HTN causality is biologically plausible. T2D shared broad cardiometabolic disorders, including obesity, insulin resistance (IR), β-cell dysfunction, inflammation, oxidative stress, vascular dysfunction, sodium retention, sympathetic excitation, renin-angiotensin-aldosterone system activation, and kidney damage, which has been widely proposed in the initiation and maintenance of HTN 3 , 29 . However, the magnitude of our genetic association of T2D on HTN (ORs ranged from 1.06 to 1.09) were much lower compared to the observational associations in the current study (multivariate-adjusted OR, 2.42 [95% CI, 2.32–2.53] in Online Table V ) and in Framingham Offspring Study (3.14 [95% CI, 2.17–4.54]) 30 . First, observational associations might be over-estimated due to various residual confounding and other bias (e.g., subtle arterial stiffness and kidney disease ahead of T2D onset 3 , 5 ). Second, the genetically instrumented T2D in our MR analyses might not be comprehensively characterized by a complex network of T2D pathophysiologic mechanisms 31 , 32 on HTN development. Third, the role of detection bias in inflating the observational estimates should also be considered, where patients with diagnosed and treated diabetes will be much more likely to have close surveillance of their BP and start on antihypertensive medications for reasons (e.g., heart failure, left ventricular hypertrophy, and chronic kidney disease) rather than elevated BP 5 . Additionally, as our MMR analysis showing a negative association between T2D-IVs and BMI, we speculated that the lower MR estimate might be as a result of undetected negative bias due to unbalanced pleiotropic effects of T2D-IVs on above mentioned biological pathways 8 . Further studies are warranted to elucidate the precise mechanisms.

Previous MR studies for a causal relationship from elevated BP to an increased risk of T2D have yielded inconsistent results. Aikens et al 33 reported that 1-mm Hg genetic increase in SBP was associated with a 2% increased risk of T2D, by adopting a 2-sample MR approach integrating summary-level GWAS data from 37,293 T2D cases and 125,686 controls. In contrast, a more recent MR study conducted by Zhu et al 34 using data from two community-based studies (n=162,030) showed that there were no causal relations from BP to T2D (OR, 1.07 [95%CI, 0.89–1.29]; P =0.44 for SBP→T2D; OR, 1.12 [95%CI, 0.94–1.33]; P =0.20 for DBP→T2D), which was in line with our findings. In a case-control setting, selection bias might be inferred as T2D patients were more likely to take BP lowering medications compared with controls, in which the use of β blocker and diuretics was associated with the increased risk of T2D 35 . Hence, MR estimates based on a general population would be less biased for causal inference between HTN and T2D risk 36 . Moreover, in compared with 29 BP-related SNPs accounted for only 2.2% of BP variance 37 were adopted as IVs in the above two MR studies 33 , 34 , a total number of over 200 SNPs here we used as IVs for BP might be more indicative as over 3.56% of the variance could be explained 17 .

Previous reported observational associations of HTN with T2D risk were unlikely to be causal, and instead, might be the results of two of bias ― collider bias or pleiotropy 8 . As mentioned previously 33 , a false association between HTN and T2D might occur when the study sample comprised an excess number of undiagnosed coronary artery disease cases in T2D patients (collider stratification) 38 . Our MR estimates, however, were less likely to be affected by collider bias as we excluded patients with prevalent and incident cardiovascular diseases before running MR analyses. But still, the causal HTN→T2D relationship can be true in terms of recently proposed biologic mechanisms that HTN manifests as vasoconstriction 39 , IR 40 , and inflammation 33 , which increase the risk of T2D.

Our study is the first to use a bidirectional MR approach to investigate the causal relationship between T2D and HTN/SBP/DBP among over 300 thousand adults, with a variety of sensitivity analyses and MR diagnostics performed for evaluating the robustness of our MR estimates. However, there are several limitations. First, the lack of data on glycemic traits (e.g., fasting glucose, insulin, and HbA1c) might have led to a small number of undiagnosed T2D cases, who were misclassified into controls. However, the information on the linked health records provided us with an alternative way to identify such cases. Second, the lack of data on insulin resistance, β cell function, chronic inflammation, and renal function limited further investigation of the precise mechanisms underlying the observed bidirectional associations between T2D and HTN. Third, the present MR analyses conducted in participants of European descent might limit the generalization of our findings in other ancestry groups.

In summary, our comprehensively bidirectional MR results suggest a potentially causal T2D→HTN relationship, especially a causal relationship of T2D with a higher SBP but not with a higher DBP. In contrast, the HTN→T2D association was unlikely to be causal. Our findings have clinical significance for maintaining an optimal glycemic profile for the general populations, and the importance of BP screening and monitoring, especially SBP, in patients with T2D.

T2D, type 2 diabetes; HTN, hypertension; IVs, instrument variables; IVW, the inverse-variance weighted (IVW) method; MR, Mendelian randomization; OR, odds ratio; CI, confidence interval; MR RAPS, an MR method for correcting for horizontal pleiotropy using robust adjusted profile scores.

a , MR-RAPS estimates were given after pruning two SNPs (rs73455744 and rs7041847) with extraordinarily large direct effects 18 ;

b , MR-RAPS estimates were given after pruning six SNPs (rs10922502, rs2760061, rs17477177, rs12628032, rs10948071, and rs449789) with extraordinarily large direct effects 18 ;

c , MR-PRESSO IV outlier detected was rs12899811;

d , MR-PRESSO IV outliers detected: rs4660293, rs62270945, rs2071518, rs2782980, rs8059962, and rs76326501.

NOVELTY AND SIGNIFICANCE

What is known.

• Type 2 diabetes (T2D) is associated with an increased risk of hypertension (HTN), and vice versa, in observational studies.

What New Information Does This Article Contribute?

• T2D may causally affect hypertension, whereas the HTN→T2D relation is unlikely to be causal.

In this bidirectional Mendelian randomization analysis, the genetic predisposition to T2D is associated with the development of HTN and elevated SBP, but not with elevated diastolic BP; whereas the “HTN to T2D” relation is unlikely to be causal. BP control, especially for SBP control, in patients with T2D is essential in clinical practice and self-management, and it is of great importance for general populations to maintain an optimal glycemic profile and a normal BP level.

Supplementary Material

314487 online, acknowledgments.

The authors thank the participants, the members, the project development and management teams in the present study in the UK for their outstanding commitment and cooperation. This research has been conducted using the UK Biobank Resource, approved project number 29256.

SOURCES OF FUNDING

Dr. Qi is supported by grants from the National Heart, Lung, and Blood Institute (HL071981, HL034594, HL126024), the National Institute of Diabetes and Digestive and Kidney Diseases (DK091718, DK100383, DK078616), the Boston Obesity Nutrition Research Center (DK46200), and United States–Israel Binational Science Foundation Grant2011036. This study has been conducted using the UK Biobank Resource, approved project number 29256.

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Taking insulin  or other diabetes medicines is often part of treating diabetes. In addition to making healthy food and beverage choices, getting physical activity, getting enough sleep, and managing stress, medicines can help you manage the disease. Some other treatment options are also available.

The medicine you take depends on the type of diabetes you have and how well the medicine controls your blood glucose  levels, also called blood sugar levels. Other factors, such as any other health conditions you may have, medication costs, your insurance coverage and copays, access to care, and your lifestyle, may affect what diabetes medicine you take.

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If you have type 1 diabetes , you must take insulin because your pancreas  does not make it. You will need to take insulin several times during the day, including when you eat and drink, to control your blood glucose level.

There are different ways to take insulin . You can use a needle and syringe , an insulin pen , or an insulin pump . An artificial pancreas —also called an automated insulin delivery system—may be another option for some people.

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Some people with type 2 diabetes  can control their blood glucose level by making lifestyle changes. These lifestyle changes include consuming healthy meals and beverages, limiting calories if they have overweight  or obesity , and getting physical activity.

Many people with type 2 diabetes need to take diabetes medicines as well. These medicines may include diabetes pills or medicines you inject, such as insulin. Over time, you may need more than one diabetes medicine to control your blood glucose level. Even if you do not take insulin, you may need it at special times, such as if you are pregnant or if you are in the hospital for treatment.

Gestational diabetes

If you have gestational diabetes , you can manage your blood glucose level by following a healthy eating plan and doing a moderate-intensity physical activity, such as brisk walking for 150 minutes, each week. If consuming healthy food and beverages and getting regular physical activity aren’t enough to keep your blood glucose level in your target range, a doctor will work with you and may recommend you take insulin. Insulin is safe to take while you are pregnant.

No matter what type of diabetes you have, taking diabetes medicines every day can feel like a burden sometimes. New medications and improved delivery systems can help make it easier to manage your blood glucose levels. Talk with your doctor to find out which medications and delivery systems will work best for you and fit into your lifestyle.

Several types of insulin are available. Each type starts to work at a different speed, known as “onset,” and its effects last a different length of time, known as “duration.” Most types of insulin reach a peak, which is when they have the strongest effect. After the peak, the effects of the insulin wear off over the next few hours or so. Table 1 lists the different types of insulin, how fast they start to work, when they peak, and how long they last.

Table 1. Types of insulin and how they work 1,2

Another type of insulin, called premixed insulin, is a combination of insulins listed in Table 1. Premixed insulin starts to work in 15 to 60 minutes and can last from 10 to 16 hours. The peak time varies depending on which insulins are mixed.

Your doctor will work with you to review your medication options. Talk with your doctor about your activity level, what you eat and drink, how well you manage your blood glucose levels, your age and lifestyle, and how long your body takes to absorb insulin.

Follow your doctor’s advice on when and how to take your insulin. If you're worried about the cost, talk with your doctor. Some types of insulin cost more than others. You can also find resources to get financial help for diabetes care .

The way you take insulin may depend on your lifestyle, insurance plan, and preferences. Talk with your doctor about the options and which one is best for you. Most people with diabetes take insulin using a needle and syringe, insulin pen, or insulin pump. Inhalers and insulin jet injectors  are less common ways to take insulin. Artificial pancreas systems are now approved by the U.S. Food and Drug Administration (FDA). Talk with your doctor to see if an artificial pancreas is an option for you.

Needle and syringe

You can give yourself insulin shots using a needle and syringe . You draw up your dose of insulin from the vial—or bottle—through the needle into the syringe. Insulin works fastest when you inject it in your belly, but your doctor may recommend alternating the spot where you inject it. Injecting insulin in the same spot repeatedly could cause the tissue to harden, making it harder to take shots in that area over time. Other spots you can inject insulin include your thigh, buttocks, or upper arm, but it may take longer for the insulin to work from those areas. Some people with diabetes who take insulin need 2 to 4 shots a day to reach their blood glucose targets. Others can take a single shot. Injection aids can help you give yourself the shots.

An insulin pen looks like a writing pen but has a needle for its point. Some insulin pens come filled with insulin and are disposable. Others have room for an insulin cartridge that you insert and replace after use. Many people find insulin pens easier to use, but they cost more than needles and syringes. You may want to consider using an insulin pen if you find it hard to fill the syringe while holding the vial or cannot read the markings on the syringe. Different pen types have features that can help with your injections. Some reusable pens have a memory function, which can recall dose amounts and timing. Other types of “connected” insulin pens can be programmed to calculate insulin doses and provide downloadable data reports, which can help you and your doctor adjust your insulin doses.

An insulin pump is a small machine that gives you steady doses of insulin throughout the day. You wear one type of pump outside your body on a belt or in a pocket or pouch. The insulin pump connects to a small plastic tube and a very small needle. You insert the plastic tube with a needle under your skin, then take out the needle. The plastic tube will stay inserted for several days while attached to the insulin pump. The machine pumps insulin through the tube into your body 24 hours a day and can be programmed to give you more or less insulin based on your needs. You can also give yourself doses of insulin through the pump at mealtimes.

Another type of pump has no tubes. This pump attaches directly to your skin with a self-adhesive pad and is controlled by a hand-held device. The plastic tube and pump device are changed every several days.

Another way to take insulin is by breathing powdered insulin into your mouth from an inhaler device. The insulin goes into your lungs and moves quickly into your blood. You may want to use an insulin inhaler to avoid using needles. Inhaled insulin is only for adults with type 1 or type 2 diabetes. Taking insulin with an inhaler is less common than using a needle and syringe.

Jet injector

A jet injector is a device that sends a fine spray of insulin into the skin at high pressure instead of using a needle to deliver the insulin. It is used less commonly than a needle and syringe or a pen.

Artificial pancreas

An artificial pancreas is a system of three devices that work together to mimic how a healthy pancreas controls blood glucose in the body. A continuous glucose monitor (CGM)  tracks blood glucose levels every few minutes using a small sensor inserted under the skin that is held in place with an adhesive pad. The CGM wirelessly sends the information to a program on a smartphone or an insulin infusion pump. The program calculates how much insulin you need. The insulin infusion pump will adjust how much insulin is given from minute to minute to help keep your blood glucose level in your target range. An artificial pancreas is mainly used to help people with type 1 diabetes.

You may need to take medicines to manage your type 2 diabetes, in addition to consuming healthy foods and beverages and being physically active. You can take many diabetes medicines by mouth. These medicines are called oral medicines.

Most people with type 2 diabetes start with metformin pills. Metformin also comes as a liquid. Metformin helps your liver make less glucose and helps your body use insulin better. This drug may help you lose a small amount of weight.

Other oral medicines act in different ways to lower blood glucose levels. Combining two or three kinds of diabetes medicines can lower blood glucose levels better than taking just one medicine.

Read about different kinds of diabetes medicines (PDF, 2.8 MB) from the FDA.

If you have type 1 diabetes, your doctor may recommend you take other medicines, in addition to insulin, to help control your blood glucose. Some of these medicines work to slow how fast food and beverages move through your stomach . These medicines also slow down how quickly and how high your blood glucose levels rise after eating. Other medicines work to block certain hormones  in your digestive system  that raise blood glucose levels after meals or help the kidneys to remove more glucose from your blood.

Besides insulin, other types of injected medicines (PDF, 2.8 MB) are available that will keep your blood glucose level from rising too high after you eat or drink. These medicines, known as glucagon-like peptide-1 (GLP-1) receptor agonists, 3 may make you feel less hungry and help you lose some weight. GLP-1 medicines are not substitutes for insulin.

Side effects are problems that result from taking a medicine. Some diabetes medicines can cause hypoglycemia , also called low blood glucose, if you don’t balance your medicines with food and activity.

Ask your doctor whether your diabetes medicine can cause hypoglycemia or other side effects, such as upset stomach and weight gain. Aim to take your diabetes medicines as your doctor instructs you, to help prevent side effects and diabetes problems.

If medicines and lifestyle changes are not enough to manage your diabetes, there are other treatments that might help you. These treatments include weight-loss (bariatric) surgery  for certain people with type 1 or type 2 diabetes, or pancreatic islet transplantation  for some people with type 1 diabetes.

Weight-loss surgery

Weight-loss surgery  are operations that help you lose weight by making changes to your digestive system. Weight-loss surgery is also called bariatric or metabolic surgery.

This type of surgery may help some people who have obesity and type 2 diabetes lose a large amount of weight and bring their blood glucose levels back to a healthy range. How long the improved response lasts can vary by patient, type of weight-loss surgery, and the amount of weight the person lost. Other factors include how long a person had diabetes and whether the person used insulin. Some people with type 2 diabetes may no longer need to use diabetes medicines after weight-loss surgery . 4

Researchers are studying whether weight-loss surgery can help control blood glucose levels in people with type 1 diabetes who have obesity. 5

Pancreatic islet transplantation

Pancreatic islet transplantation is an experimental treatment for people with type 1 diabetes who have trouble controlling their blood glucose levels. Pancreatic islets  are clusters of cells in the pancreas that make the hormone insulin. In type 1 diabetes, the body’s immune system attacks these cells. A pancreatic islet transplantation replaces destroyed islets with new islets from organ donors. The new islets make and release insulin. Because researchers are still studying pancreatic islet transplantation , the procedure is only available to people enrolled in research studies.

The NIDDK conducts and supports clinical trials in many diseases and conditions, including diabetes. The trials look to find new ways to prevent, detect, or treat disease and improve quality of life.

What are clinical trials for insulin, medicines, and other diabetes treatments?

Clinical trials—and other types of clinical studies —are part of medical research and involve people like you. When you volunteer to take part in a clinical study, you help health care professionals and researchers learn more about disease and improve health care for people in the future.

Find out if clinical trials are right for you .

Researchers are studying many aspects of diabetes medicines, including

• new types of insulin
• the most effective times to take diabetes medicines
• new types of monitoring devices and delivery systems

Watch a video of NIDDK Director Dr. Griffin P. Rodgers explaining the importance of participating in clinical trials.

What clinical trials for insulin, medicines, and other diabetes treatments are looking for participants?

You can view a filtered list of clinical studies on insulin, medicines, and other diabetes treatments covered in this health topic that are federally funded, open, and recruiting at www.ClinicalTrials.gov . You can expand or narrow the list to include clinical studies from industry, universities, and individuals; however, the National Institutes of Health does not review these studies and cannot ensure they are safe. Always talk with your health care provider before you participate in a clinical study.

This content is provided as a service of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health. NIDDK translates and disseminates research findings to increase knowledge and understanding about health and disease among patients, health professionals, and the public. Content produced by NIDDK is carefully reviewed by NIDDK scientists and other experts.

The NIDDK would like to thank Stuart A. Weinzimer, M.D., Yale University School of Medicine

• Open access
• Published: 27 May 2024

Postoperative pneumonia after femoral fracture surgery: an in-depth retrospective analysis

• Mohammad Hamdan   ORCID: orcid.org/0000-0003-2382-5585 1 ,
• Bassem I. Haddad   ORCID: orcid.org/0000-0001-7246-2384 1 ,
• Jamil Almohtasib   ORCID: orcid.org/0000-0002-5055-674X 2 ,
• Mira Eid   ORCID: orcid.org/0000-0002-6371-2174 2 ,
• Tasneem Jamal Al-Din   ORCID: orcid.org/0009-0004-6882-9066 2 ,
• Hashem A. Rayyan   ORCID: orcid.org/0009-0003-7211-7224 2 ,
• Ahmad M. Altantawi 2 ,
• Abdussalam S. Akaheal 1 &
• Mohammad Ali Alshrouf   ORCID: orcid.org/0000-0003-3806-2859 2  

BMC Musculoskeletal Disorders volume  25 , Article number:  413 ( 2024 ) Cite this article

243 Accesses

Metrics details

Femoral fractures significantly contribute to disability, predominantly in the elderly. Despite this, data on postoperative pneumonia following femoral fracture surgeries remains sparse. Our study sought to explore the incidence and impact of postoperative pneumonia on outcomes following such surgeries.

A retrospective study analyzed femoral fracture patients hospitalized from 2016 to 2022. We scrutinized postoperative outcomes, including pneumonia, hospital stay duration, intensive care unit (ICU) admissions, and in-hospital mortality. We established stringent diagnostic criteria for postoperative pneumonia, incorporating both clinical signs and radiological evidence, excluding patients with prior infections or those discharged within 24 h post-surgery. Statistical analyses involved Chi-square and t-tests, linear regression, and logestic regression using SPSS.

Out of 636 patients, 10.8% were diagnosed with postoperative pneumonia. The average age was 79.55 ± 8.57 years, with a male prevalence of 47.8%. Common comorbidities were hypertension (78.3%), diabetes (60.9%), and cardiovascular diseases (40.6%). Surgical interventions were categorized as intramedullary nailing (40.6%), partial hip replacement (37.7%), and dynamic hip screw (21.7%). Postoperative pneumonia was associated with older age (AOR = 1.053, 95% CI 1.020 to 1.087, p  = 0.002), ICU admission (AOR = 2.283, 95% CI 1.256 to 4.148, p  = 0.007), and longer length of hospital stay (AOR = 1.079, 95% CI 1.030 to 1.130, p  = 0.001). The presence of pneumonia was associated with a 2.621-day increase in hospitalization after adjusting for other variables ( p  < 0.001, 95% CI: 1.454 to 3.789).

This study accentuates the clinical significance of postoperative pneumonia in femoral fracture patients, with a noted incidence of 10.8%. A notable association with older age, prolonged hospital stays, and ICU admissions was observed, underscoring the necessity of addressing this complication to improve patient outcomes and healthcare resource allocation.

Peer Review reports

Introduction

Femoral fractures are recognized as a serious, debilitating problem worldwide, especially concerning the geriatric population. As this issue continues to rise, with an annual estimate of 1.6 million patients with a hip fracture hospitalized [ 1 ], the number of hip fracture surgeries simultaneously expands alongside their associated complications, like postoperative pneumonia. It has been estimated that the total annual incidence of geriatric hip fractures in the Middle East in general is between 60 and 150 per 100,000 [ 2 , 3 , 4 , 5 ]. There is a scarcity of research examining the incidence and complications of hip fractures among the Jordanian population. According to recent research in Jordan, it was estimated that the annual incidence of hip fracture patients above the age of 55 in 2021 was approximately 96 cases per 100,000 individuals [ 6 ].

Factors such as advanced age, anemia, diabetes, prior stroke, the number of comorbidities, an American society of anesthesiologists (ASA) score ≥ III, general anesthesia, and delay in surgery were positively correlated to acquiring pneumonia after surgery [ 7 , 8 ]. On the other hand, many elements, regardless of pneumonia, were found to affect the length-of-stay (LOS) following a hip fracture surgery. These included advanced age, higher ASA physical status scores, comorbid burden, with the addition of female gender, severe obesity with a body mass index (BMI) exceeding 40, the use of a cemented implant in the total hip replacement, previous hip fractures, acute renal failure, diabetes, cerebrovascular disease, smokers, and others [ 9 , 10 , 11 ]. Others were linked with death after these surgeries. For instance, longer LOS, age over 80, poor mobility prior to the surgery, inability to return to baseline mobility, the presence of 3 or more comorbidities, an ASA over III or IV, chest infection, and heart failure [ 10 , 12 , 13 ]. In the management of hip fractures, particularly among elderly patients, postoperative complications significantly influence outcomes and mortality rates [ 14 , 15 , 16 ]. Among these complications, pneumonia stands out as a critical risk factor. The incidence of pneumonia following surgery not only complicates recovery but also markedly increases the risk of mortality [ 14 , 16 , 17 ]. This relationship is particularly pronounced in geriatric patients, who may already present with compromised health status pre-surgery.

While many of the aforementioned factors overlap, very limited data is available regarding the complications associated with the development of postoperative pneumonia after femoral fracture surgeries. Some evidence suggests that postoperative pneumonia significantly increased the 30-day mortality to be 27–43%, prolonged the hospital stay by 56%, increased the rate of sepsis by about 10%, and increased the risk of readmission by eightfold [ 18 , 19 ].The exact relationship between the incidence of pneumonia and these numerous complications is not well understood, and there are variable trends in this regard, which calls for further investigation. Moreover, studies suggest that the 30-day mortality was higher in hip fracture patients with coronavirus disease (COVID-19) infection; however, vaccinated patients with COVID-19 infection had a comparable mortality risk to those without the virus, indicating that the illness was less severe [ 20 , 21 ].

The incidence and implications of postoperative pneumonia have been a worthy topic of discussion, and scholars have attempted to investigate the potential risk factors for postoperative pneumonia following surgically treated femoral fractures in elderly patients [ 7 , 18 , 19 , 22 , 23 ]. Due to the lack of such data in this population, this study aims to determine the incidence and effects of postoperative pneumonia after femoral fracture surgery on the length-of-stay in the hospital, as well as the mortality rate and factors associated with postoperative pneumonia in the hospital in the Jordanian population. We hypothesize that the effects of pneumonia on our sample would increase both factors involved. Therefore, the results emerging from this study are expected to help optimize the care provided to these patients and eventually improve their quality of life.

Methodology

Study design.

We conducted a retrospective cohort study using data that was prospectively collected from patients diagnosed with femur fractures who have undergone surgical treatment at Jordan University Hospital between the years 2016 and 2022. Prior to the study’s beginning, the protocol was evaluated and approved by the Jordan University Hospital ethics committee, and the appropriate institutional review board (IRB) approved the study proposal (approval number 101,202,315,854; 2/3/2023). The Code of Ethics of the World Medical Association (Declaration of Helsinki) was followed while conducting the study.

Study population

The research includes patients diagnosed with femoral fractures, confirmed through imaging, and admitted to the University of Jordan Hospital within the time frame of 2016 to 2022. There are no specific limitations regarding the time frame between the occurrence of the fracture and admission. Inclusion criteria cover patients who underwent femoral fracture surgery as confirmed through imaging (e.g., X-rays, computed tomography (CT) scans), patients admitted to the University of Jordan Hospital within the specified time frame of 2016 to 2022, and had complete medical records for analysis. Exclusion criteria involve patients discharged within 24 h of admission, to ensure that we could accurately capture cases of postoperative pneumonia, which typically do not manifest immediately after surgery, and patients who were diagnosed with other infectious diseases (such as respiratory infections and urinary tract infections). The diagnosis was based on the presence of any symptoms or signs and additional diagnostic tests that were necessary, and this was done to avoid any possible confounding effects and focus only on the incidence and effects of postoperative pneumonia related to the surgery.

Data collection

The data were systematically retrieved from electronic medical records at Jordan University Hospital. The data encompassed vital patient information, such as gender and age. A comprehensive evaluation of previous medical history was done to identify conditions like diabetes, hypertension, and malignancies, along with any other cardiovascular, pulmonary, renal, and neurological diseases. Pulmonary co-morbidity was based on the patient having one of the following: asthma, COPD, or pulmonary fibrosis. Factors related to surgery, including if the surgery was performed within 48 h, type of fracture (classified according to the International Classification of Diseases, Tenth Revision (ICD-10) codes). These included femur neck fractures (S72.0), pertrochanteric fracture (S72.1), intertrochanteric fractures (S72.14), subtrochanteric fractures (S72.2), and femoral shaft fractures (S72.3)), type of surgical procedure (dynamic hip screw (DHS), intramedullary nailing (IMN), partial hip replacement (PHR), proximal femoral nail antirotation (PFNA)), and type of anesthesia (general vs. spinal) were also scrutinized. The hemoglobin level upon admission was also collected as a standard procedure aimed at evaluating patients’ overall health status and identifying potential risks associated with surgery, such as anemia or other hematological conditions. Additionally, the study took into account post-operative outcomes by assessing the occurrence of post-operative pneumonia, length of hospital-stay, and in-hospital mortality. In our study, strict hospital guidelines were implemented to control COVID-19 infections among patients. These guidelines mandate that all patients undergo a COVID-19 polymerase chain reaction (PCR) test within 48 h prior to the operation, and positive cases were quarantined and the surgery delayed.

Diagnosis of pneumonia

Post-operative pneumonia was diagnosed by the respiratory team in the hospital, depending on the clinical findings of the patient and an examination of the chest X-ray findings after surgery. The assessment of chest X-ray involved an examination by experienced radiologists, with specific attention to the presence of infiltrates, consolidations, or other abnormalities indicative of pneumonia. In addition, the clinical assessment included a thorough examination, which included the evaluation of temperature, respiration rate, white blood cell count, chest physical examination, and other relevant indicators.

Statistical analysis

All collected data was cleaned, coded, and analyzed on SPSS version 27. Categorical variables (e.g., gender) were presented as frequencies n (%), while continuous variables (e.g., age) were presented as means ± standard deviations. Mean differences in responses and domain scores were examined using the independent t-test and Chi-square test. Femoral fracture patients were split according to their diagnosis of pneumonia; mean differences were examined using the independent t-test. Variables that showed univariate analysis with a p  < 0.1 were included in the logistic regression model in order to control for possible confounding factors for the predictors of post-operative pneumonia, which were summarized using adjusted odds ratio (AOR). A linear regression model was conducted to test the effect of post-operative pneumonia on the length of stay, adjusted for variables with p  < 0.1 in univariant analysis. All statistical tests are conducted with a 95% confidence interval and a 5% error margin. A p-value of less than 0.05 is considered statistically significant.

A total of 636 patients were included in our study between 2016 and 2022. Of which, 69 patients (10.8%) were treated for post-operative pneumonia. The mean age for these patients was 79.55 ± 8.57, with 47.8% being male. Hypertension (78.3%), diabetes (60.9%), and cardiovascular disease (40.6%) were the most prevalent co-morbidities. Table  1 demonstrates the patient characteristics and clinical information in relation to the diagnosis of pneumonia.

In a logistic regression to investigate the influence of postoperative pneumonia on variables with a p-value less than 0.1, the results revealed positive statistically significant predictors including older age, length of stay, and ICU admission. Table  2 showcases the outcomes of the regression model, examining the influence of pneumonia on various clinical parameters.

We used linear regression to analyze the predictors of length of hospital stay. Relevant variables with a p-value < 0.1 in univariate analysis were entered into the model. Our analysis revealed that post-operative pneumonia and ICU admission were positively associated with the length of hospital stay, indicating longer stays. Conversely, higher hemoglobin levels and surgery performed within 48 h of admission were negatively associated with the length of hospital stay, indicating shorter stays (Table  3 ). Figure  1 represents a boxplot overlaid on top of a violin plot, illustrating the influence of postoperative pneumonia on the length of hospital stay, stratified by age groups. It demonstrates a significant increase in the length of hospital stay for patients diagnosed with pneumonia within the age groups of 71–80 and + 81 years ( p  < 0.05).

Boxplot on top of the violin plot to demonstrate the impact of postoperative pneumonia on length of hospital stay with a subgroup analysis by age groups

Our retrospective study, which included 636 patients who had surgical procedures for femoral fractures from 2016 to 2022, provides insights into the prevalence and outcomes of postoperative pneumonia in this specific population. Significantly, 10.8% of patients were diagnosed with post-operative pneumonia. The participants had a mean age of 79.55 ± 8.57 and a balanced male-to-female ratio. Postoperative pneumonia was associated with older age, ICU admission, and a longer length of hospital stay. The presence of pneumonia was associated with a 2.621-day increase in hospitalization after adjusting for other variables. In addition, prolonged hospital stay was associated with surgery not being performed within 48 h of admission, lower hemoglobin levels upon admission, and ICU admission.

The effect of postoperative pneumonia (POP) on the duration of hospitalization following femoral fracture surgeries is a crucial aspect to consider when assessing patient care. Consistent with the existing literature, our study revealed that patients diagnosed with POP experience a longer hospital stay and are more likely to be admitted to the ICU [ 1 ]. This extended stay not only bears repercussions for individual patients but also exerts an effect on healthcare institutions by increasing the cost of care and utilizing scarce resources inefficiently. Therefore, it is essential to minimize the risk of POP by identifying high-risk patients and implementing strategies for the early detection and treatment of pneumonia. In a cohort study designed to evaluate a standardized POP prevention program, Kazaure et al. showcased a notable decrease of 43.6% in POP rates post-implementation [ 24 ]. Additionally, in a non-randomized, quasi-experimental study, Chang et al. highlighted the benefits of pulmonary rehabilitation in elderly patients with hip fractures, demonstrating a significantly lower incidence of POP and shorter hospital stay in patients receiving chest physiotherapy on the first post-surgery day in comparison to the control group [ 25 ]. Therefore, a multifaceted approach focused on identifying high-risk patients and adopting comprehensive preventive and management strategies emerges as a critical factor in mitigating the risk of POP and subsequently reducing the duration of hospitalization and ICU admissions.

On the other hand, the lack of a significant relationship between mortality and pneumonia in our population warrants further investigation and exploration, as it contradicts previous literature [ 19 ]. Jang et al. studied the effect of pneumonia on all-cause mortality after elderly hip fractures, which suggested an increase in mortality in pneumonia patients at 30 days to 1 year compared to non-pneumonia patients [ 26 ]. This could be attributed to the fact that the population used in their study is composed of patients over the age of 65, compared to the population in our study, which included patients from a wider range of age groups. The impact of COVID-19 pneumonia on overall mortality among hip fracture patients, which Fessler et al. studied, is another factor worth mentioning [ 27 ]. Several meta-analysis studies suggested a significant increase in mortality among patients with femoral or hip fractures who had a perioperative or concomitant COVID-19 infection [ 27 , 28 , 29 ]. In addition, in national research involving 3303 adults who underwent hip fracture surgery, the all-cause mortality for individuals who tested positive for COVID-19 was 27.0%, compared to 12.4% for those who tested negative for COVID-19 [ 30 ]. On the other hand, COVID-19 infection did not significantly modify 30-day and 6-month mortality, and in another study, they found no significant difference in 120-day mortality [ 31 , 32 ]. This information is critical for orthopedic surgeons to consider when managing patients with femoral fractures and concomitant COVID-19 infection. This could have an effect on our mortality results because our population includes patients from before and after the pandemic.

In our study, older age was a significant predictor of postoperative pneumonia in patients after femoral fracture. Similar to our study results, in a study including 3147 patients, they found a postoperative pneumonia rate of 5.8%, and they found age to be an independent risk factor for postoperative pneumonia [ 18 ].n In a multicenter retrospective study, they found that older age was associated with a higher risk for aspiration pneumonia in patients with hip fractures [ 33 ]. Moreover, low hemoglobin levels on admission have been linked to increased severity and adverse outcomes in various infectious diseases [ 34 , 35 ]. In the context of pneumonia, reduced hemoglobin levels may reflect compromised oxygen-carrying capacity, potentially exacerbating tissue hypoxia and impairing the immune responses that combat infection [ 35 ]. Furthermore, a previous study found that low hemoglobin levels upon admission were significantly associated with 6-month mortality in hip fracture patients [ 36 ]. In our study, lower hemoglobin levels were associated with prolonged hospital stays in the linear regression, but there was no significant difference in the hemoglobin level between the patients with postoperative pneumonia and those without.

The data in this paper ascertains that there are comorbidities more prevalent than others in those patients who developed postoperative pneumonia. These include hypertension, diabetes, and cardiovascular disease. However, only cardiovascular disease was significantly more prevalent in patients with prolonged LOS, regardless of the pneumonia diagnosis. While the specific comorbidities involved are not extensively discussed, a retrospective multi-center cohort study has found that broadly, the presence of preoperative comorbidities has been associated with a rise in LOS [ 7 , 10 ]. In addition, evidence suggests an increased prevalence of comorbidities coinciding with the initial incidence of hip fractures. A study by Yu Jiang et al., which involved patients undergoing surgical treatment for hip fractures, found hypertension as the most prevalent comorbidity at 52.0% (67.8% in our study), followed by 23.6% with type 2 diabetes (49.4% in our study), coronary heart disease (20.9%), stroke (18.7%), and arrhythmia (11.2%) (combined cardiovascular disease prevalence in our study was 29.1%) [ 37 ].

Previous studies have linked hypertension to respiratory infections, specifically pneumonia [ 38 ]. Chronic hypertension commonly coexists with endothelial dysfunction, immunological dysregulation, and altered inflammatory responses, suggesting a complicated interaction [ 39 , 40 ]. The higher prevalence (78.3%) of hypertension among patients with postoperative pneumonia in our study, warrants consideration. Also, hypertension has an established role in immune modulation and a potential impact on lung function [ 38 , 39 , 40 ]. Furthermore, similar to the previously mentioned effects of hypertension, diabetes also had several mechanisms that could increase their risk of infection, including increased altered immune cell function, bacterial proliferation, and changes in vascular permeability and endothelial cells, which was attributed to an increase in the incidence of postoperative pneumonia after an array of surgeries [ 41 ]. This element of immunosuppression may explain why diabetes was more prevalent (60.9%) in our cases of postoperative pneumonia. Nonetheless, its effects on increased LOS could be explained by an increase in other postoperative complications [ 42 ], which negatively impact surgical outcomes and require further interventions post-operatively. Moreover, it is well known that diabetes influences wound healing due to its deleterious effects on microcirculation and the metabolic pathway [ 43 ]. Cardiovascular diseases (CVD) were found to be a trigger for hemodynamic instability, which contributes to pulmonary congestion and edema that could result in an infection, possibly pneumonia [ 44 ]. Moreover, Lee et al. reported that for geriatric patients with femoral neck fractures undergoing hemiarthroplasty procedures, congestive heart failure doubled the chances of developing POP [ 45 ]. This further confirms our findings of increased POP and LOS in patients with cardiovascular diseases on univariate analysis. Preoperative cardiac evaluation guidelines set out by the American College of Cardiology/American Heart Association (ACC/AHA) categorize any orthopedic procedure, including femoral fracture repair, as “intermediate risk.” [ 46 ]. Specifically, heart failure has been previously found to increase LOS following hip fracture surgeries, which goes hand in hand with our findings [ 47 ].

A Danish study in 2019 confirmed our findings regarding postoperative pneumonia. It suggested that a delay of 12 h was associated with an increased risk of pneumonia in patients with no comorbidities, a delay of 24 h was associated with an increased risk of pneumonia in patients with a medium level of comorbidity, and a delay of 48 h was associated with an increased risk of reoperation due to infection in patients with a high level of comorbidity. In conclusion, a delay in surgery was associated with an increased risk of hospital-treated pneumonia and reoperations due to infection within 30 days of surgery [ 48 ]. Many articles have confirmed that a delay in surgery over 48 h is concurrent with worsening outcomes, hence increased LOS, reasoning that a delay in the performance of surgery is linked to major medical complications, minor medical complications, and pressure sores [ 49 , 50 ]. Furthermore, prior research involving polytrauma patients has demonstrated that early stabilization of femur fractures is linked to a reduced risk of acute respiratory distress syndrome and mortality [ 51 ]. Interestingly, a retrospective review conducted in 2018 revealed that increasing time to surgery was associated with longer postoperative lengths of stay but not with adverse outcomes of surgery [ 52 ].

The retrospective study investigating the impact of pneumonia on the length of hospital stay and mortality in elderly femoral fracture patients exhibits several notable strengths. The study addressed a clinically significant issue by investigating the impact of pneumonia, specifically in femoral fracture patients. In addition, understanding the interplay between these two conditions can inform healthcare strategies and improve patient care. Furthermore, a larger sample increases the likelihood of detecting true associations, strengthens the study’s external validity, and utilizes multivariate analysis controlled for potential confounding variables. However, certain limitations warrant consideration. The study’s retrospective design is inherently limited by its reliance on existing medical records, which may lack some critical information. Moreover, conducting the study at a single healthcare center may limit the generalizability of the findings. Also, failure to account for nosocomial cases could underestimate the true impact of hospital-acquired infections on the studied outcomes. Future studies could benefit from incorporating ASA grades and utilizing the CURB-65 scoring system, which could potentially enrich the analysis and provide deeper insights into the prognostic factors influencing postoperative outcomes.

In light of our findings, this study underscores the significant impact of postoperative pneumonia on the outcomes of patients undergoing femur fracture surgery. With a notable incidence of 10.8%, postoperative pneumonia was associated with older age, prolonged hospital stay, and intensive care unit (ICU) admissions, though it did not significantly affect mortality rates. In addition, prolonged hospital stay was associated with surgery not being performed within 48 h of admission, lower hemoglobin levels upon admission, and ICU admission. For clinicians, our study emphasizes the importance of early identification and management of risk factors for postoperative pneumonia. Implementing targeted interventions, such as preoperative optimization, timely surgical intervention, and enhanced postoperative care protocols, could mitigate the risk of developing pneumonia, improve overall outcomes, and lower the incidence of postoperative pneumonia in patients with femur fractures.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to the sake of patient privacy but are available from the corresponding author on reasonable request.

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Mohammad Hamdan, Bassem I. Haddad & Abdussalam S. Akaheal

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Jamil Almohtasib, Mira Eid, Tasneem Jamal Al-Din, Hashem A. Rayyan, Ahmad M. Altantawi & Mohammad Ali Alshrouf

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Conceptualization, MH and BH; methodology, TA and AMA; validation, MH, BIH and HAR; formal analysis, MAA and JA; investigation, JA, HAR, ME , AMA, and TA; resources, JA and MAA; data curation, JA, AMA, ME, TA, HAR, and MAA; writing—original draft preparation, MAA, and JA, HAR, ME , AMA, and TA; writing—review and editing, MAA, MH, BH, JA, HAR, ME , AMA, TA, and MAA; visualization, MAA and JA; supervision, MH, BH and MAA; project administration, MH. All authors made substantial contributions to conception and design and have read and agreed to the published version of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Mohammad Ali Alshrouf .

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Hamdan, M., Haddad, B.I., Almohtasib, J. et al. Postoperative pneumonia after femoral fracture surgery: an in-depth retrospective analysis. BMC Musculoskelet Disord 25 , 413 (2024). https://doi.org/10.1186/s12891-024-07529-4

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Received : 30 December 2023

Accepted : 17 May 2024

Published : 27 May 2024

DOI : https://doi.org/10.1186/s12891-024-07529-4

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