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Which nondrug alternatives can help with insomnia?
Cognitive behavioral therapy (CBT) interventions—particularly stimulus control and sleep hygiene—are well-validated, effective treatments for chronic insomnia that are equivalent or superior to pharmacological interventions (strength of recommendation: A, based on systematic reviews). The long-term efficacy of CBT interventions, and their successful implementation by primary care physicians (as compared with behavioral science providers), is unclear.
Can I provide these interventions without a referral?
John D Hallgren, Lt Col, USAF, MC
Uniformed Services University of the Health Sciences, RAF Menwith Hill, UK
A large proportion of people in my patient population are shift workers, so chronic insomnia plays a large role in my daily workload, both directly and indirectly. This summary tells me that I have a proven and equally efficacious alternative to drugs for these sufferers—which is great.
However, I was disappointed to see that none of the CBT interventions were performed by family physicians in the office. So the good news is that I have a nondrug intervention for insomnia; the bad news is I don’t know if it’s something I can provide without a referral. Maybe it’s time for some practice-based research to see if that is possible.
Evidence summary
Approximately 10% to 15% of adults complain of chronic insomnia, best defined as difficulty initiating or maintaining sleep 3 or more nights per week for 6 months or longer, with secondary impairments in daytime functioning, including fatigue and disturbed mood.1-3
Behavioral and psychological treatments have emerged as increasingly popular adjunctive interventions to pharmacotherapy and as independent interventions for chronic insomnia. No evidence exists that behavioral treatments have adverse effects.1
Sleep hygiene, relaxation training, and cognitive therapy improve sleep
CBT interventions are based on the notion that distorted thoughts about sleep and learned behavior patterns hyperarouse the central nervous system and deregulate sleep cycles, resulting in chronic insomnia.4 CBT interventions combine empirically tested behavioral, cognitive, and educational procedures to alter faulty beliefs and attitudes, modify sleep habits, and regulate sleep-wake schedules.3
These interventions include stimulus control, sleep hygiene, sleep restriction, relaxation training, and cognitive therapy.5 These methods can be used separately; however, they are increasingly being used together to treat the complexities of individual patients.5
Five recent high-quality randomized control trials (RCTs) confirmed findings from earlier RCTs that CBT methods improve sleep.5 Compared with those given a placebo or placed on a waiting list, CBT-treated patients in these RCTs reported clinically significant improvements in sleep onset latency, sleep efficiency, time awake after sleep onset, and total sleep time. In one RCT, 64% of CBT patients had improvements in sleep efficiency and time awake after sleep onset, compared with 8% who improved with a placebo intervention (number needed to treat [NNT]=1.8).5 Further, sleep onset latency for primary care patients with chronic insomnia was decreased from 61 to 28 minutes, compared with 74 to 70 minutes for a waiting-list group.5 The maintenance of sleep gains from CBT beyond 1 year is unknown since no published RCT clinical trials to date have lasted longer than 12 months.1
An important related meta-analysis of 21 studies validated behavior therapy, and revealed CBT reduced sleep onset latency by an additional 8.8 minutes over medication (95% confidence interval, 0.17–1.04 minutes).6 Although not superior on other outcomes, behavior therapy produced similar short-term results to pharmacotherapy across all other sleep measures, without attendant medication side effects.
Stimulus control is the most effective CBT intervention
A recent systematic review with meta-analysis of 37 clinical investigations determined that stimulus control was the most effective CBT intervention.3 Stimulus control consists of 5 basic instructions (TABLE) designed to help the patient reassociate sleep stimuli (ie, bed/bedroom) with falling asleep and establishing consistent sleep-wake schedules. These 5 instructions are frequently used in combination with CBT sleep hygiene techniques (TABLE) and can be easily integrated into the office setting.3,4
Among the CBT techniques, stimulus control and sleep hygiene are the least time-consuming and may be more easily applied in the primary care setting; however, minimal research has been done into the specific incorporation of CBT into primary care settings.
Researchers conducting a single-blind randomized group study in a Veterans Affairs primary care clinic concluded that an abbreviated CBT approach with two 25-minute sessions effectively improved participant sleep onset latency, and time awake after sleep onset.7 Researchers reviewed participants’ sleep logs and a behavioral health provider offered patients a condensed education on sleep hygiene, stimulus control, and sleep restrictions strategies. The study was limited because of small sample size (<25). Generalizability to practice is restricted because sessions were conducted by a behavioral health provider, not a family physician.
TABLE
Patient needs a good night’s sleep? Offer this advice
| STIMULUS CONTROL INSTRUCTIONS3 |
|
| SLEEP HYGIENE INSTRUCTIONS4 |
|
Recommendations from others
The Agency for Healthcare Research and Quality recommends CBT as an effective treatment in the management of chronic .8 It also recommends that further large-scale RCTs be conducted to establish CBT’s effectiveness across subsets of the population of individuals with chronic (ie, gender, age, shift workers, and those with psychiatric illnesses).
The American Psychological Association (APA) recommends CBT as the “treatment of choice” for chronic , with 70% to 80% of patients showing a treatment response.9
Acknowledgments
The opinions and assertions contained herein are the private views of the authors and not to be construed as official, or as reflecting the views of the US Air Force Medical Service or the US Air Force at large.
1. NIH State-of-the-Science Conference Statement on Manifestations and Management of Chronic Insomnia in Adults. NIH Consensus Science Statement. 2005; 22(2). Available at: consensus.nih.gov/2005/2005InsomniaSOS026main.htm. Accessed on September 4, 2007.
2. Ohayon M. Epidemiology of insomnia: What we know and what we still need to learn. Sleep Med Rev 2002;6:97-111.
3. Morin CM. Cognitive-behavioral approaches to the treatment of insomnia. J Clin Psychiatry 2004;65 Suppl 16:33-40.
4. Smith MT, Neubauer DN. Cognitive behavioral therapy for chronic insomnia. Clinical Cornerstone 2003;5:1-9.
5. Morin CM, Bootzin RR, Buysse DJ, Edinger JD, Espie CA. Psychological and behavioral treatment of insomnia: Update of the recent evidence (1998–2004). Sleep 2006;29:1398-1413.
6. Smith MT, Perlis ML, Park A, et al. Comparative meta-analysis of pharmacotherapy and behavior therapy for persistent insomnia. Am J Psychiatry 2002;159:5-11.
7. Edinger JD, Sampson WS. A primary care “friendly” cognitive behavioral insomnia therapy. Sleep 2003;26:177-182.
8. Buscemi N, Vandermeer B, Friesen C, et al. Manifestations of chronic insomnia in adults. Evidence report/technology assessment No. 125. (Prepared by the University of Alberta Evidence-based Practice Center, under Contract N. C400000021.) AHRQ Publication No. 05-E021-1. Rockville, Md: Agency for Healthcare Research and Quality. June 2005. Available at: www.ahrq.gov/downloads/pub/evidence/pdf/insomnia/insomnia.pdf. Accessed on September 4, 2007.
9. American Psychological Association Web site. Getting a good night’s sleep with the help of psychology. Available at: www.psychologymatters.org/insomnia.html. Accessed on September 4, 2007.
Cognitive behavioral therapy (CBT) interventions—particularly stimulus control and sleep hygiene—are well-validated, effective treatments for chronic insomnia that are equivalent or superior to pharmacological interventions (strength of recommendation: A, based on systematic reviews). The long-term efficacy of CBT interventions, and their successful implementation by primary care physicians (as compared with behavioral science providers), is unclear.
Can I provide these interventions without a referral?
John D Hallgren, Lt Col, USAF, MC
Uniformed Services University of the Health Sciences, RAF Menwith Hill, UK
A large proportion of people in my patient population are shift workers, so chronic insomnia plays a large role in my daily workload, both directly and indirectly. This summary tells me that I have a proven and equally efficacious alternative to drugs for these sufferers—which is great.
However, I was disappointed to see that none of the CBT interventions were performed by family physicians in the office. So the good news is that I have a nondrug intervention for insomnia; the bad news is I don’t know if it’s something I can provide without a referral. Maybe it’s time for some practice-based research to see if that is possible.
Evidence summary
Approximately 10% to 15% of adults complain of chronic insomnia, best defined as difficulty initiating or maintaining sleep 3 or more nights per week for 6 months or longer, with secondary impairments in daytime functioning, including fatigue and disturbed mood.1-3
Behavioral and psychological treatments have emerged as increasingly popular adjunctive interventions to pharmacotherapy and as independent interventions for chronic insomnia. No evidence exists that behavioral treatments have adverse effects.1
Sleep hygiene, relaxation training, and cognitive therapy improve sleep
CBT interventions are based on the notion that distorted thoughts about sleep and learned behavior patterns hyperarouse the central nervous system and deregulate sleep cycles, resulting in chronic insomnia.4 CBT interventions combine empirically tested behavioral, cognitive, and educational procedures to alter faulty beliefs and attitudes, modify sleep habits, and regulate sleep-wake schedules.3
These interventions include stimulus control, sleep hygiene, sleep restriction, relaxation training, and cognitive therapy.5 These methods can be used separately; however, they are increasingly being used together to treat the complexities of individual patients.5
Five recent high-quality randomized control trials (RCTs) confirmed findings from earlier RCTs that CBT methods improve sleep.5 Compared with those given a placebo or placed on a waiting list, CBT-treated patients in these RCTs reported clinically significant improvements in sleep onset latency, sleep efficiency, time awake after sleep onset, and total sleep time. In one RCT, 64% of CBT patients had improvements in sleep efficiency and time awake after sleep onset, compared with 8% who improved with a placebo intervention (number needed to treat [NNT]=1.8).5 Further, sleep onset latency for primary care patients with chronic insomnia was decreased from 61 to 28 minutes, compared with 74 to 70 minutes for a waiting-list group.5 The maintenance of sleep gains from CBT beyond 1 year is unknown since no published RCT clinical trials to date have lasted longer than 12 months.1
An important related meta-analysis of 21 studies validated behavior therapy, and revealed CBT reduced sleep onset latency by an additional 8.8 minutes over medication (95% confidence interval, 0.17–1.04 minutes).6 Although not superior on other outcomes, behavior therapy produced similar short-term results to pharmacotherapy across all other sleep measures, without attendant medication side effects.
Stimulus control is the most effective CBT intervention
A recent systematic review with meta-analysis of 37 clinical investigations determined that stimulus control was the most effective CBT intervention.3 Stimulus control consists of 5 basic instructions (TABLE) designed to help the patient reassociate sleep stimuli (ie, bed/bedroom) with falling asleep and establishing consistent sleep-wake schedules. These 5 instructions are frequently used in combination with CBT sleep hygiene techniques (TABLE) and can be easily integrated into the office setting.3,4
Among the CBT techniques, stimulus control and sleep hygiene are the least time-consuming and may be more easily applied in the primary care setting; however, minimal research has been done into the specific incorporation of CBT into primary care settings.
Researchers conducting a single-blind randomized group study in a Veterans Affairs primary care clinic concluded that an abbreviated CBT approach with two 25-minute sessions effectively improved participant sleep onset latency, and time awake after sleep onset.7 Researchers reviewed participants’ sleep logs and a behavioral health provider offered patients a condensed education on sleep hygiene, stimulus control, and sleep restrictions strategies. The study was limited because of small sample size (<25). Generalizability to practice is restricted because sessions were conducted by a behavioral health provider, not a family physician.
TABLE
Patient needs a good night’s sleep? Offer this advice
| STIMULUS CONTROL INSTRUCTIONS3 |
|
| SLEEP HYGIENE INSTRUCTIONS4 |
|
Recommendations from others
The Agency for Healthcare Research and Quality recommends CBT as an effective treatment in the management of chronic .8 It also recommends that further large-scale RCTs be conducted to establish CBT’s effectiveness across subsets of the population of individuals with chronic (ie, gender, age, shift workers, and those with psychiatric illnesses).
The American Psychological Association (APA) recommends CBT as the “treatment of choice” for chronic , with 70% to 80% of patients showing a treatment response.9
Acknowledgments
The opinions and assertions contained herein are the private views of the authors and not to be construed as official, or as reflecting the views of the US Air Force Medical Service or the US Air Force at large.
Cognitive behavioral therapy (CBT) interventions—particularly stimulus control and sleep hygiene—are well-validated, effective treatments for chronic insomnia that are equivalent or superior to pharmacological interventions (strength of recommendation: A, based on systematic reviews). The long-term efficacy of CBT interventions, and their successful implementation by primary care physicians (as compared with behavioral science providers), is unclear.
Can I provide these interventions without a referral?
John D Hallgren, Lt Col, USAF, MC
Uniformed Services University of the Health Sciences, RAF Menwith Hill, UK
A large proportion of people in my patient population are shift workers, so chronic insomnia plays a large role in my daily workload, both directly and indirectly. This summary tells me that I have a proven and equally efficacious alternative to drugs for these sufferers—which is great.
However, I was disappointed to see that none of the CBT interventions were performed by family physicians in the office. So the good news is that I have a nondrug intervention for insomnia; the bad news is I don’t know if it’s something I can provide without a referral. Maybe it’s time for some practice-based research to see if that is possible.
Evidence summary
Approximately 10% to 15% of adults complain of chronic insomnia, best defined as difficulty initiating or maintaining sleep 3 or more nights per week for 6 months or longer, with secondary impairments in daytime functioning, including fatigue and disturbed mood.1-3
Behavioral and psychological treatments have emerged as increasingly popular adjunctive interventions to pharmacotherapy and as independent interventions for chronic insomnia. No evidence exists that behavioral treatments have adverse effects.1
Sleep hygiene, relaxation training, and cognitive therapy improve sleep
CBT interventions are based on the notion that distorted thoughts about sleep and learned behavior patterns hyperarouse the central nervous system and deregulate sleep cycles, resulting in chronic insomnia.4 CBT interventions combine empirically tested behavioral, cognitive, and educational procedures to alter faulty beliefs and attitudes, modify sleep habits, and regulate sleep-wake schedules.3
These interventions include stimulus control, sleep hygiene, sleep restriction, relaxation training, and cognitive therapy.5 These methods can be used separately; however, they are increasingly being used together to treat the complexities of individual patients.5
Five recent high-quality randomized control trials (RCTs) confirmed findings from earlier RCTs that CBT methods improve sleep.5 Compared with those given a placebo or placed on a waiting list, CBT-treated patients in these RCTs reported clinically significant improvements in sleep onset latency, sleep efficiency, time awake after sleep onset, and total sleep time. In one RCT, 64% of CBT patients had improvements in sleep efficiency and time awake after sleep onset, compared with 8% who improved with a placebo intervention (number needed to treat [NNT]=1.8).5 Further, sleep onset latency for primary care patients with chronic insomnia was decreased from 61 to 28 minutes, compared with 74 to 70 minutes for a waiting-list group.5 The maintenance of sleep gains from CBT beyond 1 year is unknown since no published RCT clinical trials to date have lasted longer than 12 months.1
An important related meta-analysis of 21 studies validated behavior therapy, and revealed CBT reduced sleep onset latency by an additional 8.8 minutes over medication (95% confidence interval, 0.17–1.04 minutes).6 Although not superior on other outcomes, behavior therapy produced similar short-term results to pharmacotherapy across all other sleep measures, without attendant medication side effects.
Stimulus control is the most effective CBT intervention
A recent systematic review with meta-analysis of 37 clinical investigations determined that stimulus control was the most effective CBT intervention.3 Stimulus control consists of 5 basic instructions (TABLE) designed to help the patient reassociate sleep stimuli (ie, bed/bedroom) with falling asleep and establishing consistent sleep-wake schedules. These 5 instructions are frequently used in combination with CBT sleep hygiene techniques (TABLE) and can be easily integrated into the office setting.3,4
Among the CBT techniques, stimulus control and sleep hygiene are the least time-consuming and may be more easily applied in the primary care setting; however, minimal research has been done into the specific incorporation of CBT into primary care settings.
Researchers conducting a single-blind randomized group study in a Veterans Affairs primary care clinic concluded that an abbreviated CBT approach with two 25-minute sessions effectively improved participant sleep onset latency, and time awake after sleep onset.7 Researchers reviewed participants’ sleep logs and a behavioral health provider offered patients a condensed education on sleep hygiene, stimulus control, and sleep restrictions strategies. The study was limited because of small sample size (<25). Generalizability to practice is restricted because sessions were conducted by a behavioral health provider, not a family physician.
TABLE
Patient needs a good night’s sleep? Offer this advice
| STIMULUS CONTROL INSTRUCTIONS3 |
|
| SLEEP HYGIENE INSTRUCTIONS4 |
|
Recommendations from others
The Agency for Healthcare Research and Quality recommends CBT as an effective treatment in the management of chronic .8 It also recommends that further large-scale RCTs be conducted to establish CBT’s effectiveness across subsets of the population of individuals with chronic (ie, gender, age, shift workers, and those with psychiatric illnesses).
The American Psychological Association (APA) recommends CBT as the “treatment of choice” for chronic , with 70% to 80% of patients showing a treatment response.9
Acknowledgments
The opinions and assertions contained herein are the private views of the authors and not to be construed as official, or as reflecting the views of the US Air Force Medical Service or the US Air Force at large.
1. NIH State-of-the-Science Conference Statement on Manifestations and Management of Chronic Insomnia in Adults. NIH Consensus Science Statement. 2005; 22(2). Available at: consensus.nih.gov/2005/2005InsomniaSOS026main.htm. Accessed on September 4, 2007.
2. Ohayon M. Epidemiology of insomnia: What we know and what we still need to learn. Sleep Med Rev 2002;6:97-111.
3. Morin CM. Cognitive-behavioral approaches to the treatment of insomnia. J Clin Psychiatry 2004;65 Suppl 16:33-40.
4. Smith MT, Neubauer DN. Cognitive behavioral therapy for chronic insomnia. Clinical Cornerstone 2003;5:1-9.
5. Morin CM, Bootzin RR, Buysse DJ, Edinger JD, Espie CA. Psychological and behavioral treatment of insomnia: Update of the recent evidence (1998–2004). Sleep 2006;29:1398-1413.
6. Smith MT, Perlis ML, Park A, et al. Comparative meta-analysis of pharmacotherapy and behavior therapy for persistent insomnia. Am J Psychiatry 2002;159:5-11.
7. Edinger JD, Sampson WS. A primary care “friendly” cognitive behavioral insomnia therapy. Sleep 2003;26:177-182.
8. Buscemi N, Vandermeer B, Friesen C, et al. Manifestations of chronic insomnia in adults. Evidence report/technology assessment No. 125. (Prepared by the University of Alberta Evidence-based Practice Center, under Contract N. C400000021.) AHRQ Publication No. 05-E021-1. Rockville, Md: Agency for Healthcare Research and Quality. June 2005. Available at: www.ahrq.gov/downloads/pub/evidence/pdf/insomnia/insomnia.pdf. Accessed on September 4, 2007.
9. American Psychological Association Web site. Getting a good night’s sleep with the help of psychology. Available at: www.psychologymatters.org/insomnia.html. Accessed on September 4, 2007.
1. NIH State-of-the-Science Conference Statement on Manifestations and Management of Chronic Insomnia in Adults. NIH Consensus Science Statement. 2005; 22(2). Available at: consensus.nih.gov/2005/2005InsomniaSOS026main.htm. Accessed on September 4, 2007.
2. Ohayon M. Epidemiology of insomnia: What we know and what we still need to learn. Sleep Med Rev 2002;6:97-111.
3. Morin CM. Cognitive-behavioral approaches to the treatment of insomnia. J Clin Psychiatry 2004;65 Suppl 16:33-40.
4. Smith MT, Neubauer DN. Cognitive behavioral therapy for chronic insomnia. Clinical Cornerstone 2003;5:1-9.
5. Morin CM, Bootzin RR, Buysse DJ, Edinger JD, Espie CA. Psychological and behavioral treatment of insomnia: Update of the recent evidence (1998–2004). Sleep 2006;29:1398-1413.
6. Smith MT, Perlis ML, Park A, et al. Comparative meta-analysis of pharmacotherapy and behavior therapy for persistent insomnia. Am J Psychiatry 2002;159:5-11.
7. Edinger JD, Sampson WS. A primary care “friendly” cognitive behavioral insomnia therapy. Sleep 2003;26:177-182.
8. Buscemi N, Vandermeer B, Friesen C, et al. Manifestations of chronic insomnia in adults. Evidence report/technology assessment No. 125. (Prepared by the University of Alberta Evidence-based Practice Center, under Contract N. C400000021.) AHRQ Publication No. 05-E021-1. Rockville, Md: Agency for Healthcare Research and Quality. June 2005. Available at: www.ahrq.gov/downloads/pub/evidence/pdf/insomnia/insomnia.pdf. Accessed on September 4, 2007.
9. American Psychological Association Web site. Getting a good night’s sleep with the help of psychology. Available at: www.psychologymatters.org/insomnia.html. Accessed on September 4, 2007.
Evidence-based answers from the Family Physicians Inquiries Network
Should we recommend universal neonatal hearing screening?
Universal neonatal hearing screening leads to both earlier detection and earlier treatment of infants with hearing loss (strength of recommendation [SOR]: A, based on a systematic review). Available evidence suggests early identification and intervention may improve language outcomes (SOR: C, based on retrospective cohort studies).
Despite lack of evidence, early intervention could aid future language skills
Despite the lack of hard outcomes data to support neonatal hearing screening, it seems reasonable that early intervention will aid future language skills. Hopefully, future evidence will support the notion that early treatment leads to tangible school performance improvement. For most, however, the decision to universally screen neonates will be guided by state law rather than clinical evidence alone; 38 states currently have mandated screening programs with legislation pending in others.
Evidence summary
In the United States, approximately 5000 infants with moderate-to-profound hearing loss are born annually.1 Affected children graduate high school averaging 4th-grade academic performance skills.2 Efforts to reduce the impact on these children have focused on early diagnosis and treatment.
A systematic review gathered studies comparing universal hearing screening with selective screening.1 Most included studies used a 2-stage universal screening protocol. Infants who failed initial testing were retested within 12 weeks. Testing methods included otoacoustic emissions (OAE) and auditory brainstem response (ABR). Infants who failed the second test were referred for audiological evaluation. Using these data, a hypothetical model was created, which found that 1441 newborns would need to be screened to diagnose 1 additional case of moderate-to-profound permanent hearing loss before 10 months of age (at cost of 200 extra referrals for false-positives). Sensitivity and specificity of the hypothetical model’s 2-stage screening was 85% and 97%, respectively. The estimated positive predictive value was 6.7%.1,3
Individually, OAE and ABR accurately diagnose neonatal hearing loss. One multicenter cohort of 2995 infants measured test performance of OAE and ABR against the gold standard (visual reinforcement audiometry performed at 8–12 months).4 The authors used a receiver operating characteristics (ROC) curve to plot speech awareness thresholds for both tests. When middle-ear pathology and progressive hearing loss were excluded, the area under the ROC curves for ABR and OAE were 0.91 and 0.94, respectively, indication that both tests had excellent test accuracy (a perfect test would have an area under the curve of 1.0).
Strategies based on selective screening of high-risk infants fails to identify permanent hearing loss in many affected infants. In a cohort study of more than 10,000 infants, only 43% of infants with permanent hearing loss were identified with selective versus universal screening. Most affected infants would have been missed using risk-based criteria.5
Limited evidence suggests that early identification of infants with permanent hearing loss improves language skills. In a retrospective cohort study of 150 infants examining language outcomes, participants were grouped according to age at identification of hearing loss.6 All participants received comprehensive in-home language intervention services plus amplification devices.
Of the 85 children with normal cognitive ability, the mean receptive and expressive language quotients at 13 to 36 months were higher in the early-identified group vs the late-identified group (receptive language quotients, 79.6 vs 64.6, P<.001; expressive language quotients, 78.3 vs 63.1, P<.001). Total language quotient was also higher in the early group (language quotients, 79 vs 64; P<.001).
The conclusions were limited by multiple factors: retrospective study design, cohort selection drawn from different hospitals during different time periods, unblinded participant selection, and unblended outcome assessments. Other published studies have inconclusive outcome data. The Cochrane Collaboration published a systematic review in which no studies were found that fulfilled the inclusion criteria to evaluate the effectiveness of universal hearing screening.7
Recommendations from others
The Joint Committee on Infant Hearing recommended universal neonatal hearing screening during hospital birth admission in their Year 2000 Position Statement.8 For infants whose hearing is impaired on re-screening, the committee recommends audiology referral and medical evaluation to rule out associated conditions before age 3 months. They further recommend interventional services begin before age 6 months for infants with confirmed hearing loss.
The US Preventive Services Task Force does not recommend for or against universal hearing screening, citing insufficient outcomes data.9
1. Thompson DC, McPhillips H, Davis RL, Lieu TL, Homer CJ, Helfand M. Universal newborn hearing screening, summary of evidence. JAMA 2001;256:200-010.
2. Holt JA. Stanford Achievement Test—8th edition: reading comprehension subgroup results. Am Ann Deaf 1993;138:17-75.
3. Controlled trial of universal neonatal screening for early identification of permanent childhood hearing impairment. Wessex Universal Neonatal Hearing Screening Trial Group. Lancet 1998;352:195-964.
4. Norton SJ, Gorga MP, Widen SJ, et al. Identification of neonatal hearing impairment: evaluation of transient evoked otoacoustic emission, distortion product otoacoustic emission, and auditory brainstem response test performance. Ear Hear 2000;21:50-28.
5. Watkin PM, Baldwin M, McEnery G. Neonatal at risk screening and the identification of deafness. Arch Dis Child 1991;66(10 Spec No):113-135.
6. Yoshinaga-Itano C, Sedey AL, Coulter DK, Mehl AL. Language of early-and later-identified children with hearing loss. Pediatrics 1998;102:116-171.
7. Puig T, Municio A, Medà C. Universal neonatal hearing screening versus selective screening as part of the management of childhood deafness. Cochrane Database Syst Rev 2005;(2):CD003731.-
8. Joint Committee on Infant Hearing, American Academy of Audiology, American Academy of Pediatrics, American Speech-Language-Hearing Association, Directors of Speech and Hearing Programs in State Health and Welfare Agencies. Year 2000 position statement: Principles and guidelines for early hearing detection and intervention programs. Pediatrics 2000;106:79-17.
9. US Preventive Services Task Force. Newborn Hearing Screening: Recommendations and Rationale. October 2001. Agency for Healthcare Research and Quality, Rockville, Md. Available at: www.ahrq.gov/clinic/3rduspstf/newbornscreen/newhearrr.htm. Accessed on July 6, 2005.
Universal neonatal hearing screening leads to both earlier detection and earlier treatment of infants with hearing loss (strength of recommendation [SOR]: A, based on a systematic review). Available evidence suggests early identification and intervention may improve language outcomes (SOR: C, based on retrospective cohort studies).
Despite lack of evidence, early intervention could aid future language skills
Despite the lack of hard outcomes data to support neonatal hearing screening, it seems reasonable that early intervention will aid future language skills. Hopefully, future evidence will support the notion that early treatment leads to tangible school performance improvement. For most, however, the decision to universally screen neonates will be guided by state law rather than clinical evidence alone; 38 states currently have mandated screening programs with legislation pending in others.
Evidence summary
In the United States, approximately 5000 infants with moderate-to-profound hearing loss are born annually.1 Affected children graduate high school averaging 4th-grade academic performance skills.2 Efforts to reduce the impact on these children have focused on early diagnosis and treatment.
A systematic review gathered studies comparing universal hearing screening with selective screening.1 Most included studies used a 2-stage universal screening protocol. Infants who failed initial testing were retested within 12 weeks. Testing methods included otoacoustic emissions (OAE) and auditory brainstem response (ABR). Infants who failed the second test were referred for audiological evaluation. Using these data, a hypothetical model was created, which found that 1441 newborns would need to be screened to diagnose 1 additional case of moderate-to-profound permanent hearing loss before 10 months of age (at cost of 200 extra referrals for false-positives). Sensitivity and specificity of the hypothetical model’s 2-stage screening was 85% and 97%, respectively. The estimated positive predictive value was 6.7%.1,3
Individually, OAE and ABR accurately diagnose neonatal hearing loss. One multicenter cohort of 2995 infants measured test performance of OAE and ABR against the gold standard (visual reinforcement audiometry performed at 8–12 months).4 The authors used a receiver operating characteristics (ROC) curve to plot speech awareness thresholds for both tests. When middle-ear pathology and progressive hearing loss were excluded, the area under the ROC curves for ABR and OAE were 0.91 and 0.94, respectively, indication that both tests had excellent test accuracy (a perfect test would have an area under the curve of 1.0).
Strategies based on selective screening of high-risk infants fails to identify permanent hearing loss in many affected infants. In a cohort study of more than 10,000 infants, only 43% of infants with permanent hearing loss were identified with selective versus universal screening. Most affected infants would have been missed using risk-based criteria.5
Limited evidence suggests that early identification of infants with permanent hearing loss improves language skills. In a retrospective cohort study of 150 infants examining language outcomes, participants were grouped according to age at identification of hearing loss.6 All participants received comprehensive in-home language intervention services plus amplification devices.
Of the 85 children with normal cognitive ability, the mean receptive and expressive language quotients at 13 to 36 months were higher in the early-identified group vs the late-identified group (receptive language quotients, 79.6 vs 64.6, P<.001; expressive language quotients, 78.3 vs 63.1, P<.001). Total language quotient was also higher in the early group (language quotients, 79 vs 64; P<.001).
The conclusions were limited by multiple factors: retrospective study design, cohort selection drawn from different hospitals during different time periods, unblinded participant selection, and unblended outcome assessments. Other published studies have inconclusive outcome data. The Cochrane Collaboration published a systematic review in which no studies were found that fulfilled the inclusion criteria to evaluate the effectiveness of universal hearing screening.7
Recommendations from others
The Joint Committee on Infant Hearing recommended universal neonatal hearing screening during hospital birth admission in their Year 2000 Position Statement.8 For infants whose hearing is impaired on re-screening, the committee recommends audiology referral and medical evaluation to rule out associated conditions before age 3 months. They further recommend interventional services begin before age 6 months for infants with confirmed hearing loss.
The US Preventive Services Task Force does not recommend for or against universal hearing screening, citing insufficient outcomes data.9
Universal neonatal hearing screening leads to both earlier detection and earlier treatment of infants with hearing loss (strength of recommendation [SOR]: A, based on a systematic review). Available evidence suggests early identification and intervention may improve language outcomes (SOR: C, based on retrospective cohort studies).
Despite lack of evidence, early intervention could aid future language skills
Despite the lack of hard outcomes data to support neonatal hearing screening, it seems reasonable that early intervention will aid future language skills. Hopefully, future evidence will support the notion that early treatment leads to tangible school performance improvement. For most, however, the decision to universally screen neonates will be guided by state law rather than clinical evidence alone; 38 states currently have mandated screening programs with legislation pending in others.
Evidence summary
In the United States, approximately 5000 infants with moderate-to-profound hearing loss are born annually.1 Affected children graduate high school averaging 4th-grade academic performance skills.2 Efforts to reduce the impact on these children have focused on early diagnosis and treatment.
A systematic review gathered studies comparing universal hearing screening with selective screening.1 Most included studies used a 2-stage universal screening protocol. Infants who failed initial testing were retested within 12 weeks. Testing methods included otoacoustic emissions (OAE) and auditory brainstem response (ABR). Infants who failed the second test were referred for audiological evaluation. Using these data, a hypothetical model was created, which found that 1441 newborns would need to be screened to diagnose 1 additional case of moderate-to-profound permanent hearing loss before 10 months of age (at cost of 200 extra referrals for false-positives). Sensitivity and specificity of the hypothetical model’s 2-stage screening was 85% and 97%, respectively. The estimated positive predictive value was 6.7%.1,3
Individually, OAE and ABR accurately diagnose neonatal hearing loss. One multicenter cohort of 2995 infants measured test performance of OAE and ABR against the gold standard (visual reinforcement audiometry performed at 8–12 months).4 The authors used a receiver operating characteristics (ROC) curve to plot speech awareness thresholds for both tests. When middle-ear pathology and progressive hearing loss were excluded, the area under the ROC curves for ABR and OAE were 0.91 and 0.94, respectively, indication that both tests had excellent test accuracy (a perfect test would have an area under the curve of 1.0).
Strategies based on selective screening of high-risk infants fails to identify permanent hearing loss in many affected infants. In a cohort study of more than 10,000 infants, only 43% of infants with permanent hearing loss were identified with selective versus universal screening. Most affected infants would have been missed using risk-based criteria.5
Limited evidence suggests that early identification of infants with permanent hearing loss improves language skills. In a retrospective cohort study of 150 infants examining language outcomes, participants were grouped according to age at identification of hearing loss.6 All participants received comprehensive in-home language intervention services plus amplification devices.
Of the 85 children with normal cognitive ability, the mean receptive and expressive language quotients at 13 to 36 months were higher in the early-identified group vs the late-identified group (receptive language quotients, 79.6 vs 64.6, P<.001; expressive language quotients, 78.3 vs 63.1, P<.001). Total language quotient was also higher in the early group (language quotients, 79 vs 64; P<.001).
The conclusions were limited by multiple factors: retrospective study design, cohort selection drawn from different hospitals during different time periods, unblinded participant selection, and unblended outcome assessments. Other published studies have inconclusive outcome data. The Cochrane Collaboration published a systematic review in which no studies were found that fulfilled the inclusion criteria to evaluate the effectiveness of universal hearing screening.7
Recommendations from others
The Joint Committee on Infant Hearing recommended universal neonatal hearing screening during hospital birth admission in their Year 2000 Position Statement.8 For infants whose hearing is impaired on re-screening, the committee recommends audiology referral and medical evaluation to rule out associated conditions before age 3 months. They further recommend interventional services begin before age 6 months for infants with confirmed hearing loss.
The US Preventive Services Task Force does not recommend for or against universal hearing screening, citing insufficient outcomes data.9
1. Thompson DC, McPhillips H, Davis RL, Lieu TL, Homer CJ, Helfand M. Universal newborn hearing screening, summary of evidence. JAMA 2001;256:200-010.
2. Holt JA. Stanford Achievement Test—8th edition: reading comprehension subgroup results. Am Ann Deaf 1993;138:17-75.
3. Controlled trial of universal neonatal screening for early identification of permanent childhood hearing impairment. Wessex Universal Neonatal Hearing Screening Trial Group. Lancet 1998;352:195-964.
4. Norton SJ, Gorga MP, Widen SJ, et al. Identification of neonatal hearing impairment: evaluation of transient evoked otoacoustic emission, distortion product otoacoustic emission, and auditory brainstem response test performance. Ear Hear 2000;21:50-28.
5. Watkin PM, Baldwin M, McEnery G. Neonatal at risk screening and the identification of deafness. Arch Dis Child 1991;66(10 Spec No):113-135.
6. Yoshinaga-Itano C, Sedey AL, Coulter DK, Mehl AL. Language of early-and later-identified children with hearing loss. Pediatrics 1998;102:116-171.
7. Puig T, Municio A, Medà C. Universal neonatal hearing screening versus selective screening as part of the management of childhood deafness. Cochrane Database Syst Rev 2005;(2):CD003731.-
8. Joint Committee on Infant Hearing, American Academy of Audiology, American Academy of Pediatrics, American Speech-Language-Hearing Association, Directors of Speech and Hearing Programs in State Health and Welfare Agencies. Year 2000 position statement: Principles and guidelines for early hearing detection and intervention programs. Pediatrics 2000;106:79-17.
9. US Preventive Services Task Force. Newborn Hearing Screening: Recommendations and Rationale. October 2001. Agency for Healthcare Research and Quality, Rockville, Md. Available at: www.ahrq.gov/clinic/3rduspstf/newbornscreen/newhearrr.htm. Accessed on July 6, 2005.
1. Thompson DC, McPhillips H, Davis RL, Lieu TL, Homer CJ, Helfand M. Universal newborn hearing screening, summary of evidence. JAMA 2001;256:200-010.
2. Holt JA. Stanford Achievement Test—8th edition: reading comprehension subgroup results. Am Ann Deaf 1993;138:17-75.
3. Controlled trial of universal neonatal screening for early identification of permanent childhood hearing impairment. Wessex Universal Neonatal Hearing Screening Trial Group. Lancet 1998;352:195-964.
4. Norton SJ, Gorga MP, Widen SJ, et al. Identification of neonatal hearing impairment: evaluation of transient evoked otoacoustic emission, distortion product otoacoustic emission, and auditory brainstem response test performance. Ear Hear 2000;21:50-28.
5. Watkin PM, Baldwin M, McEnery G. Neonatal at risk screening and the identification of deafness. Arch Dis Child 1991;66(10 Spec No):113-135.
6. Yoshinaga-Itano C, Sedey AL, Coulter DK, Mehl AL. Language of early-and later-identified children with hearing loss. Pediatrics 1998;102:116-171.
7. Puig T, Municio A, Medà C. Universal neonatal hearing screening versus selective screening as part of the management of childhood deafness. Cochrane Database Syst Rev 2005;(2):CD003731.-
8. Joint Committee on Infant Hearing, American Academy of Audiology, American Academy of Pediatrics, American Speech-Language-Hearing Association, Directors of Speech and Hearing Programs in State Health and Welfare Agencies. Year 2000 position statement: Principles and guidelines for early hearing detection and intervention programs. Pediatrics 2000;106:79-17.
9. US Preventive Services Task Force. Newborn Hearing Screening: Recommendations and Rationale. October 2001. Agency for Healthcare Research and Quality, Rockville, Md. Available at: www.ahrq.gov/clinic/3rduspstf/newbornscreen/newhearrr.htm. Accessed on July 6, 2005.
Evidence-based answers from the Family Physicians Inquiries Network
What is the best way to distinguish type 1 and 2 diabetes?
No clinical characteristic or diagnostic test is available to readily distinguish type 1 from type 2 diabetes mellitus. Although C-peptide levels, autoantibodies, and adiponectin-to-leptin ratios show some utility, they do not yet have a standard diagnostic role; research on the pathophysiology of diabetes suggests that the classic type 1 and type 2 distinctions may not be appropriate for all patients1 (strength of recommendation: C, based on expert opinion).
Evidence summary
Onset of diabetes in childhood with ketoacidosis and insulin dependency has traditionally been sufficient to diagnose type 1 diabetes, while onset in older, obese patients with primary insulin resistance suggested type 2 diabetes. Unfortunately, features of type 1 and type 2 diabetes may be present in the same patient, making differentiation difficult. No diagnostic studies in the literature were identified that definitively demonstrate how to separate type 1 from type 2 diabetes.
A patient’s age may suggest, but does not reliably distinguish, diabetes types. A study of 569 new-onset type 1 and type 2 diabetic children and adolescents showed that older age was only weakly associated with type 2 diagnosis (odds ratio [OR]= 1.4 for each 1-year increment in age; 95% confidence interval [CI], 1.3–1.6).2 In fact, newly diagnosed 12-year-old children have an equal incidence of type 1 as type 2 diabetes. Likewise, adults with type 2 phenotype (no initial insulin requirement) can present with positive autoantibodies typically found in younger type 1 patients. Older patients who fit this profile have been classified as type 1.5 diabetes or latent autoimmune disease in adults (LADA).3
A history of diabetic ketoacidosis (DKA) also does not reliably distinguish between types 1 and 2. A retrospective chart review gathered data on adults over 18 years of age who were admitted for DKA in a urban US hospital. Many patients with DKA were subsequently diagnosed with type 2 diabetes. Rates of type 2 diabetes in patients with DKA varied by race: 47% of Hispanics, 44% of African Americans, and 17% of Caucasians had type 2 diabetes.4
The overlapping presence of autoantibodies in both types of diabetes limits their use (TABLE). Autoantibodies do predict an earlier need for insulin. One prevalence study of 101 type 2 adult patients found 20% were positive for glutamic acid decarboxylase autoantibody (GADAb), which was positively associated with insulin dependence at 4 years postdiagnosis (OR=5.8; 95% CI, 1.8–18.9).5 Eighty percent of patients with autoantibodies required insulin compared with 41% of patients without autoantibodies. Another study in young adults with type 2 or unclassified diabetes from Sweden found 93% of patients who were GADAb+ required insulin at 3 years, compared with 51% who were GADAb–(OR=18.8; 95% CI, 1.8–191).6
One motivation to study autoantibody testing is a potential benefit in preserving pancreatic function. Kobayashi proposed treating those with autoantibody-positive diabetes (presumed type 1 or type 1.5) with insulin immediately, while initiating oral medications in those who test negative (presumed type 2 diabetes). This approach lacks significant patient-oriented outcome data, but his small RCT of 55 patients was encouraging. With a 3-year follow-up rate of 89%, early insulin use in GADAb+ patients preserved C-peptide levels and possibly prolonged pancreatic beta cell survival.7 Insulin dependency, defined as needing insulin for survival, occurred in 47% of controls (who received oral sulfonylureas) and only 13% of patients receiving insulin (number needed to treat [NNT]= 4; P=.043).7 The theoretical benefit is that if beta cell exhaustion can be delayed, endogenous insulin production could be maintained to assist prevention of damaging postprandial glucose spikes.
Although daily variation in serum insulin levels limits its use, C-peptide levels show more promise. Random C-peptide levels were superior to fasting or glucagon stimulated levels in 1093 patients, who were followed for 3 years to confirm insulin requirements. Using a receiver operating characteristic (ROC) curve, the area under the curve for random C-peptide levels to distinguish diabetes types was 0.98 (95% CI, 0.97–0.99).8 For patients under the optimal cutoff of 0.5 nmol/L, the positive predictive value was 96% for diagnosing type 1 and the likelihood ratio was 22.5.
Finally, the ratio of adiponectin to leptin hormone may show diagnostic merit. Adipocytes secrete adiponectin which acts as an insulin sensitizer, antiatherogenic and anti-inflammatory agent. Obesity and type 2 phenotype correlate with lower levels of adiponectin, but are associated with higher levels of leptin hormone, another molecule secreted by adipocytes. A recent case-control study of children aged 6 to 21 years analyzed adiponectin and leptin hormone levels in patients with classical type 1 and 2 diabetes, as determined by 2 pediatric endocrinologists; interestingly, 29% of the type 1 patients were autoantibody negative.9 After plotting a ROC curve, they found the area under the curve was 0.97 (95% CI, 0.93–1.00). At an adiponectin-to-leptin ratio cutoff less than 0.7, they found the sensitivity to diagnose type 2 was 88% (95% CI, 64–99%), the specificity was 90% (95% CI, 77–97), and the likelihood ratio for a positive test was 8.8.9
TABLE 1
Antibody markers and diabetes type
| PREVALENCE OF ANY AUTOANTIBODY MARKER | PERCENT |
|---|---|
| Newly diagnosed type 1 (Caucasian) | 73–90 |
| Newly diagnosed type 1 (African or Asian) | 50 |
| Newly diagnosed type 2 (Caucasian) | 3–22 |
| Healthy individuals | 1–2 |
| Source: Wingfield et al 20041 and Maron et al 1996.3 | |
Recommendations from others
The National Academy of Clinical Biochemistry and the American Association of Clinical Endocrinologists recommend against routine testing of insulin, C-peptide, autoantibodies and genetic markers.1,10 Guidelines from the American Diabetes Association admit that many diabetic individuals do not easily fit into a distinct diagnostic category; however, they only provide criteria for the general diagnosis of diabetes, not specific criteria to distinguish type 1 from type 2.11
Focus on attaining optimal diabetes control goals as recommended by the ADA
Vincent Lo, MD
St. Elizabeth Family Medicine Residency Program/SUNY Upstate Medical University, Utica, New York
Not long ago, clinicians were advised to avoid the terms type 1 and type 2 diabetes, because they were not very helpful in clinical management of our patients. Instead, it was suggested that we use insulin-dependent or non-insulin-dependent. The rationale is that for patients with diabetes, there is an absolute insulin deficiency due to premature beta-cell failure in type 1 diabetes, as well as a relative insulin deficiency due to insulin resistance in type 2. In addition, studies also suggest that a majority of patients with type 2 diabetes would require some form of exogenous insulin therapy after a duration of 8 to 10 years of their disease. Therefore, distinguishing between types 1 and 2 is neither clinically helpful nor cost-effective, as suggested by current review of the literature. Instead, clinicians should focus on attaining optimal diabetic control goals as recommended by the practice guidelines of management of diabetes mellitus from the ADA. Furthermore, it was also recognized that one of the hurdles of failure to reach the target goal of HbA1C <7.0, among patients with type 2 diabetes is the delayed use of exogenous insulin therapy. Therefore, it is imperative for clinicians to discuss with each patient with a new diagnosis of diabetes, the natural progression of its disease process and its potential need and benefit of exogenous insulin therapy in the near future.
Acknowledgments
The opinions and assertions contained herein are the private views of the author and are not to be construed as official, or as reflecting the views of the US Air Force medical department or the US Air Force at large.
1. Sacks DB, Bruns DE, Goldstein DE, Maclaren NK, McDonald JM, Parrott M. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Clin Chem 2002;48:436-472.
2. Macaluso CJ, Bauer UE, Deeb LC, et al. Type 2 diabetes mellitus among Florida children and adolescents, 1994 through 1998. Public Health Rep 2002;117:373-379.
3. Pozzilli P, Di Mario U. Autoimmune diabetes not requiring insulin at diagnosis (latent autoimmune diabetes of the adult): definition, characterization, and potential prevention. Diabetes Care 2001;24:1460-1467.
4. Balasubramanyam A, Zern JW, Hyman DJ, Pavlik V. New profiles of diabetic ketoacidosis: type 1 vs type 2 diabetics and the effect of ethnicity. Arch Int Med 1999;159:2317-2322.
5. Grasso YZ, Reddy SK, Rosenfeld CR, et al. Autoantibodies to IA-2 and GAD65 in patients with type 2 diabetes mellitus of varied duration: prevalence and correlation with clinical features. Endocr Pract 2001;7:339-345.
6. Torn C, Landin-Olsson M, Ostman J, et al. Glutamic acid decarboxylase antibodies (GADA) is the most important factor for prediction of insulin therapy within 3 years in young adult diabetic patients not classified as Type 1 diabetes on clinical grounds. Diabetes Metab Res Rev 2000;16:442-447.
7. Kobayashi T, Maruyama T, Shimada A, et al. Insulin intervention to preserve beta cells in slowly progressive insulin-dependent (type 1) diabetes mellitus. Ann NY Acad Sci 2002;958:117-130.
8. Berger B, Stenstrom G, Sundkvist G. Random C-peptide in the classification of diabetes. Scand J Clin Lab Invest 2000;60:687-693.
9. Morales A, Wasserfall C, Brusko T, et al. Adiponectin and leptin concentrations may aid in discriminating disease forms in children and adolescents with type 1 and type 2 diabetes. Diabetes Care 2004;27:2010-2014.
10. The American Association of Clinical Endocrinologists. The American Association of Clinical Endocrinologists Medical Guidelines for the Management of Diabetes Mellitus: the AACE system of intensive diabetes self-management—2000 Update. Endocr Pract 2000;6:43-84.
11. Genuth S, Alberti KG, Bennett P, et al. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 2003;26:3160-3167.
No clinical characteristic or diagnostic test is available to readily distinguish type 1 from type 2 diabetes mellitus. Although C-peptide levels, autoantibodies, and adiponectin-to-leptin ratios show some utility, they do not yet have a standard diagnostic role; research on the pathophysiology of diabetes suggests that the classic type 1 and type 2 distinctions may not be appropriate for all patients1 (strength of recommendation: C, based on expert opinion).
Evidence summary
Onset of diabetes in childhood with ketoacidosis and insulin dependency has traditionally been sufficient to diagnose type 1 diabetes, while onset in older, obese patients with primary insulin resistance suggested type 2 diabetes. Unfortunately, features of type 1 and type 2 diabetes may be present in the same patient, making differentiation difficult. No diagnostic studies in the literature were identified that definitively demonstrate how to separate type 1 from type 2 diabetes.
A patient’s age may suggest, but does not reliably distinguish, diabetes types. A study of 569 new-onset type 1 and type 2 diabetic children and adolescents showed that older age was only weakly associated with type 2 diagnosis (odds ratio [OR]= 1.4 for each 1-year increment in age; 95% confidence interval [CI], 1.3–1.6).2 In fact, newly diagnosed 12-year-old children have an equal incidence of type 1 as type 2 diabetes. Likewise, adults with type 2 phenotype (no initial insulin requirement) can present with positive autoantibodies typically found in younger type 1 patients. Older patients who fit this profile have been classified as type 1.5 diabetes or latent autoimmune disease in adults (LADA).3
A history of diabetic ketoacidosis (DKA) also does not reliably distinguish between types 1 and 2. A retrospective chart review gathered data on adults over 18 years of age who were admitted for DKA in a urban US hospital. Many patients with DKA were subsequently diagnosed with type 2 diabetes. Rates of type 2 diabetes in patients with DKA varied by race: 47% of Hispanics, 44% of African Americans, and 17% of Caucasians had type 2 diabetes.4
The overlapping presence of autoantibodies in both types of diabetes limits their use (TABLE). Autoantibodies do predict an earlier need for insulin. One prevalence study of 101 type 2 adult patients found 20% were positive for glutamic acid decarboxylase autoantibody (GADAb), which was positively associated with insulin dependence at 4 years postdiagnosis (OR=5.8; 95% CI, 1.8–18.9).5 Eighty percent of patients with autoantibodies required insulin compared with 41% of patients without autoantibodies. Another study in young adults with type 2 or unclassified diabetes from Sweden found 93% of patients who were GADAb+ required insulin at 3 years, compared with 51% who were GADAb–(OR=18.8; 95% CI, 1.8–191).6
One motivation to study autoantibody testing is a potential benefit in preserving pancreatic function. Kobayashi proposed treating those with autoantibody-positive diabetes (presumed type 1 or type 1.5) with insulin immediately, while initiating oral medications in those who test negative (presumed type 2 diabetes). This approach lacks significant patient-oriented outcome data, but his small RCT of 55 patients was encouraging. With a 3-year follow-up rate of 89%, early insulin use in GADAb+ patients preserved C-peptide levels and possibly prolonged pancreatic beta cell survival.7 Insulin dependency, defined as needing insulin for survival, occurred in 47% of controls (who received oral sulfonylureas) and only 13% of patients receiving insulin (number needed to treat [NNT]= 4; P=.043).7 The theoretical benefit is that if beta cell exhaustion can be delayed, endogenous insulin production could be maintained to assist prevention of damaging postprandial glucose spikes.
Although daily variation in serum insulin levels limits its use, C-peptide levels show more promise. Random C-peptide levels were superior to fasting or glucagon stimulated levels in 1093 patients, who were followed for 3 years to confirm insulin requirements. Using a receiver operating characteristic (ROC) curve, the area under the curve for random C-peptide levels to distinguish diabetes types was 0.98 (95% CI, 0.97–0.99).8 For patients under the optimal cutoff of 0.5 nmol/L, the positive predictive value was 96% for diagnosing type 1 and the likelihood ratio was 22.5.
Finally, the ratio of adiponectin to leptin hormone may show diagnostic merit. Adipocytes secrete adiponectin which acts as an insulin sensitizer, antiatherogenic and anti-inflammatory agent. Obesity and type 2 phenotype correlate with lower levels of adiponectin, but are associated with higher levels of leptin hormone, another molecule secreted by adipocytes. A recent case-control study of children aged 6 to 21 years analyzed adiponectin and leptin hormone levels in patients with classical type 1 and 2 diabetes, as determined by 2 pediatric endocrinologists; interestingly, 29% of the type 1 patients were autoantibody negative.9 After plotting a ROC curve, they found the area under the curve was 0.97 (95% CI, 0.93–1.00). At an adiponectin-to-leptin ratio cutoff less than 0.7, they found the sensitivity to diagnose type 2 was 88% (95% CI, 64–99%), the specificity was 90% (95% CI, 77–97), and the likelihood ratio for a positive test was 8.8.9
TABLE 1
Antibody markers and diabetes type
| PREVALENCE OF ANY AUTOANTIBODY MARKER | PERCENT |
|---|---|
| Newly diagnosed type 1 (Caucasian) | 73–90 |
| Newly diagnosed type 1 (African or Asian) | 50 |
| Newly diagnosed type 2 (Caucasian) | 3–22 |
| Healthy individuals | 1–2 |
| Source: Wingfield et al 20041 and Maron et al 1996.3 | |
Recommendations from others
The National Academy of Clinical Biochemistry and the American Association of Clinical Endocrinologists recommend against routine testing of insulin, C-peptide, autoantibodies and genetic markers.1,10 Guidelines from the American Diabetes Association admit that many diabetic individuals do not easily fit into a distinct diagnostic category; however, they only provide criteria for the general diagnosis of diabetes, not specific criteria to distinguish type 1 from type 2.11
Focus on attaining optimal diabetes control goals as recommended by the ADA
Vincent Lo, MD
St. Elizabeth Family Medicine Residency Program/SUNY Upstate Medical University, Utica, New York
Not long ago, clinicians were advised to avoid the terms type 1 and type 2 diabetes, because they were not very helpful in clinical management of our patients. Instead, it was suggested that we use insulin-dependent or non-insulin-dependent. The rationale is that for patients with diabetes, there is an absolute insulin deficiency due to premature beta-cell failure in type 1 diabetes, as well as a relative insulin deficiency due to insulin resistance in type 2. In addition, studies also suggest that a majority of patients with type 2 diabetes would require some form of exogenous insulin therapy after a duration of 8 to 10 years of their disease. Therefore, distinguishing between types 1 and 2 is neither clinically helpful nor cost-effective, as suggested by current review of the literature. Instead, clinicians should focus on attaining optimal diabetic control goals as recommended by the practice guidelines of management of diabetes mellitus from the ADA. Furthermore, it was also recognized that one of the hurdles of failure to reach the target goal of HbA1C <7.0, among patients with type 2 diabetes is the delayed use of exogenous insulin therapy. Therefore, it is imperative for clinicians to discuss with each patient with a new diagnosis of diabetes, the natural progression of its disease process and its potential need and benefit of exogenous insulin therapy in the near future.
Acknowledgments
The opinions and assertions contained herein are the private views of the author and are not to be construed as official, or as reflecting the views of the US Air Force medical department or the US Air Force at large.
No clinical characteristic or diagnostic test is available to readily distinguish type 1 from type 2 diabetes mellitus. Although C-peptide levels, autoantibodies, and adiponectin-to-leptin ratios show some utility, they do not yet have a standard diagnostic role; research on the pathophysiology of diabetes suggests that the classic type 1 and type 2 distinctions may not be appropriate for all patients1 (strength of recommendation: C, based on expert opinion).
Evidence summary
Onset of diabetes in childhood with ketoacidosis and insulin dependency has traditionally been sufficient to diagnose type 1 diabetes, while onset in older, obese patients with primary insulin resistance suggested type 2 diabetes. Unfortunately, features of type 1 and type 2 diabetes may be present in the same patient, making differentiation difficult. No diagnostic studies in the literature were identified that definitively demonstrate how to separate type 1 from type 2 diabetes.
A patient’s age may suggest, but does not reliably distinguish, diabetes types. A study of 569 new-onset type 1 and type 2 diabetic children and adolescents showed that older age was only weakly associated with type 2 diagnosis (odds ratio [OR]= 1.4 for each 1-year increment in age; 95% confidence interval [CI], 1.3–1.6).2 In fact, newly diagnosed 12-year-old children have an equal incidence of type 1 as type 2 diabetes. Likewise, adults with type 2 phenotype (no initial insulin requirement) can present with positive autoantibodies typically found in younger type 1 patients. Older patients who fit this profile have been classified as type 1.5 diabetes or latent autoimmune disease in adults (LADA).3
A history of diabetic ketoacidosis (DKA) also does not reliably distinguish between types 1 and 2. A retrospective chart review gathered data on adults over 18 years of age who were admitted for DKA in a urban US hospital. Many patients with DKA were subsequently diagnosed with type 2 diabetes. Rates of type 2 diabetes in patients with DKA varied by race: 47% of Hispanics, 44% of African Americans, and 17% of Caucasians had type 2 diabetes.4
The overlapping presence of autoantibodies in both types of diabetes limits their use (TABLE). Autoantibodies do predict an earlier need for insulin. One prevalence study of 101 type 2 adult patients found 20% were positive for glutamic acid decarboxylase autoantibody (GADAb), which was positively associated with insulin dependence at 4 years postdiagnosis (OR=5.8; 95% CI, 1.8–18.9).5 Eighty percent of patients with autoantibodies required insulin compared with 41% of patients without autoantibodies. Another study in young adults with type 2 or unclassified diabetes from Sweden found 93% of patients who were GADAb+ required insulin at 3 years, compared with 51% who were GADAb–(OR=18.8; 95% CI, 1.8–191).6
One motivation to study autoantibody testing is a potential benefit in preserving pancreatic function. Kobayashi proposed treating those with autoantibody-positive diabetes (presumed type 1 or type 1.5) with insulin immediately, while initiating oral medications in those who test negative (presumed type 2 diabetes). This approach lacks significant patient-oriented outcome data, but his small RCT of 55 patients was encouraging. With a 3-year follow-up rate of 89%, early insulin use in GADAb+ patients preserved C-peptide levels and possibly prolonged pancreatic beta cell survival.7 Insulin dependency, defined as needing insulin for survival, occurred in 47% of controls (who received oral sulfonylureas) and only 13% of patients receiving insulin (number needed to treat [NNT]= 4; P=.043).7 The theoretical benefit is that if beta cell exhaustion can be delayed, endogenous insulin production could be maintained to assist prevention of damaging postprandial glucose spikes.
Although daily variation in serum insulin levels limits its use, C-peptide levels show more promise. Random C-peptide levels were superior to fasting or glucagon stimulated levels in 1093 patients, who were followed for 3 years to confirm insulin requirements. Using a receiver operating characteristic (ROC) curve, the area under the curve for random C-peptide levels to distinguish diabetes types was 0.98 (95% CI, 0.97–0.99).8 For patients under the optimal cutoff of 0.5 nmol/L, the positive predictive value was 96% for diagnosing type 1 and the likelihood ratio was 22.5.
Finally, the ratio of adiponectin to leptin hormone may show diagnostic merit. Adipocytes secrete adiponectin which acts as an insulin sensitizer, antiatherogenic and anti-inflammatory agent. Obesity and type 2 phenotype correlate with lower levels of adiponectin, but are associated with higher levels of leptin hormone, another molecule secreted by adipocytes. A recent case-control study of children aged 6 to 21 years analyzed adiponectin and leptin hormone levels in patients with classical type 1 and 2 diabetes, as determined by 2 pediatric endocrinologists; interestingly, 29% of the type 1 patients were autoantibody negative.9 After plotting a ROC curve, they found the area under the curve was 0.97 (95% CI, 0.93–1.00). At an adiponectin-to-leptin ratio cutoff less than 0.7, they found the sensitivity to diagnose type 2 was 88% (95% CI, 64–99%), the specificity was 90% (95% CI, 77–97), and the likelihood ratio for a positive test was 8.8.9
TABLE 1
Antibody markers and diabetes type
| PREVALENCE OF ANY AUTOANTIBODY MARKER | PERCENT |
|---|---|
| Newly diagnosed type 1 (Caucasian) | 73–90 |
| Newly diagnosed type 1 (African or Asian) | 50 |
| Newly diagnosed type 2 (Caucasian) | 3–22 |
| Healthy individuals | 1–2 |
| Source: Wingfield et al 20041 and Maron et al 1996.3 | |
Recommendations from others
The National Academy of Clinical Biochemistry and the American Association of Clinical Endocrinologists recommend against routine testing of insulin, C-peptide, autoantibodies and genetic markers.1,10 Guidelines from the American Diabetes Association admit that many diabetic individuals do not easily fit into a distinct diagnostic category; however, they only provide criteria for the general diagnosis of diabetes, not specific criteria to distinguish type 1 from type 2.11
Focus on attaining optimal diabetes control goals as recommended by the ADA
Vincent Lo, MD
St. Elizabeth Family Medicine Residency Program/SUNY Upstate Medical University, Utica, New York
Not long ago, clinicians were advised to avoid the terms type 1 and type 2 diabetes, because they were not very helpful in clinical management of our patients. Instead, it was suggested that we use insulin-dependent or non-insulin-dependent. The rationale is that for patients with diabetes, there is an absolute insulin deficiency due to premature beta-cell failure in type 1 diabetes, as well as a relative insulin deficiency due to insulin resistance in type 2. In addition, studies also suggest that a majority of patients with type 2 diabetes would require some form of exogenous insulin therapy after a duration of 8 to 10 years of their disease. Therefore, distinguishing between types 1 and 2 is neither clinically helpful nor cost-effective, as suggested by current review of the literature. Instead, clinicians should focus on attaining optimal diabetic control goals as recommended by the practice guidelines of management of diabetes mellitus from the ADA. Furthermore, it was also recognized that one of the hurdles of failure to reach the target goal of HbA1C <7.0, among patients with type 2 diabetes is the delayed use of exogenous insulin therapy. Therefore, it is imperative for clinicians to discuss with each patient with a new diagnosis of diabetes, the natural progression of its disease process and its potential need and benefit of exogenous insulin therapy in the near future.
Acknowledgments
The opinions and assertions contained herein are the private views of the author and are not to be construed as official, or as reflecting the views of the US Air Force medical department or the US Air Force at large.
1. Sacks DB, Bruns DE, Goldstein DE, Maclaren NK, McDonald JM, Parrott M. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Clin Chem 2002;48:436-472.
2. Macaluso CJ, Bauer UE, Deeb LC, et al. Type 2 diabetes mellitus among Florida children and adolescents, 1994 through 1998. Public Health Rep 2002;117:373-379.
3. Pozzilli P, Di Mario U. Autoimmune diabetes not requiring insulin at diagnosis (latent autoimmune diabetes of the adult): definition, characterization, and potential prevention. Diabetes Care 2001;24:1460-1467.
4. Balasubramanyam A, Zern JW, Hyman DJ, Pavlik V. New profiles of diabetic ketoacidosis: type 1 vs type 2 diabetics and the effect of ethnicity. Arch Int Med 1999;159:2317-2322.
5. Grasso YZ, Reddy SK, Rosenfeld CR, et al. Autoantibodies to IA-2 and GAD65 in patients with type 2 diabetes mellitus of varied duration: prevalence and correlation with clinical features. Endocr Pract 2001;7:339-345.
6. Torn C, Landin-Olsson M, Ostman J, et al. Glutamic acid decarboxylase antibodies (GADA) is the most important factor for prediction of insulin therapy within 3 years in young adult diabetic patients not classified as Type 1 diabetes on clinical grounds. Diabetes Metab Res Rev 2000;16:442-447.
7. Kobayashi T, Maruyama T, Shimada A, et al. Insulin intervention to preserve beta cells in slowly progressive insulin-dependent (type 1) diabetes mellitus. Ann NY Acad Sci 2002;958:117-130.
8. Berger B, Stenstrom G, Sundkvist G. Random C-peptide in the classification of diabetes. Scand J Clin Lab Invest 2000;60:687-693.
9. Morales A, Wasserfall C, Brusko T, et al. Adiponectin and leptin concentrations may aid in discriminating disease forms in children and adolescents with type 1 and type 2 diabetes. Diabetes Care 2004;27:2010-2014.
10. The American Association of Clinical Endocrinologists. The American Association of Clinical Endocrinologists Medical Guidelines for the Management of Diabetes Mellitus: the AACE system of intensive diabetes self-management—2000 Update. Endocr Pract 2000;6:43-84.
11. Genuth S, Alberti KG, Bennett P, et al. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 2003;26:3160-3167.
1. Sacks DB, Bruns DE, Goldstein DE, Maclaren NK, McDonald JM, Parrott M. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Clin Chem 2002;48:436-472.
2. Macaluso CJ, Bauer UE, Deeb LC, et al. Type 2 diabetes mellitus among Florida children and adolescents, 1994 through 1998. Public Health Rep 2002;117:373-379.
3. Pozzilli P, Di Mario U. Autoimmune diabetes not requiring insulin at diagnosis (latent autoimmune diabetes of the adult): definition, characterization, and potential prevention. Diabetes Care 2001;24:1460-1467.
4. Balasubramanyam A, Zern JW, Hyman DJ, Pavlik V. New profiles of diabetic ketoacidosis: type 1 vs type 2 diabetics and the effect of ethnicity. Arch Int Med 1999;159:2317-2322.
5. Grasso YZ, Reddy SK, Rosenfeld CR, et al. Autoantibodies to IA-2 and GAD65 in patients with type 2 diabetes mellitus of varied duration: prevalence and correlation with clinical features. Endocr Pract 2001;7:339-345.
6. Torn C, Landin-Olsson M, Ostman J, et al. Glutamic acid decarboxylase antibodies (GADA) is the most important factor for prediction of insulin therapy within 3 years in young adult diabetic patients not classified as Type 1 diabetes on clinical grounds. Diabetes Metab Res Rev 2000;16:442-447.
7. Kobayashi T, Maruyama T, Shimada A, et al. Insulin intervention to preserve beta cells in slowly progressive insulin-dependent (type 1) diabetes mellitus. Ann NY Acad Sci 2002;958:117-130.
8. Berger B, Stenstrom G, Sundkvist G. Random C-peptide in the classification of diabetes. Scand J Clin Lab Invest 2000;60:687-693.
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Evidence-based answers from the Family Physicians Inquiries Network